1
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Jiang J, Shang J. Feature Screening for High-Dimensional Variable Selection in Generalized Linear Models. ENTROPY (BASEL, SWITZERLAND) 2023; 25:851. [PMID: 37372195 DOI: 10.3390/e25060851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/20/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023]
Abstract
The two-stage feature screening method for linear models applies dimension reduction at first stage to screen out nuisance features and dramatically reduce the dimension to a moderate size; at the second stage, penalized methods such as LASSO and SCAD could be applied for feature selection. A majority of subsequent works on the sure independent screening methods have focused mainly on the linear model. This motivates us to extend the independence screening method to generalized linear models, and particularly with binary response by using the point-biserial correlation. We develop a two-stage feature screening method called point-biserial sure independence screening (PB-SIS) for high-dimensional generalized linear models, aiming for high selection accuracy and low computational cost. We demonstrate that PB-SIS is a feature screening method with high efficiency. The PB-SIS method possesses the sure independence property under certain regularity conditions. A set of simulation studies are conducted and confirm the sure independence property and the accuracy and efficiency of PB-SIS. Finally we apply PB-SIS to one real data example to show its effectiveness.
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Affiliation(s)
- Jinzhu Jiang
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403, USA
| | - Junfeng Shang
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403, USA
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2
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Chen LP. A note of feature screening via a rank-based coefficient of correlation. Biom J 2023:e2100373. [PMID: 37160692 DOI: 10.1002/bimj.202100373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 09/13/2022] [Accepted: 02/05/2023] [Indexed: 05/11/2023]
Abstract
Feature screening is a useful and popular tool to detect informative predictors for ultrahigh-dimensional data before developing statistical analysis or constructing statistical models. While a large body of feature screening procedures has been developed, most methods are restricted to examine either continuous or discrete responses. Moreover, even though many model-free feature screening methods have been proposed, additional assumptions are imposed in those methods to ensure their theoretical results. To address those difficulties and provide simple implementation, in this paper we extend the rank-based coefficient of correlation to develop a feature screening procedure. We show that this new screening criterion is able to deal with continuous and binary responses. Theoretically, the sure screening property is established to justify the proposed method. Simulation studies demonstrate that the predictors with nonlinear and oscillatory trajectories are successfully retained regardless of the distribution of the response. Finally, the proposed method is implemented to analyze two microarray datasets.
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Affiliation(s)
- Li-Pang Chen
- Department of Statistics, National Chengchi University, Taipei, Taiwan, ROC
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3
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Li X, Xu C. Feature Screening with Conditional Rank Utility for Big-data Classification. J Am Stat Assoc 2023. [DOI: 10.1080/01621459.2023.2195976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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4
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Yuan Z, Chen J, Qiu H, Huang Y. Quantile-Adaptive Sufficient Variable Screening by Controlling False Discovery. ENTROPY (BASEL, SWITZERLAND) 2023; 25:524. [PMID: 36981413 PMCID: PMC10048075 DOI: 10.3390/e25030524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Sufficient variable screening rapidly reduces dimensionality with high probability in ultra-high dimensional modeling. To rapidly screen out the null predictors, a quantile-adaptive sufficient variable screening framework is developed by controlling the false discovery. Without any specification of an actual model, we first introduce a compound testing procedure based on the conditionally imputing marginal rank correlation at different quantile levels of response to select active predictors in high dimensionality. The testing statistic can capture sufficient dependence through two paths: one is to control false discovery adaptively and the other is to control the false discovery rate by giving a prespecified threshold. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.
