On the Extended Generalized Inverted Kumaraswamy Distribution

In this work, we provide a new generated class of models, namely, the extended generalized inverted Kumaraswamy generated (EGIKw-G) family of distributions. Several structural properties (survival function $\left(\mathrm{sf}\right)$ , hazard rate function $\left(\mathrm{hrf}\right)$ , reverse hazard rate function $\left(\mathrm{rhrf}\right)$ , quantile function $\left(\mathrm{qf}\right)$ and median, $s^{\text{th}}$ raw moment, generating function, mean deviation $\left(\mathrm{md}\right)$ , etc.) are provided. The estimates for parameters of new G class are derived via maximum likelihood estimation $\left(\text{MLE}\right)$ method. The special models of the proposed class are discussed, and particular attention is given to one special model, the extended generalized inverted Kumaraswamy Burr XII (EGIKw-Burr XII) model. Estimators are evaluated via a Monte Carlo simulation $\left(\text{MCS}\right)$ . The superiority of EGIKw-Burr XII model is proved using a lifetime data applications.

Multi-component stress-strength model for Weibull distribution in progressively censored samples

One of the important issues is risk assessment and calculation in complex and multi-component systems. In this paper, the estimation of multi-component stress-strength reliability for the Weibull distribution under the progressive Type-II censored samples is studied. We assume that both stress and strength are two independent Weibull distributions with different parameters. First, assuming the same shape parameter, the maximum likelihood estimation (MLE), different approximations of Bayes estimators (Lindley’s approximation and Markov chain Monte Carlo method) and different confidence intervals (asymptotic and highest posterior density) are obtained. In the case when the shape parameter is known, the MLE, uniformly minimum variance unbiased estimator (UMVUE), exact Bayes estimator and different confidence intervals (asymptotic and highest posterior density) are considered. Finally, in the general case, the statistical inferences on multi-component stress-strength reliability are derived. To compare the performances of different methods, Monte Carlo simulations are performed. Moreover, one data set for illustrative purposes is analyzed.

Predicting ecological footprint based on global macro indicators in G-20 countries using machine learning approaches

Paying attention to human activities in terms of land grazing infrastructure, crops, forest products, and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. For the present study, global database data were used. The ability of the penalized regression (RR) approaches (including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 countries over the past two decades (1999-2018) was illustrated and compared. For this purpose, 10-fold cross-validation was used to evaluate the predictive performance and determine the penalty parameter for PR models. According to the results, the predictive performance compared to linear regression improved somewhat using the penalized methods. Using the elastic net model, more global macro indices were selected than Lasso. Although Lasso selected only a few indicators, it had better predictive performance among PR ns models. In addition to relative improvement in the predictive performance of PR methods, their interest in selecting a subset of indicators by shrinking coefficients and creating a parsimonious model was evident and significant. As a result, PR methods would be preferred, using variable selection and interpretive considerations to predictive performance alone. On the other hand, ANN models with higher determination coefficients (R²) and lower RMSE values performed significantly better than PR and OLS and showed that they were more accurate in predicting EF. Therefore, ANN could provide considerable and appropriate predictions for EF indicators in the G-20 countries.

Prediction models of treatment response in lupus nephritis

In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three-quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.

Manufacturing, Exports, and Sustainable Growth: Evidence from Developing Countries

Using data for 130 developing countries over a 24 year period from 1996 to 2019, this study investigates the role of manufacturing development in sustainable growth and how the contribution of the manufacturing sector to growth is affected by exports and the underlying export-oriented policies. By employing a vintage difference GMM estimation developed by Arellano and Bond (1991), we find that the manufacturing sector positively contributes to economic growth in developing countries, whereas exports (and thus, their related growth policies) lead to deindustrialization and are thus harmful to growth. In addition, we find that this export-led deindustrialization and the resulting negative growth effect might differ depending on a country’s stage of development measured in terms of the per capita income level. In particular, the growth of countries with lower income levels is more severely negatively impacted than in the case of the richer countries, which is consistent with the findings in the literature. Finally, our main results are robust under two alternative regression checks in which we take into account the potential endogeneity problem and additionally control for the share of imports in GDP in the model.

