Regularization and variable selection for infinite variance autoregressive models

Ganggang Xu; Yanbiao Xiang; Suojin Wang; Zhengyan Lin

doi:10.1016/j.jspi.2012.03.014

Regularization and variable selection for infinite variance autoregressive models

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2012.03.014 ◽

2012 ◽

Vol 142 (9) ◽

pp. 2545-2553 ◽

Cited By ~ 2

Author(s):

Ganggang Xu ◽

Yanbiao Xiang ◽

Suojin Wang ◽

Zhengyan Lin

Keyword(s):

Variable Selection ◽

Autoregressive Models ◽

Infinite Variance ◽

Selection For

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Efficient estimation and variable selection for infinite variance autoregressive models

Journal of Applied Mathematics and Computing ◽

10.1007/s12190-012-0567-7 ◽

2012 ◽

Vol 40 (1-2) ◽

pp. 399-413 ◽

Cited By ~ 6

Author(s):

Linjun Tang ◽

Zhangong Zhou ◽

Changchun Wu

Keyword(s):

Variable Selection ◽

Efficient Estimation ◽

Autoregressive Models ◽

Infinite Variance ◽

Selection For

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Variable selection for spatial autoregressive models

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2019.1649428 ◽

2019 ◽

pp. 1-16

Author(s):

Li Xie ◽

Xiaorui Wang ◽

Weihu Cheng ◽

Tian Tang

Keyword(s):

Variable Selection ◽

Autoregressive Models ◽

Spatial Autoregressive ◽

Selection For ◽

Spatial Autoregressive Models

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Orthogonal projection based variable selection for semiparametric spatial autoregressive models

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2021.2012193 ◽

2021 ◽

pp. 1-12

Author(s):

Peixin Zhao ◽

Hao Wu ◽

Suli Cheng ◽

Yiping Yang

Keyword(s):

Variable Selection ◽

Orthogonal Projection ◽

Autoregressive Models ◽

Spatial Autoregressive ◽

Selection For ◽

Spatial Autoregressive Models

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Variable selection for spatial autoregressive models with a diverging number of parameters

Statistical Papers ◽

10.1007/s00362-018-0984-2 ◽

2018 ◽

Vol 61 (3) ◽

pp. 1125-1145 ◽

Cited By ~ 5

Author(s):

Tianfa Xie ◽

Ruiyuan Cao ◽

Jiang Du

Keyword(s):

Variable Selection ◽

Autoregressive Models ◽

Spatial Autoregressive ◽

Diverging Number Of Parameters ◽

Selection For ◽

Spatial Autoregressive Models

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Random Forest Variable Selection for Sparse Vector Autoregressive Models

Contributions to Statistics - Theory and Applications of Time Series Analysis ◽

10.1007/978-3-030-56219-9_1 ◽

2020 ◽

pp. 3-17

Author(s):

Dmitry Pavlyuk

Keyword(s):

Random Forest ◽

Variable Selection ◽

Autoregressive Models ◽

Vector Autoregressive Models ◽

Vector Autoregressive ◽

Sparse Vector ◽

Selection For

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Variable selection for semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2019.1659367 ◽

2019 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Guowang Luo ◽

Mixia Wu

Keyword(s):

Variable Selection ◽

Autoregressive Models ◽

Varying Coefficient ◽

Spatial Autoregressive ◽

Diverging Number Of Parameters ◽

Selection For ◽

Spatial Autoregressive Models

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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 ◽

Selection For

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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 ◽

Selection For

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Orthogonality based penalized GMM estimation for variable selection in partially linear spatial autoregressive models

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2021.1937652 ◽

2021 ◽

pp. 1-16

Author(s):

Peixin Zhao ◽

Haogeng Gan ◽

Suli Cheng ◽

Xiaoshuang Zhou

Keyword(s):

Variable Selection ◽

Autoregressive Models ◽

Gmm Estimation ◽

Partially Linear ◽

Spatial Autoregressive ◽

Spatial Autoregressive Models

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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 ◽

Selection For

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