Simultaneous variable selection for joint models of longitudinal and survival outcomes

10.1111/biom.12221 ◽

2014 ◽

Vol 71 (1) ◽

pp. 178-187 ◽

Author(s):

Zangdong He ◽

Wanzhu Tu ◽

Sijian Wang ◽

Haoda Fu ◽

Zhangsheng Yu

Keyword(s):

Variable Selection ◽

Survival Outcomes ◽

Joint Models ◽

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Network-Based Variable Selection for Survival Outcomes in Oncological Data

Bioinformatics and Biomedical Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-030-45385-5_49 ◽

2020 ◽

pp. 550-561

Author(s):

Eunice Carrasquinha ◽

André Veríssimo ◽

Marta B. Lopes ◽

Susana Vinga

Keyword(s):

Variable Selection ◽

Survival Outcomes ◽

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Variable selection for joint models of multivariate longitudinal measurements and event time data

Statistics in Medicine ◽

10.1002/sim.7391 ◽

2017 ◽

Vol 36 (24) ◽

pp. 3820-3829 ◽

Author(s):

Yuqi Chen ◽

Yuedong Wang

Keyword(s):

Variable Selection ◽

Joint Models ◽

Time Data ◽

Longitudinal Measurements ◽

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Strong Consistency of Variable Selection for Stationary Linear Stochastic Systems

2019 Chinese Control Conference (CCC) ◽

10.23919/chicc.2019.8866312 ◽

2019 ◽

Author(s):

ZHAO Wenxiao ◽

YIN G. George ◽

BAI Er-Wei

Keyword(s):

Variable Selection ◽

Strong Consistency ◽

Stochastic Systems ◽

Linear Stochastic Systems ◽

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Variable selection for semiparametric random-effects conditional density models with longitudinal data

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2018.1554130 ◽

2018 ◽

Vol 49 (4) ◽

pp. 977-996

Author(s):

Xiaohui Yuan ◽

Yue Wang ◽

Tianqing Liu

Keyword(s):

Variable Selection ◽

Longitudinal Data ◽

Random Effects ◽

Conditional Density ◽

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Robust communication-efficient distributed composite quantile regression and variable selection for massive data

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2021.107262 ◽

2021 ◽

pp. 107262

Author(s):

Kangning Wang ◽

Shaomin Li ◽

Benle Zhang

Keyword(s):

Variable Selection ◽

Quantile Regression ◽

Massive Data ◽

Composite Quantile Regression ◽

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Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

Cogent Economics & Finance ◽

10.1080/23322039.2019.1597956 ◽

2019 ◽

Vol 7 (1) ◽

pp. 1597956

Author(s):

Carlos Valencia ◽

Sergio Cabrales ◽

Laura Garcia ◽

Juan Ramirez ◽

Diego Calderona ◽

...

Keyword(s):

Variable Selection ◽

Generalized Additive Model ◽

Additive Model ◽

Bankruptcy Prediction ◽

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Variable Selection for Mixed Data Clustering: Application in Human Population Genomics

Journal of Classification ◽

10.1007/s00357-018-9301-y ◽

2019 ◽

Vol 37 (1) ◽

pp. 124-142 ◽

Author(s):

Matthieu Marbac ◽

Mohammed Sedki ◽

Tienne Patin

Keyword(s):

Variable Selection ◽

Data Clustering ◽

Human Population ◽

Population Genomics ◽

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Automatic variable selection for varying coefficient models with longitudinal data

Statistics & Probability Letters ◽

10.1016/j.spl.2016.07.012 ◽

2016 ◽

Vol 119 ◽

pp. 84-90 ◽

Author(s):

Ruiqin Tian ◽

Liugen Xue ◽

Dengke Xu

Keyword(s):

Variable Selection ◽

Longitudinal Data ◽

Varying Coefficient Models ◽

Varying Coefficient ◽

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Flexible variable selection for recovering sparsity in nonadditive nonparametric models

10.1111/biom.12518 ◽

2016 ◽

Vol 72 (4) ◽

pp. 1155-1163 ◽

Author(s):

Zaili Fang ◽

Inyoung Kim ◽

Patrick Schaumont

Keyword(s):

Variable Selection ◽

Nonparametric Models ◽

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A novel heterogeneous ensemble approach to variable selection for gas-liquid two-phase CO2 flow metering

International Journal of Greenhouse Gas Control ◽

10.1016/j.ijggc.2021.103418 ◽

2021 ◽

Vol 110 ◽

pp. 103418

Author(s):

Caiying Sun ◽

Lijuan Wang ◽

Yong Yan ◽

Wenbiao Zhang ◽

Ding Shao

Keyword(s):

Variable Selection ◽

Two Phase ◽

Ensemble Approach ◽

Flow Metering ◽

Selection For ◽

Co2 Flow ◽

Heterogeneous Ensemble

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