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Amro L, Pauly M, Ramosaj B. Asymptotic-based bootstrap approach for matched pairs with missingness in a single arm. Biom J 2021; 63:1389-1405. [PMID: 34240446 DOI: 10.1002/bimj.202000051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 12/11/2020] [Accepted: 01/20/2021] [Indexed: 11/06/2022]
Abstract
The issue of missing values is an arising difficulty when dealing with paired data. Several test procedures are developed in the literature to tackle this problem. Some of them are even robust under deviations and control type-I error quite accurately. However, most of these methods are not applicable when missing values are present only in a single arm. For this case, we provide asymptotic correct resampling tests that are robust under heteroskedasticity and skewed distributions. The tests are based on a meaningful restructuring of all observed information in quadratic form-type test statistics. An extensive simulation study is conducted exemplifying the tests for finite sample sizes under different missingness mechanisms. In addition, illustrative data examples based on real life studies are analyzed.
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Affiliation(s)
- Lubna Amro
- Mathematical Statistics and Applications in Industry, Faculty of Statistics, Technical University of Dortmund, Dortmund, Germany
| | - Markus Pauly
- Mathematical Statistics and Applications in Industry, Faculty of Statistics, Technical University of Dortmund, Dortmund, Germany
| | - Burim Ramosaj
- Mathematical Statistics and Applications in Industry, Faculty of Statistics, Technical University of Dortmund, Dortmund, Germany
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Combination rules for homoscedastic and heteroscedastic MANOVA models from multiply imputed datasets. Behav Res Methods 2021; 53:669-685. [PMID: 32804343 DOI: 10.3758/s13428-020-01429-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Classical MANOVA tests do not pose any difficulty when the assumptions on which they are based are satisfied, while the modified Brown-Forsythe (MBF) procedure has low sensitivity to the lack of multivariate normality and homogeneity of covariance matrices. Both methods assume complete data for all subjects. In this paper, we present combination rules for the MANOVA and MBF procedures with multiply imputed datasets. These rules are illustrated by pooling the results obtained with a two-factor multivariate design after applying the two approaches to each of the imputed datasets when the covariance matrices were equal (MI-MANOVA) and when the covariance matrices were unequal (MI-MBF). A Monte-Carlo study was carried out to compare the proposed solution, in terms of type I error rates and statistical power, with the MANOVA and MBF approaches without missing data, and with listwise deletion of missing data followed by the MANOVA approach (LD-MANOVA) and listwise deletion followed by the MBF procedure (LD-MBF). Simulations showed that the type I error rates in all analyses on datasets with missing values (with or without imputation) were well controlled. We also found that the MI-MANOVA approach was substantially more powerful than LD-MANOVA. Moreover, the power of the MI-MANOVA was generally comparable to that of its complete data counterpart. Similar results were obtained for the MI-MBF procedure when covariance matrices were unequal. We conclude, based on the current evidence, that the solution presented performs well and could be of practical use. We illustrate the application of combination rules using a real dataset.
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Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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Rupasinghe Arachchige Don HS, Olive DJ. Bootstrapping analogs of the one way MANOVA test. COMMUN STAT-THEOR M 2019. [DOI: 10.1080/03610926.2018.1515363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
| | - David J. Olive
- Department of Mathematics, Southern Illinois University, Carbondale, IL, USA
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Sotton B, Paris A, Le Manach S, Blond A, Duval C, Qiao Q, Catherine A, Combes A, Pichon V, Bernard C, Marie B. Specificity of the metabolic signatures of fish from cyanobacteria rich lakes. CHEMOSPHERE 2019; 226:183-191. [PMID: 30927670 DOI: 10.1016/j.chemosphere.2019.03.115] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/15/2019] [Accepted: 03/16/2019] [Indexed: 06/09/2023]
Abstract
With the increasing impact of the global warming, occurrences of cyanobacterial blooms in aquatic ecosystems are becoming a main worldwide ecological concern. Due to their capacity to produce potential toxic metabolites, interactions between the cyanobacteria, their cyanotoxins and the surrounding freshwater organisms have been investigated during the last past years. Non-targeted metabolomic analyses have the powerful capacity to study simultaneously a high number of metabolites and thus to investigate in depth the molecular signatures between various organisms encountering different environmental scenario, and potentially facing cyanobacterial blooms. In this way, the liver metabolomes of two fish species (Perca fluviatilis and Lepomis gibbosus) colonizing various peri-urban lakes of the Île-de-France region displaying high biomass of cyanobacteria, or not, were investigated. The fish metabolome hydrophilic fraction was analyzed by 1H NMR analysis coupled with Batman peak treatment for the quantification and the annotation attempt of the metabolites. The results suggest that similar metabolome profiles occur in both fish species, for individuals collected from cyanobacterial blooming lakes compared to organism from non-cyanobacterial dominant environments. Overall, such environmental metabolomic pilot study provides new research perspectives in ecology and ecotoxicology fields, and may notably provide new information concerning the cyanobacteria/fish ecotoxicological interactions.
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Affiliation(s)
- Benoît Sotton
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Alain Paris
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Séverine Le Manach
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Alain Blond
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Charlotte Duval
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Qin Qiao
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Arnaud Catherine
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Audrey Combes
- Department of Analytical, Bioanalytical Sciences and Miniaturization (LSABM), UMR CNRS-ESPCI Paris, CBI 8231, PSL Research University, ESPCI Paris, 10 rue Vauquelin, Paris, France
| | - Valérie Pichon
- Department of Analytical, Bioanalytical Sciences and Miniaturization (LSABM), UMR CNRS-ESPCI Paris, CBI 8231, PSL Research University, ESPCI Paris, 10 rue Vauquelin, Paris, France
| | - Cécile Bernard
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France
| | - Benjamin Marie
- UMR 7245 MNHN/CNRS Molécules de Communication et Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, 12 rue Buffon, F-75231, Paris Cedex 05, France.