A New Family of Continuous Distributions: Properties and Estimation

We introduce a new flexible modified alpha power (MAP) family of distributions by adding two parameters to a baseline model. Some of its mathematical properties are addressed. We show empirically that the new family is a good competitor to the Beta-F and Kumaraswamy-F classes, which have been widely applied in several areas. A new extension of the exponential distribution, called the modified alpha power exponential (MAPE) distribution, is defined by applying the MAP transformation to the exponential distribution. Some properties and maximum likelihood estimates are provided for this distribution. We analyze three real datasets to compare the flexibility of the MAPE distribution to the exponential, Weibull, Marshall–Olkin exponential and alpha power exponential distributions.

Reliability Estimation in Multicomponent Stress-Strength Based on Inverse Weibull Distribution

The present study focuses on the multi-component stress-strength (MCSS) model based on inverse Weibull distribution (IWD). Both stress and strength are assumed to follow IWD with a common shape parameter. In such a system, reliability is obtained by the maximum likelihood (ML) method. The results are extracted using Monte Carlo simulation for comparing the performance of the reliability component Rs,k using different sample sizes and different combinations of the parameters (s,k). The procedure is further illustrated through a real data set to show how the proposed technique may be employed to study the strength and stress of multicomponent model.

A generalized class of estimators for sensitive variable in the presence of measurement error and non-response

In this paper, a general class of estimators is proposed for estimating the finite population mean for sensitive variable, in the presence of measurement error and non-response in simple random sampling. Expressions for bias and mean square error up to first order of approximation, are derived. Impact of measurement errors is examined using real data sets, including the survey conducted at Quaid-i-Azam University, Islamabad. Simulated data sets are also used to observe the performance of the proposed estimators in comparison to some other estimators. We obtain the empirical bias and MSE values for the proposed and the competing estimators.

A New Goodness of Fit Measure Based on Income Inequality Curves

This paper uses inequality-measurement techniques to assess goodness of fit in income distribution models. It exposes the shortcomings of the use of conventional goodness of fit criteria in face of the big income data and proposes a new set of metrics, based on income inequality curves. In this note, we mentioned that the distance between theoretical and empirical inequality curves can be considered as a goodness of fit criterion. We demonstrate certain advantages of this measure over the other general goodness of fit criteria. Unlike other goodness of fit measures, this criterion is bounded. It is 0 in minimum difference and 1 in maximum distance. Furthermore, there is a consistency between this new goodness of fit measure and the other conventional criteria. A simulation study based on fitted distribution to real income data is performed in order to investigate some statistical properties of the new goodness of fit measure. An empirical study and comparisons are also provided.

Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis

Objectives

To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG).

Methods

Patients with histologically confirmed TET in the years 2000–2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC<0.75) were excluded. Repeated nested cross-validation was used for feature selection (Boruta algorithm), model training, and evaluation (out-of-fold predictions). Shapley additive explanation (SHAP) values were calculated to assess feature importance.

Results

105 patients undergoing surgery for TET were identified. After applying exclusion criteria, 62 patients (28 female; mean age, 57±14 years; range, 22–82 years) with 34 low-risk TET (LRT; WHO types A/AB/B1), 28 high-risk TET (HRT; WHO B2/B3/C) in early stage (49, TNM stage I-II) or advanced stage (13, TNM III-IV) were included. 14(23%) of the patients had MG. 334(25%) features were excluded after intra-/inter-reader analysis. Discriminatory performance of the random forest classifiers was good for histology(AUC, 87.6%; 95% confidence interval, 76.3–94.3) and TNM stage(AUC, 83.8%; 95%CI, 66.9–93.4) but poor for the prediction of MG (AUC, 63.9%; 95%CI, 44.8–79.5).

Conclusions

CT-derived radiomic features may be a useful imaging biomarker for TET histology and TNM stage.