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Affiliation(s)
- Zihao Yuan
- Department of Statistics, Wuhan University of Technology, Wuhan 430070, China
| | - Jiaqing Chen
- Department of Statistics, Wuhan University of Technology, Wuhan 430070, China
| | - Han Qiu
- Department of Statistics, Wuhan University of Technology, Wuhan 430070, China
| | - Yangxin Huang
- Department of Epidemiology and Biostatistics, University of South Florida, Tampa, FL 33612, USA
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5
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Cheng X, Wang H. A generic model-free feature screening procedure for ultra-high dimensional data with categorical response. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107269. [PMID: 36463676 DOI: 10.1016/j.cmpb.2022.107269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Identifying active features from ultra-high dimensional data is one of the primary and vital tasks in statistical learning and biological discovery. METHODS In this paper, we develop a generic concordance index screening (CI-SIS) procedure to wrestle with ultra-high dimensional data with categorical response. The proposed procedure is model-free and nonparametric based on the concordance index measure. It enjoys both sure screening and ranking consistency properties under some relatively weak assumptions. We investigate the flexibility of this procedure by considering some commonly-encountered challenging settings in biomedical studies, such as category-adaptive data and extremely unbalanced response distributions. A data-driven threshold selection procedure via knockoff features is also presented. RESULTS On the real lung dataset, our method achieves a lower prediction error with a mean error of 0.107 with linear discriminant analysis (LDA) and 0.117 with random forest (RF), respectively. In addition, we obtain an accuracy improvement of 3% with LDA and 5% with RF compared to the runner-up method. In a more challenging real data of SRBCT (Small round blue cell tumours), CI-SIS brings about a amazing performance improvement, which is at least 8% higher than all other competing methods. CONCLUSION Experimental results show that the proposed method can efficiently identify genes that are associated with certain types of diseases. Therefore, survived features (filtering out irrelevant features) selected by our procedure can help doctors make precision diagnoses and refined treatments of patients.
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Affiliation(s)
- Xuewei Cheng
- School of Mathematics and Statistics, Central South University, Changsha, China; Department of Statistics and Data Science, National University of Singapore, Singapore.
| | - Hong Wang
- School of Mathematics and Statistics, Central South University, Changsha, China.
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6
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Zhong W, Qian C, Liu W, Zhu L, Li R. Feature Screening for Interval-Valued Response with Application to Study Association between Posted Salary and Required Skills. J Am Stat Assoc 2023; 118:805-817. [PMID: 37448462 PMCID: PMC10338024 DOI: 10.1080/01621459.2022.2152342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 12/05/2022]
Abstract
It is important to quantify the differences in returns to skills using the online job advertisements data, which have attracted great interest in both labor economics and statistics fields. In this paper, we study the relationship between the posted salary and the job requirements in online labor markets. There are two challenges to deal with. First, the posted salary is always presented in an interval-valued form, for example, 5k-10k yuan per month. Simply taking the mid-point or the lower bound as the alternative for salary may result in biased estimators. Second, the number of the potential skill words as predictors generated from the job advertisements by word segmentation is very large and many of them may not contribute to the salary. To this end, we propose a new feature screening method, Absolute Distribution Difference Sure Independence Screening (ADD-SIS), to select important skill words for the interval-valued response. The marginal utility for feature screening is based on the difference of estimated distribution functions via nonparametric maximum likelihood estimation, which sufficiently uses the interval information. It is model-free and robust to outliers. Numerical simulations show that the new method using the interval information is more efficient to select important predictors than the methods only based on the single points of the intervals. In the real data application, we study the text data of job advertisements for data scientists and data analysts in a major China's online job posting website, and explore the important skill words for the salary. We find that the skill words like optimization, long short-term memory (LSTM), convolutional neural networks (CNN), collaborative filtering, are positively correlated with the salary while the words like Excel, Office, data collection, may negatively contribute to the salary.
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7
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Chamlal H, Ouaderhman T, Aaboub F. A graph based preordonnances theoretic supervised feature selection in high dimensional data. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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8
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Anzarmou Y, Mkhadri A, Oualkacha K. Sparse overlapped linear discriminant analysis. TEST-SPAIN 2022. [DOI: 10.1007/s11749-022-00839-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Independence index sufficient variable screening for categorical responses. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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10
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Li Y, Hsu W. A classification for complex imbalanced data in disease screening and early diagnosis. Stat Med 2022; 41:3679-3695. [PMID: 35603639 PMCID: PMC9541048 DOI: 10.1002/sim.9442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/11/2022] [Accepted: 05/10/2022] [Indexed: 11/09/2022]
Abstract
Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high-dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high-dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high-dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.