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Bathke AC, Friedrich S, Pauly M, Konietschke F, Staffen W, Strobl N, Höller Y. Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions. MULTIVARIATE BEHAVIORAL RESEARCH 2018; 53:348-359. [PMID: 29565679 PMCID: PMC5935051 DOI: 10.1080/00273171.2018.1446320] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved.
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Affiliation(s)
- Arne C. Bathke
- Department of Mathematics, University of Salzburg; Department of Statistics, University of Kentucky
| | | | | | | | - Wolfgang Staffen
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University
| | - Nicolas Strobl
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University
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Eftekhar S, Sadooghi-Alvandi M, Kharrati-Kopaei M. Testing the equality of several multivariate normal mean vectors under heteroscedasticity: A fiducial approach and an approximate test. COMMUN STAT-THEOR M 2018. [DOI: 10.1080/03610926.2017.1324984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Sana Eftekhar
- Department of Statistics, Shiraz University, Shiraz, Iran
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Xu L, Wang D. Parametric bootstrap inferences for unbalanced panel data models. COMMUN STAT-SIMUL C 2017. [DOI: 10.1080/03610918.2016.1248567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Liwen Xu
- College of Sciences, North China University of Technology, Beijing, China
- School of Statistics, Renmin University of China, Beijing, China
| | - Dengkui Wang
- College of Sciences, North China University of Technology, Beijing, China
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Xu L, Tian M. Parametric bootstrap inferences for panel data models. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2015.1105981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Liwen Xu
- School of Statistics, Renmin University of China, Beijing, China
- College of Sciences, North China University of Technology, Beijing, China
| | - Maozai Tian
- School of Statistics, Renmin University of China, Beijing, China
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Affiliation(s)
- Łukasz Smaga
- Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska, Poznań, Poland
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Xu L. Parametric bootstrap inferences for the growth curve models with intraclass correlation structure. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2015.1060343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Liwen Xu
- Renmin University of China, North China University of Technology, Beijing, China
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Anderson MJ, Walsh DCI, Robert Clarke K, Gorley RN, Guerra-Castro E. Some solutions to the multivariate Behrens-Fisher problem for dissimilarity-based analyses. AUST NZ J STAT 2017. [DOI: 10.1111/anzs.12176] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Marti J. Anderson
- New Zealand Institute for Advanced Study (NZIAS); Massey University; Albany campus, Private Bag 102 904 North Shore Auckland 0745 New Zealand
| | - Daniel C. I. Walsh
- Institute of Natural & Mathematical Sciences (INMS); Massey University; Albany Campus, Private Bag 102 904 North Shore Auckland 0745 New Zealand
| | - K. Robert Clarke
- Plymouth Marine Laboratory; Prospect Place; The Hoe Plymouth PL1 3DH UK
| | - Ray N. Gorley
- PRIMER-E Limited, c/o Plymouth Marine Laboratory; Prospect Place; The Hoe Plymouth PL1 3DH UK
| | - Edlin Guerra-Castro
- CONACYT, Unidad Multidisciplinaria de Docencia e Investigacón Sisal, Facultad de Ciencias; Universidad Nacional Autónoma de México; Puerto de Sisal, Yucatán México
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Xu L, Yuan L. A Parametric Bootstrap Solution to Two-way MANOVA without Interaction under Heteroscedasticity: Fixed and Mixed Models. COMMUN STAT-SIMUL C 2016. [DOI: 10.1080/03610918.2014.930906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Xu L, Qu K, Wu M, Mei B, Chen R. Parametric Bootstrap Tests for Unbalanced Three-factor Nested Designs under Heteroscedasticity. COMMUN STAT-SIMUL C 2016. [DOI: 10.1080/03610918.2013.862276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Xu L, Yang F, Chen R, Yu S. A Parametric Bootstrap Test for Two-Way ANOVA Model Without Interaction Under Heteroscedasticity. COMMUN STAT-SIMUL C 2015. [DOI: 10.1080/03610918.2013.818689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Gunasekera S, Ananda MM. Generalized variable method inference for the location parameter of the general half-normal distribution. J STAT COMPUT SIM 2015. [DOI: 10.1080/00949655.2014.923424] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Parametric bootstrap approaches for two-way MANOVA with unequal cell sizes and unequal cell covariance matrices. J MULTIVARIATE ANAL 2015. [DOI: 10.1016/j.jmva.2014.09.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Xu LW, Mei B, Chen RR, Guo HX, Wang JJ. Parametric bootstrap tests for unbalanced nested designs under heteroscedasticity. J STAT COMPUT SIM 2014. [DOI: 10.1080/00949655.2013.782028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Sadooghi-Alvandi S, Malekzadeh A. Simultaneous confidence intervals for ratios of means of several lognormal distributions: A parametric bootstrap approach. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2013.07.039] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Xu LW, Yang FQ, Abula A, Qin S. A parametric bootstrap approach for two-way ANOVA in presence of possible interactions with unequal variances. J MULTIVARIATE ANAL 2013. [DOI: 10.1016/j.jmva.2012.10.008] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zhang JT. An Approximate Hotelling T<sup>2</sup>-Test for Heteroscedastic One-Way MANOVA. ACTA ACUST UNITED AC 2012. [DOI: 10.4236/ojs.2012.21001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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