Bayesian and Classical Inference under Type-II Censored Samples of the Extended Inverse Gompertz Distribution with Engineering Applications

In this article, we introduce a new three-parameter distribution called the extended inverse-Gompertz (EIGo) distribution. The implementation of three parameters provides a good reconstruction for some applications. The EIGo distribution can be seen as an extension of the inverted exponential, inverse Gompertz, and generalized inverted exponential distributions. Its failure rate function has an upside-down bathtub shape. Various statistical and reliability properties of the EIGo distribution are discussed. The model parameters are estimated by the maximum-likelihood and Bayesian methods under Type-II censored samples, where the parameters are explained using gamma priors. The performance of the proposed approaches is examined using simulation results. Finally, two real-life engineering data sets are analyzed to illustrate the applicability of the EIGo distribution, showing that it provides better fits than competing inverted models such as inverse-Gompertz, inverse-Weibull, inverse-gamma, generalized inverse-Weibull, exponentiated inverted-Weibull, generalized inverted half-logistic, inverted-Kumaraswamy, inverted Nadarajah-Haghighi, and alpha-power inverse-Weibull distributions.

Analytic Approaches to Assess the Impact of Local Spending on Sexually Transmitted Diseases

OBJECTIVE

To compare the estimated associations between annual STD (sexually transmitted diseases) expenditures per capita and STD rates among Florida and Washington local health departments (LHDs) from 2001-2017, using two approaches--a longitudinal regression model with lagged STD spending, and a regression model with the Arellano-Bond panel estimator.

DATA SOURCES

Secondary data for LHDs were obtained from Florida and Washington state government offices and combined with county sociodemographic and health system data from the federal government.

STUDY DESIGN

We examined LHDs in Florida and Washington using a longitudinal panel study design to estimate ecological relationships between annual STD expenditures per capita and annual STD incidence rates from 2001 to 2017 with LHDs as the unit of analysis. We compared two regression models: generalized estimating equations (GEE) and the Arellano-Bond panel estimator (an instrumental variable approach).

DATA COLLECTION

The secondary data were combined to build a longitudinal panel database for LHDs in Florida and Washington from 2001 to 2017.

PRINCIPAL FINDINGS

In the GEE model with both states, greater STD spending in a prior year was associated unexpectedly with greater STD incidence rates in succeeding years. The Arellano-Bond models for both states had the expected inverse associations but were not significant. In the Arellano-Bond models for Florida, a $1 increase in STD spending in previous years was followed by decreases in STD incidence rates ranging between 29 and 59 points in succeeding years (0.09 ≥ p ≥ 0.04).

CONCLUSIONS

In longitudinal panel data for LHDs in two states, the Arellano-Bond estimator, or other instrumental variable approach, is preferred over conventional regression models to obtain unbiased estimates of the relationship between annual STD spending rates and annual STD rates. Future studies will require accurate, standardized, and detailed longitudinal data and rigorous analytic approaches, such as those illustrated in our study. This article is protected by copyright. All rights reserved.

The modified beta transmuted family of distributions with applications using the exponential distribution

In this work, a new family of distributions, which extends the Beta transmuted family, was obtained, called the Modified Beta Transmuted Family of distribution. This derived family has the Beta Family of Distribution and the Transmuted family of distribution as subfamilies. The Modified beta transmuted frechet, modified beta transmuted exponential, modified beta transmuted gompertz and modified beta transmuted lindley were obtained as special cases. The analytical expressions were studied for some statistical properties of the derived family of distribution which includes the moments, moments generating function and order statistics. The estimates of the parameters of the family were obtained using the maximum likelihood estimation method. Using the exponential distribution as a baseline for the family distribution, the resulting distribution (modified beta transmuted exponential distribution) was studied and its properties. The modified beta transmuted exponential distribution was applied to a real life time data to assess its flexibility in which the results shows a better fit when compared to some competitive models.

Discrete Pseudo Lindley Distribution: Properties, Estimation and Application on INAR(1) Process

In this paper, we introduce a discrete version of the Pseudo Lindley (PsL) distribution, namely, the discrete Pseudo Lindley (DPsL) distribution, and systematically study its mathematical properties. Explicit forms gathered for the properties such as the probability generating function, moments, skewness, kurtosis and stress–strength reliability made the distribution favourable. Two different methods are considered for the estimation of unknown parameters and, hence, compared with a broad simulation study. The practicality of the proposed distribution is illustrated in the first-order integer-valued autoregressive process. Its empirical importance is proved through three real datasets.