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Affiliation(s)
- Yiming Li
- Department of StatisticsKansas State UniversityManhattanKansasUSA
| | - Wei‐Wen Hsu
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health SciencesUniversity of CincinnatiCincinnatiOhioUSA
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11
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Conditional characteristic feature screening for massive imbalanced data. Stat Pap (Berl) 2022. [DOI: 10.1007/s00362-022-01342-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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12
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Sen S, Kundu D, Das K. Variable selection for categorical response: a comparative study. Comput Stat 2022. [DOI: 10.1007/s00180-022-01260-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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13
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Ma W, Xiao J, Yang Y, Ye F. Model-free feature screening for ultrahigh dimensional data via a Pearson chi-square based index. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2062358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Weidong Ma
- Department of Mathematical Sciences, Tsinghua University, Beijing, People's Republic of China
| | - Jingsong Xiao
- Department of Mathematical Sciences, Tsinghua University, Beijing, People's Republic of China
| | - Ying Yang
- Department of Mathematical Sciences, Tsinghua University, Beijing, People's Republic of China
| | - Fei Ye
- School of Statistics, Capital University of Economics and Business, Beijing, People's Republic of China
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14
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Yu G, Hou S. Integrative nearest neighbor classifier for block-missing multi-modality data. Stat Methods Med Res 2022; 31:1242-1262. [PMID: 35301917 DOI: 10.1177/09622802221084596] [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: 11/16/2022]
Abstract
In modern biomedical classification applications, data are often collected from multiple modalities, ranging from various omics technologies to brain scans. As different modalities provide complementary information, classifiers using multi-modality data usually have good classification performance. However, in many studies, due to the high cost of measures, in a lot of samples, some modalities are missing and therefore all data from those modalities are missing completely. In this case, the training data set is a block-missing multi-modality data set. In this paper, considering such classification problems, we develop a new weighted nearest neighbors classifier, called the integrative nearest neighbor (INN) classifier. INN harnesses all available information in the training data set and the feature vector of the test data point effectively to predict the class label of the test data point without deleting or imputing any missing data. Given a test data point, INN determines the weights on the training samples adaptively by minimizing the worst-case upper bound on the estimation error of the regression function over a convex class of functions. Our simulation study shows that INN outperforms common weighted nearest neighbors classifiers that only use complete training samples or modalities that are available in each sample. It performs better than methods that impute the missing data as well, even for the case where some modalities are missing not at random. The effectiveness of INN has been also demonstrated by our theoretical studies and a real application from the Alzheimer's disease neuroimaging initiative.
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Affiliation(s)
- Guan Yu
- Department of Biostatistics, 12292The State University of New York at Buffalo, NY, USA
| | - Surui Hou
- Department of Biostatistics, 12292The State University of New York at Buffalo, NY, USA
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15
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Mai Q, He D, Zou H. Coordinatewise Gaussianization: Theories and Applications. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2044825] [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)
- Qing Mai
- Department of Statistics, Florida State University
| | - Di He
- School of Economics, Nanjing University, Nanjing, 210046, China
| | - Hui Zou
- School of Statistics, University of Minnesota
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16
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Anzarmou Y, Mkhadri A, Oualkacha K. The Kendall interaction filter for variable interaction screening in high dimensional classification problems. J Appl Stat 2022; 50:1496-1514. [PMID: 37197752 PMCID: PMC10184587 DOI: 10.1080/02664763.2022.2031125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Accounting for important interaction effects can improve the prediction of many statistical learning models. Identification of relevant interactions, however, is a challenging issue owing to their ultrahigh-dimensional nature. Interaction screening strategies can alleviate such issues. However, due to heavier tail distribution and complex dependence structure of interaction effects, innovative robust and/or model-free methods for screening interactions are required to better scale analysis of complex and high-throughput data. In this work, we develop a new model-free interaction screening method, termed Kendall Interaction Filter (KIF), for the classification in high-dimensional settings. KIF method suggests a weighted-sum measure, which compares the overall to the within-cluster Kendall's τ of pairs of predictors, to select interactive couples of features. The proposed KIF measure captures relevant interactions for the clusters response-variable, handles continuous, categorical or a mixture of continuous-categorical features, and is invariant under monotonic transformations. The tKIF measure enjoys the sure screening property in the high-dimensional setting under mild conditions, without imposing sub-exponential moment assumptions on the features' distribution. We illustrate the favorable behavior of the proposed methodology compared to the methods in the same category using simulation studies, and we conduct real data analyses to demonstrate its utility.
