Model selection for high-dimensional linear regression with dependent observations

The Annals of Statistics ◽

10.1214/19-aos1872 ◽

2020 ◽

Vol 48 (4) ◽

pp. 1959-1980

Author(s):

Ching-Kang Ing

Keyword(s):

Model Selection ◽

Linear Regression ◽

High Dimensional ◽

Dependent Observations ◽

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Relative Cost Based Model Selection for Sparse High-Dimensional Linear Regression Models

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp40776.2020.9054045 ◽

2020 ◽

Author(s):

Prakash B. Gohain ◽

Magnus Jansson

Keyword(s):

Model Selection ◽

Linear Regression ◽

Regression Models ◽

High Dimensional ◽

Relative Cost ◽

Linear Regression Models ◽

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Model Selection for High-Dimensional Problems

Handbook of Financial Econometrics and Statistics ◽

10.1007/978-1-4614-7750-1_77 ◽

2014 ◽

pp. 2093-2118

Author(s):

Jing-Zhi Huang ◽

Zhan Shi ◽

Wei Zhong

Keyword(s):

Model Selection ◽

High Dimensional ◽

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Multiobjective model selection for non-linear regression techniques

2010 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2010.5649190 ◽

2010 ◽

Author(s):

Luca Pasolli ◽

Claudia Notarnicola ◽

Lorenzo Bruzzone

Keyword(s):

Model Selection ◽

Linear Regression ◽

Regression Techniques ◽

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Model Selection for High-Dimensional Quadratic Regression via Regularization

Journal of the American Statistical Association ◽

10.1080/01621459.2016.1264956 ◽

2018 ◽

Vol 113 (522) ◽

pp. 615-625 ◽

Author(s):

Ning Hao ◽

Yang Feng ◽

Hao Helen Zhang

Keyword(s):

Model Selection ◽

High Dimensional ◽

Quadratic Regression ◽

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A stepwise regression method and consistent model selection for high-dimensional sparse linear models

Statistica Sinica ◽

10.5705/ss.2010.081 ◽

2011 ◽

Vol 21 (4) ◽

Author(s):

Ching-Kang Ing ◽

Tze Leung Lai

Keyword(s):

Model Selection ◽

Stepwise Regression ◽

Linear Models ◽

Regression Method ◽

High Dimensional ◽

Consistent Model ◽

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Robust Bayesian model selection for heavy-tailed linear regression using finite mixtures

Brazilian Journal of Probability and Statistics ◽

10.1214/18-bjps417 ◽

2020 ◽

Vol 34 (1) ◽

pp. 51-70 ◽

Author(s):

Flávio B. Gonçalves ◽

Marcos O. Prates ◽

Victor Hugo Lachos

Keyword(s):

Model Selection ◽

Linear Regression ◽

Bayesian Model ◽

Bayesian Model Selection ◽

Finite Mixtures ◽

Selection For ◽

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Budget constrained model selection for multiple linear regression

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2021.1991956 ◽

2021 ◽

pp. 1-13

Author(s):

Jingying Zhang ◽

Ronald L. Rardin ◽

Justin R. Chimka

Keyword(s):

Model Selection ◽

Linear Regression ◽

Multiple Linear Regression ◽

Constrained Model ◽

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Model Selection for High-Dimensional Models

Handbook of Survival Analysis ◽

10.1201/b16248-24 ◽

2016 ◽

pp. 309-330

Keyword(s):

Model Selection ◽

High Dimensional ◽

Selection For ◽

Dimensional Models

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Model selection for high-dimensional data

2016 50th Asilomar Conference on Signals, Systems and Computers ◽

10.1109/acssc.2016.7869114 ◽

2016 ◽

Author(s):

Arash Owrang ◽

Magnus Jansson

Keyword(s):

Model Selection ◽

High Dimensional Data ◽

High Dimensional ◽

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Global optimal model selection for high-dimensional survival analysis

Journal of Statistical Computation and Simulation ◽

10.1080/00949655.2021.1954183 ◽

2021 ◽

pp. 1-14

Author(s):

Guotao Chu ◽

Gyuhyeong Goh

Keyword(s):

Survival Analysis ◽

Model Selection ◽

High Dimensional ◽

Optimal Model ◽

Global Optimal ◽

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