Slash Truncation Positive Normal Distribution and Its Estimation Based on the EM Algorithm

In this paper, we present an extension of the truncated positive normal (TPN) distribution to model positive data with a high kurtosis. The new model is defined as the quotient between two random variables: the TPN distribution (numerator) and the power of a standard uniform distribution (denominator). The resulting model has greater kurtosis than the TPN distribution. We studied some properties of the distribution, such as moments, asymmetry, and kurtosis. Parameter estimation is based on the moments method, and maximum likelihood estimation uses the expectation-maximization algorithm. We performed some simulation studies to assess the recovery parameters and illustrate the model with a real data application related to body weight. The computational implementation of this work was included in the tpn package of the R software.

Inventory pooling decisions under demand scenarios in times of COVID-19

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. The aim of this research is to propose a novel inventory pooling model to help determine order sizes and safety inventories in local hospital warehouses. The current study attempts to portray the availability of pharmaceutical items in public hospitals facing COVID-19 challenges. Different from previous studies, this research builds upon the consecrated theory of inventory pooling, extending it to pandemic circumstances where the intractability of kurtosis and skewness in inventory models are critical issues for making sure that medicines have high availability at a low cost. These effects on the total cost of inventory are explored and compared to a supply system with no consolidation. A continuous-review model is assumed with allocation rules for centralization and regular transshipment given different skewness and kurtosis structures for the demand, describing them by the copula method. This method models a multivariate demand considering that the marginal distributions of the demand can be specified by the Generalized Additive Model for Location, Scale and Shape, which offers advantages to model demands considering virtually any marginal statistical distribution. Numerical simulations and an illustrative example show that distributions of demands with more negative skewness and high kurtosis favor to a greater extent obtaining lower total costs with regular supply transshipment systems. Our study points out important considerations for supply chain decision makers when having demands with skewness and kurtosis patterns.

Multicomponent stress-strength reliability estimation based on unit generalized Rayleigh distribution

Purpose

The purpose of this article is to derive inference for multicomponent reliability where stress-strength variables follow unit generalized Rayleigh (GR) distributions with common scale parameter.

Design/methodology/approach

The authors derive inference for the unknown parametric function using classical and Bayesian approaches. In sequel, (weighted) least square (LS) and maximum product of spacing methods are used to estimate the reliability. Bootstrapping is also considered for this purpose. Bayesian inference is derived under gamma prior distributions. In consequence credible intervals are constructed. For the known common scale, unbiased estimator is obtained and is compared with the corresponding exact Bayes estimate.

Findings

Different point and interval estimators of the reliability are examined using Monte Carlo simulations for different sample sizes. In summary, the authors observe that Bayes estimators obtained using gamma prior distributions perform well compared to the other studied estimators. The average length (AL) of highest posterior density (HPD) interval remains shorter than other proposed intervals. Further coverage probabilities of all the intervals are reasonably satisfactory. A data analysis is also presented in support of studied estimation methods. It is noted that proposed methods work good for the considered estimation problem.

Originality/value

In the literature various probability distributions which are often analyzed in life test studies are mostly unbounded in nature, that is, their support of positive probabilities lie in infinite interval. This class of distributions includes generalized exponential, Burr family, gamma, lognormal and Weibull models, among others. In many situations the authors need to analyze data which lie in bounded interval like average height of individual, survival time from a disease, income per-capita etc. Thus use of probability models with support on finite intervals becomes inevitable. The authors have investigated stress-strength reliability based on unit GR distribution. Useful comments are obtained based on the numerical study.

Exponentiated Generalized Inverted Gompertz Distribution: Properties and Estimation Methods with Applications to Symmetric and Asymmetric Data

In this article, a new four-parameter lifetime model called the exponentiated generalized inverted Gompertz distribution is studied and proposed. The newly proposed distribution is able to model the lifetimes with upside-down bathtub-shaped hazard rates and is suitable for describing the negative and positive skewness. A detailed description of some various properties of this model, including the reliability function, hazard rate function, quantile function, and median, mode, moments, moment generating function, entropies, kurtosis, and skewness, mean waiting lifetime, and others are presented. The parameters of the studied model are appreciated using four various estimation methods, the maximum likelihood, least squares, weighted least squares, and Cramér-von Mises methods. A simulation study is carried out to examine the performance of the new model estimators based on the four estimation methods using the mean squared errors (MSEs) and the bias estimates. The flexibility of the proposed model is clarified by studying four different engineering applications to symmetric and asymmetric data, and it is found that this model is more flexible and works quite well for modeling these data.