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Affiliation(s)
- Youssef Anzarmou
- Department of Mathematics, University of Cadi Ayyad, Marrakech, Morocco
| | - Abdallah Mkhadri
- Department of Mathematics, University of Cadi Ayyad, Marrakech, Morocco
| | - Karim Oualkacha
- Department of Mathematics, Université du Québec à Montréal, Montreal, Québec, Canada
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17
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Unified mean-variance feature screening for ultrahigh-dimensional regression. Comput Stat 2022. [DOI: 10.1007/s00180-021-01184-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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18
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Affiliation(s)
- Xu Guo
- School of Statistics, Beijing Normal University, Beijing, China
| | - Haojie Ren
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Changliang Zou
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Runze Li
- Department of Statistics, The Pennsylvania State University, University Park, PA
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19
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Min K, Mai Q. A general framework for tensor screening through smoothing. Electron J Stat 2022. [DOI: 10.1214/21-ejs1954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Keqian Min
- Department of Statistics, Florida State University, Tallahassee, Florida 32306, U.S.A
| | - Qing Mai
- Department of Statistics, Florida State University, Tallahassee, Florida 32306, U.S.A
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20
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Yang B, Wu W, Yin X. On sufficient variable screening using log odds ratio filter. Electron J Stat 2022. [DOI: 10.1214/21-ejs1951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Baoying Yang
- Department of Statistics, College of Mathematics Southwest Jiaotong University, Chengdu, China
| | - Wenbo Wu
- Department of Management Science and Statistics The University of Texas at San Antonio, San Antonio, TX
| | - Xiangrong Yin
- Department of Statistics, University of Kentucky 319 Multidisciplinary Science Building, Lexington, KY 40536
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21
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Xiong W, Pan H. Interaction screening for high-dimensional heterogeneous data via robust hybrid metrics. Stat Med 2021; 40:6651-6673. [PMID: 34542189 DOI: 10.1002/sim.9204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 07/22/2021] [Accepted: 09/02/2021] [Indexed: 11/07/2022]
Abstract
A novel model-free interaction screening approach called the hybrid metrics is introduced for high-dimensional heterogeneous data analysis. The metrics established based on the variation of conditional joint distribution function are measurements of interaction that include both size and direction. They are robust and can work with many types of response variables, including continuous, discrete, and categorical variables. We can apply the hybrid metrics to effective interaction selection for classification, response index models, and Poisson regression, among others. When dealing with classification, the hybrid metrics are capable of capturing both nonlinear category-general and category-specific interaction effects, providing us with a comprehensive overview and precise discovery of category information. When faced with a continuous response, the hybrid metrics perform fairly well even if the signal strength is weak, behaving as if the true interactions were known. To facilitate implementation, a fast two-stage procedure which naturally and efficiently enforces both strong and weak heredity is advocated. We further demonstrate their superior performances over popular competitors by exhaustive simulations and a SRBCT real data example. Supplementary materials for this article are available online.
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Affiliation(s)
- Wei Xiong
- School of Statistics, University of International Business and Economics, Beijing, China
| | - Han Pan
- School of Statistics, University of International Business and Economics, Beijing, China
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22
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He H, Deng G. Grouped feature screening for ultra-high dimensional data for the classification model. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1981901] [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)
- Hanji He
- College of Science, Guilin University of Technology, Guilin, People’s Republic of China
| | - Guangming Deng
- Applied Statistics Institute, Guilin University of Technology, Guilin, People’s Republic of China
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23
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Tian Y, Feng Y. RaSE: A Variable Screening Framework via Random Subspace Ensembles. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1938084] [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)
- Ye Tian
- Department of Statistics, Columbia University, New York
| | - Yang Feng
- Department of Biostatistics, School of Global Public Health, New York University, New York
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24
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Dummy endogenous treatment effect estimation using high‐dimensional instrumental variables. CAN J STAT 2021. [DOI: 10.1002/cjs.11648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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25
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Guo C, Lv J, Wu J. Composite quantile regression for ultra-high dimensional semiparametric model averaging. Comput Stat Data Anal 2021. [DOI: 10.1016/j.csda.2021.107231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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26
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Huo L, Wen XM, Yu Z. A model-free conditional screening approach via sufficient dimension reduction. J Nonparametr Stat 2020. [DOI: 10.1080/10485252.2020.1834554] [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]