A new approach using the genetic algorithm for parameter estimation in multiple linear regression with long-tailed symmetric distributed error terms: An application to the Covid-19 data

Maximum likelihood (ML) estimators of the model parameters in multiple linear regression are obtained using genetic algorithm (GA) when the distribution of the error terms is long-tailed symmetric. We compare the efficiencies of the ML estimators obtained using GA with the corresponding ML estimators obtained using other iterative techniques via an extensive Monte Carlo simulation study. Robust confidence intervals based on modified ML estimators are used as the search space in GA. Our simulation study shows that GA outperforms traditional algorithms in most cases. Therefore, we suggest using GA to obtain the ML estimates of the multiple linear regression model parameters when the distribution of the error terms is LTS. Finally, real data of the Covid-19 pandemic, a global health crisis in early 2020, is presented for illustrative purposes.

Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD

Temporal processing (TP) is associated with functions such as perception, verbal skills, temporal perspective, and future planning, and is intercorrelated with working memory, attention, and inhibitory control, which are highly impaired in individuals with attention deficit hyperactivity disorder (ADHD). Here we evaluate TP measures as potential endophenotypes in Caribbean families ascertained from probands affected by ADHD. A total of 232 individuals were recruited and clinically evaluated using an extensive battery of neuropsychological tasks and reaction time (RT)-based task paradigms. Further, the heritability (genetic variance underpinning phenotype) was estimated as a measure of the genetics apportionment. A predictive framework for ADHD diagnosis was derived using these tasks. We found that individuals with ADHD differed from controls in neuropsychological tasks assessing mental control, visual-verbal memory, verbal fluency, verbal, and semantic fluency. In addition, TP measures such as RT, errors, and variability were also affected in individuals with ADHD. Moreover, we determined that only omission and commission errors had significant heritability. In conclusion, we have disentangled omission and commission errors as possible TP endophenotypes in ADHD, which can be suitable to assess the neurobiological and genetic basis of ADHD. A predictive model using these endophenotypes led to remarkable sensitivity, specificity, precision and classification rate for ADHD diagnosis, and may be a useful tool for patients' diagnosis, follow-up, and longitudinal assessment in the clinical setting.

Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.

A new modified Kies Fréchet distribution: Applications of mortality rate of Covid-19

The purpose of this paper is to identify an effective statistical distribution for examining COVID-19 mortality rates in Canada and Netherlands in order to model the distribution of COVID-19. The modified Kies Frechet (MKIF) model is an advanced three parameter lifetime distribution that was developed by incorporating the Frechet and modified Kies families. In particular with respect to current distributions, the latest one has very versatile probability functions: increasing, decreasing, and inverted U shapes are observed for the hazard rate functions, indicating that the capability of adaptability of the model. A straight forward linear representation of PDF, moment generating functions, Probability weighted moments and hazard rate functions are among the enticing features of this novel distribution. We used three different estimation methodologies to estimate the pertinent parameters of MKIF model like least squares estimators (LSEs), maximum likelihood estimators (MLEs) and weighted least squares estimators (WLSEs). The efficiency of these estimators is assessed using a thorough Monte Carlo simulation analysis. We evaluated the newest model for a variety of data sets to examine how effectively it handled data modeling. The real implementation demonstrates that the proposed model outperforms competing models and can be selected as a superior model for developing a statistical model for COVID-19 data and other similar data sets.

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.

The relationship between development assistance for health and public health financing in 134 countries between 2000 and 2015

This paper utilizes causal time-series and panel techniques to examine the relationship between development assistance for health (DAH) and domestic health spending, both public and private, in 134 countries between 2000 and 2015. Data on 237 656 donor transactions from the Institute for Health Metrics and Evaluation's DAH and Health Expenditure datasets are merged with economic, demographic and health data from the World Bank Databank and World Health Organization's Global Health Observatory. Arellano-Bond system GMM estimation is used to assess the effect of changes in DAH on domestic health spending and health outcomes. Analyses are conducted for the entire health sector and separately for HIV, TB and malaria financing. Results show that DAH had no significant impact on overall domestic public health investment. For HIV-specific investments, a $1 increase in on-budget DAH was associated with a $0.12 increase in government spending for HIV. For the private sector, $1 in DAH is associated with a $0.60 and $0.03 increase in prepaid private spending overall and for malaria, with no significant impact on HIV spending. Results demonstrate that a 1% increase in public financing reduced under-5 mortality by 0.025%, while a 1% increase in DAH had no significant effect on reducing under-5 mortality. The relationships between DAH and public health financing suggest that malaria and HIV-specific crowding-in effects are offset by crowding-out effects in other unobserved health sectors. The results also suggest policies that crowd-in public financing will likely have larger impacts on health outcomes than DAH investments that do not crowd-in public spending.

The Skewed-Elliptical Log-Linear Birnbaum–Saunders Alpha-Power Model

In this paper, the skew-elliptical sinh-alpha-power distribution is developed as a natural follow-up to the skew-elliptical log-linear Birnbaum–Saunders alpha-power distribution, previously studied in the literature. Special cases include the ordinary log-linear Birnbaum–Saunders and skewed log-linear Birnbaum–Saunders distributions. As shown, it is able to surpass the ordinary sinh-normal models when fitting data sets with high (above the expected with the sinh-normal) degrees of asymmetry. Maximum likelihood estimation is developed with the inverse of the observed information matrix used for standard error estimation. Large sample properties of the maximum likelihood estimators such as consistency and asymptotic normality are established. An application is reported for the data set previously analyzed in the literature, where performance of the new distribution is shown when compared with other proposed alternative models.

A retrospective look at cross model validation and its applicability in vibrational spectroscopy

In this paper, it is presented how Cross Model Validation (CMV), also known as double cross validation, efficiently can be applied for variable selection in spectroscopic applications. The chosen applications are FT-IR spectroscopic measurements of mixtures of marzipan and NIR spectra of diesel fuels. Standard Normal Variate (SNV) is applied as a spectral pre-treatment to reduce baseline effects in the spectra for the FT-IR data whereas 2nd derivative was applied for the diesel fuels. Variable selection based on jack-knifing and frequency of significance from Cross Model Validation is employed for identifying non-relevant spectral regions as well as providing a relevant subset for model optimization. The results show a high degree of correspondence between the objectively found wavelength bands and the reported chemical interpretation found in the literature. In addition, the stability of the models due to conservative validation with respect to predictive performance is exemplified. Finally, an example of how the use of downweighing variables ensures optimal prediction ability and detailed model interpretation is shown.

ADGRL3, FGF1 and DRD4: Linkage and Association with Working Memory and Perceptual Organization Candidate Endophenotypes in ADHD

Attention deficit hyperactivity disorder (ADHD) is a highly heritable neurobehavioral disorder that affects children worldwide, with detrimental long-term consequences in affected individuals. ADHD-affected patients display visual-motor and visuospatial abilities and skills that depart from those exhibited by non-affected individuals and struggle with perceptual organization, which might partially explain impulsive responses. Endophenotypes (quantifiable or dimensional constructs that are closely related to the root cause of the disease) might provide a more powerful and objective framework for dissecting the underlying neurobiology of ADHD than that of categories offered by the syndromic classification. In here, we explore the potential presence of the linkage and association of single-nucleotide polymorphisms (SNPs), harbored in genes implicated in the etiology of ADHD (ADGRL3, DRD4, and FGF1), with cognitive endophenotypes related to working memory and perceptual organization in 113 nuclear families. These families were ascertained from a geographical area of the Caribbean coast, in the north of Colombia, where the community is characterized by its ethnic diversity and differential gene pool. We found a significant association and linkage of markers ADGRL3-rs1565902, DRD4-rs916457 and FGF1-rs2282794 to neuropsychological tasks outlining working memory and perceptual organization such as performance in the digits forward and backward, arithmetic, similarities, the completion of figures and the assembly of objects. Our results provide strong support to understand ADHD as a combination of working memory and perceptual organization deficits and highlight the importance of the genetic background shaping the neurobiology, clinical complexity, and physiopathology of ADHD. Further, this study supplements new information regarding an ethnically diverse community with a vast African American contribution, where ADHD studies are scarce.