Affiliation(s)
- Lei Huo
- Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, USA
| | - Xuerong Meggie Wen
- Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, USA
| | - Zhou Yu
- School of Statistics, East China Normal University, Shanghai, People's Republic of China
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27
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Wu Y, Yu G. Weighted linear programming discriminant analysis for high‐dimensional binary classification. Stat Anal Data Min 2020. [DOI: 10.1002/sam.11473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Yufei Wu
- Department of BiostatisticsUniversity at Buffalo, The State University of New York Buffalo New York USA
| | - Guan Yu
- Department of BiostatisticsUniversity at Buffalo, The State University of New York Buffalo New York USA
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28
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Xu K, Shen Z, Huang X, Cheng Q. Projection correlation between scalar and vector variables and its use in feature screening with multi-response data. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1753057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Kai Xu
- School of Mathematics and Statistics, Anhui Normal University, Wuhu, People's Republic of China
| | - Zhiling Shen
- School of Mathematics and Statistics, Anhui Normal University, Wuhu, People's Republic of China
| | - Xudong Huang
- School of Mathematics and Statistics, Anhui Normal University, Wuhu, People's Republic of China
| | - Qing Cheng
- Center for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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29
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Liu W, Ke Y, Liu J, Li R. Model-Free Feature Screening and FDR Control With Knockoff Features. J Am Stat Assoc 2020. [DOI: 10.1080/01621459.2020.1783274] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Wanjun Liu
- Department of Statistics, The Pennsylvania State University, University Park, PA
| | - Yuan Ke
- Department of Statistics, University of Georgia, Athens, GA
| | - Jingyuan Liu
- MOE Key Laboratory of Econometrics, Department of Statistics, School of Economics, Wang Yanan Institute for Studies in Economics, and Fujian Key Lab of Statistics, Xiamen University, Xiamen, China
| | - Runze Li
- Department of Statistics, The Pennsylvania State University, University Park, PA
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30
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Sheng Y, Wang Q. Model-free feature screening for ultrahigh dimensional classification. J MULTIVARIATE ANAL 2020. [DOI: 10.1016/j.jmva.2020.104618] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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31
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Sun J, Du P, Miao H, Liang H. Robust feature screening procedures for single and mixed types of data. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1719104] [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]
Affiliation(s)
- Jinhui Sun
- Ping An Technology Co., Ltd., Beijing, People's Republic of China
| | - Pang Du
- Department of Statistics, Virginia Tech, Blacksburg, VA, USA
| | - Hongyu Miao
- Department of Biostatistics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hua Liang
- Department of Statistics, George Washington University, Washington, DC, USA
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32
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Sheng Y, Wang Q. Conditional probability estimation based classification with class label missing at random. J MULTIVARIATE ANAL 2020. [DOI: 10.1016/j.jmva.2019.104566] [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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Wang L, Ma X, Zhang J. Feature screening for ultrahigh-dimensional additive logistic models. J Stat Plan Inference 2020. [DOI: 10.1016/j.jspi.2019.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Li X, Tang N, Xie J, Yan X. A nonparametric feature screening method for ultrahigh-dimensional missing response. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106828] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Feature screening for ultrahigh dimensional categorical data with covariates missing at random. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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36
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Robust composite weighted quantile screening for ultrahigh dimensional discriminant analysis. METRIKA 2020. [DOI: 10.1007/s00184-019-00758-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yang B, Yin X, Zhang N. Sufficient variable selection using independence measures for continuous response. J MULTIVARIATE ANAL 2019. [DOI: 10.1016/j.jmva.2019.04.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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39
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A modified mean-variance feature-screening procedure for ultrahigh-dimensional discriminant analysis. Comput Stat Data Anal 2019. [DOI: 10.1016/j.csda.2019.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Abstract
Feature screening plays an important role in the analysis of ultrahigh dimensional data. Due to complicated model structure and high noise level, existing screening methods often suffer from model misspecification and the presence of outliers. To address these issues, we introduce a new metric named cumulative divergence (CD), and develop a CD-based forward screening procedure. This forward screening method is model-free and resistant to the presence of outliers in the response. It also incorporates the joint effects among covariates into the screening process. With a data-driven threshold, the new method can automatically determine the number of features that should be retained after screening. These merits make the CD-based screening very appealing in practice. Under certain regularity conditions, we show that the proposed method possesses sure screening property. The performance of our proposal is illustrated through simulations and a real data example.
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Affiliation(s)
- Tingyou Zhou
- School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, P. R. China
| | - Liping Zhu
- Institute of Statistics and Big Data and Center for Applied Statistics, Renmin University of China, Beijing, P. R. China
| | - Chen Xu
- Department of Mathematics and Statistics University of Ottawa, Ottawa, Canada
| | - Runze Li
- Department of Statistics and The Methodology Center, The Pennsylvania State University at University Park, U.S.A
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Xie J, Lin Y, Yan X, Tang N. Category-Adaptive Variable Screening for Ultra-High Dimensional Heterogeneous Categorical Data. J Am Stat Assoc 2019. [DOI: 10.1080/01621459.2019.1573734] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Jinhan Xie
- Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, Yunnan University, Kunming, P. R. China
| | - Yuanyuan Lin
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
| | - Xiaodong Yan
- School of Economics, Shandong University, Jinan, China
| | - Niansheng Tang
- Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, Yunnan University, Kunming, P. R. China
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Kong E, Xia Y, Zhong W. Composite Coefficient of Determination and Its Application in Ultrahigh Dimensional Variable Screening. J Am Stat Assoc 2019. [DOI: 10.1080/01621459.2018.1514305] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Efang Kong
- School of Mathematical Sciences, University of Electronic Science and Technology of China, China
| | - Yingcun Xia
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Wei Zhong
- Wang Yanan Institute for Studies in Economics (WISE), and Department of Statistics, School of Economics, Xiamen University, China
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Song F, Lai P, Shen B, Cheng G. Variance ratio screening for ultrahigh dimensional discriminant analysis. COMMUN STAT-THEOR M 2018. [DOI: 10.1080/03610926.2017.1406113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Fengli Song
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
| | - Peng Lai
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
| | - Baohua Shen
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
| | - Guosheng Cheng
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
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Yang H, Zhu H, Ibrahim JG. MILFM: Multiple index latent factor model based on high-dimensional features. Biometrics 2018; 74:834-844. [PMID: 29665616 PMCID: PMC6158073 DOI: 10.1111/biom.12866] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Revised: 11/01/2018] [Accepted: 11/01/2017] [Indexed: 11/30/2022]
Abstract
The aim of this article is to develop a multiple-index latent factor modeling (MILFM) framework to build an accurate prediction model for clinical outcomes based on a massive number of features. We develop a three-stage estimation procedure to build the prediction model. MILFM uses an independent screening method to select a set of informative features, which may have a complex nonlinear relationship with the outcome variables. Moreover, we develop a latent factor model to project all informative predictors onto a small number of local subspaces, which lead to a few key features that capture reliable and informative covariate information. Finally, we fit the regularized empirical estimate to those key features in order to accurately predict clinical outcomes. We systematically investigate the theoretical properties of MILFM, such as risk bounds and selection consistency. Our simulation results and real data analysis show that MILFM outperforms many state-of-the-art methods in terms of prediction accuracy.
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Affiliation(s)
- Hojin Yang
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A
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A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification. ADV DATA ANAL CLASSI 2018. [DOI: 10.1007/s11634-018-0334-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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46
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Yan X, Tang N, Xie J, Ding X, Wang Z. Fused mean–variance filter for feature screening. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2017.10.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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47
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Affiliation(s)
- Chong Zhang
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Wenbo Wang
- Department of Mathematical Sciences, Binghamton University, State University of New York, Binghamton, NY
| | - Xingye Qiao
- Department of Mathematical Sciences, Binghamton University, State University of New York, Binghamton, NY
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Affiliation(s)
- Bin Li
- Department of Experimental Statistics; Louisiana State University; Baton Rouge Louisiana
| | - Qingzhao Yu
- School of Public Health; Louisiana State University Health Science Center; New Orleans Louisiana
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50
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Adjusted Pearson Chi-Square feature screening for multi-classification with ultrahigh dimensional data. METRIKA 2017. [DOI: 10.1007/s00184-017-0629-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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