The Extended Log-Logistic Distribution: Inference and Actuarial Applications

Actuaries are interested in modeling actuarial data using loss models that can be adopted to describe risk exposure. This paper introduces a new flexible extension of the log-logistic distribution, called the extended log-logistic (Ex-LL) distribution, to model heavy-tailed insurance losses data. The Ex-LL hazard function exhibits an upside-down bathtub shape, an increasing shape, a J shape, a decreasing shape, and a reversed-J shape. We derived five important risk measures based on the Ex-LL distribution. The Ex-LL parameters were estimated using different estimation methods, and their performances were assessed using simulation results. Finally, the performance of the Ex-LL distribution was explored using two types of real data from the engineering and insurance sciences. The analyzed data illustrated that the Ex-LL distribution provided an adequate fit compared to other competing distributions such as the log-logistic, alpha-power log-logistic, transmuted log-logistic, generalized log-logistic, Marshall–Olkin log-logistic, inverse log-logistic, and Weibull generalized log-logistic distributions.

Generalized Ratio-cum-Product Estimator for Finite Population Mean under Two-Phase Sampling Scheme

A method to lower the MSE of a proposed estimator relative to the MSE of the linear regression estimator under two-phase sampling scheme is developed. Estimators are developed to estimate the mean of the variate under study with the help of auxiliary variate (which are unknown but it can be accessed conveniently and economically). The mean square errors equations are obtained for the proposed estimators. In addition, optimal sample sizes are obtained under the given cost function. The comparison study has been done to set up conditions for which developed estimators are more effective than other estimators with novelty. The empirical study is also performed to supplement the claim that the developed estimators are more efficient.

Interannual variability of the frequency of MJO phases and its association with two types of ENSO

In this study, we reexamine the effect of two types of El Niño Southern Oscillation (ENSO) modes on Madden Julian Oscillation (MJO) activity in terms of the frequency of MJO phases. Evaluating all-season data, we identify two dominant zonal patterns of MJO frequency exhibiting prominent interannual variability. These patterns are structurally similar to the Wheeler and Hendon (Mon. Weather Rev. 132:1917–1932, 2004) RMM1 and RMM2 spatial patterns. The first pattern explains a higher frequency of MJO activity over the Maritime Continent and a lower frequency over the central Pacific Ocean and the western Indian Ocean, or vice versa. The second pattern is associated with a higher frequency of MJO active days over the eastern Indian Ocean and a lower frequency over the western Pacific, or vice versa. We find that these two types of MJO frequency patterns are related to the central Pacific and eastern Pacific ENSO modes. From the positive to the negative ENSO (central Pacific or eastern Pacific) phases, the respective MJO frequency patterns change their sign. The MJO frequency patterns are the lag response of the underlying ocean state. The coupling between ocean and atmosphere is exceedingly complex. The first MJO frequency pattern is most prominent during the negative central-Pacific (CP-type) ENSO phases (specifically during September–November and December-February seasons). The second MJO frequency pattern is most evident during the positive eastern-Pacific (EP-type) ENSO phases (specifically during March–May, June–August and September–November). Different zonal circulation patterns during CP-type and EP-type ENSO phases alter the mean moisture distribution throughout the tropics. The horizontal convergence of mean background moisture through intraseasonal winds are responsible for the MJO frequency anomalies during the two types of ENSO phases. The results here show how the MJO activity gets modulated on a regional scale in the presence of two types of ENSO events and can be useful in anticipating the seasonal MJO conditions from a predicted ENSO state.

A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

COVID-19 data analysis using HJ-Biplot method: A study case

COVID-19 is a new viral disease declared as a pandemic by the World Health Organization in 2020. This highly infectious disease, caused by the virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has shown a different impact on young individuals, old individuals, and people with other health conditions. In this study, we aim to identify the different relationships between COVID-19 with other health complications. Additionally, our purpose is to ordinate the ages of individuals according to COVID-19 affection. For this, we apply the HJ-Biplot along with Cluster multivariate analysis techniques to a data set of patients diagnosed with COVID-19, Pneumonia, smoking Habits, Diabetes, Obesity, Chronic Obstructive Pulmonary Disease, Asthma, Immunosuppression, Hypertension, Cardiovascular Problems, Chronic Renal Insufficiency, and other health problems. The data set that we use in this work was obtained from the Mexico Government website. This exploratory research illustrates that COVID-19 disease presents different relationships with other health problems and individuals. For example, this viral disease is highly correlated with Hypertension, Diabetes, smoking habits, and Obesity health conditions. Other high correlations are found among Chronic Obstructive Pulmonary Disease, Cardiovascular problems, Pneumonia, Chronic Renal Insufficiency, Hypertension, and Diabetes. Also, the ordination of individuals according to COVID-19 affection shows us that 60 to 89 years old people are highly affected by this disease. Furthermore, we identify the formation of two clusters of individuals. On the one hand, the first Cluster is formed by old individuals highly affected by many diseases. On the other hand, the second Cluster is formed mainly by young individuals lowly affected by this study's health problems. Identifying COVID-19 correlated health conditions and the age of the most affected individuals is crucial to the correct handle of the pandemic.

A modified truncated distribution for modeling the heavy tail, engineering and environmental sciences data

Truncated models are imperative to efficiently analyze the finite data that we observe in almost all the real life situations. In this paper, a new truncated distribution having four parameters named Weibull-Truncated Exponential Distribution (W-TEXPD) is developed. The proposed model can be used as an alternative to the Exponential, standard Weibull and shifted Gamma-Weibull and three parameter Weibull distributions. The statistical characteristics including cumulative distribution function, hazard function, cumulative hazard function, central moments, skewness, kurtosis, percentile and entropy of the proposed model are derived. The maximum likelihood estimation method is employed to evaluate the unknown parameters of the W-TEXPD. A simulation study is also carried out to assess the performance of the model parameters. The proposed probability distribution is fitted on five data sets from different fields to demonstrate its vast application. A comparison of the proposed model with some extant models is given to justify the performance of the W-TEXPD.

Sensitivity Analysis of the Optimal Inventory-Pooling Strategies According to Multivariate Demand Dependence

Inventory-pooling (IP) is an effective tool to mitigate demand uncertainty and variability, to reduce operational costs, and consequently to increase the profit. The major assumptions of the previous works in literature on IP include the following: (1) Independents demand, which satisfy the typical normal independent and identically distributed (iid) random variables; (2) dependents (correlated) symmetric demands, which follows to a multivariate normal distribution. The effect of the dependent asymmetric demand is not yet studied. The aim of this paper is to consider this more realistic case. Indeed, the contribution of this paper is twofold. Firstly, it analyzes both the sensitivity of dependence structure and the levels of skewness of distributions on IP policies in terms of optimal total cost and demand satisfaction constraint. Secondly, both symmetric and asymmetric demand distributions are modeled using various beta distribution and the dependance between demands are modeled using various copulas. A newsvendor problem inspired by the literature, with two decentralized locations and two centralized locations, is considered the empirical study. For each dependance situation, three IP models are considered: inventory centralization, regular transshipments, and independent systems. The results suggest divergences in the decisions in about 9% of cases. Bad choice of marginal distributions given that the copula is appropriate can lead to divergences that vary between 2.2% and 4%, depending on whether the demand distributions are symmetric or asymmetric.

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients' gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

D. Stress-Strength Reliability Estimation of Time-Dependent Models with Fixed Stress and Phase Type Strength Distribution

The time-dependent stress-strength reliability models deal with systems whose strength or the stress imposed on it or both are time-dependent. In this paper, we consider time-dependent stress-strength reliability model which is subjected to constant stress and it causes a change in the strength of the system over each run of the system. Assuming a continuous phase- type distribution for the initial strength and exponential distribution for the duration of each run of the system called cycle time we derived the expression for the stress-strength reliability of the system at time t. The model is further extended to the cases where cycle time distribution is Gamma and Weibull. Simulation studies are conducted to assess the variations in stress-strength reliability, R(t) at different time points, corresponding to the changes in the initial strength distribution and cycle time distribution.