Jackknife empirical likelihood test for high-dimensional regression coefficients

2016 ◽  
Vol 94 ◽  
pp. 302-316 ◽  
Author(s):  
Yangguang Zang ◽  
Sanguo Zhang ◽  
Qizhai Li ◽  
Qingzhao Zhang
Keyword(s):  
Empirical Likelihood ◽  
Regression Coefficients ◽  
High Dimensional ◽  
High Dimensional Regression ◽  
Jackknife Empirical Likelihood

scholarly journals Rank-based score tests for high-dimensional regression coefficients

2013 ◽  
Vol 7 (0) ◽  
pp. 2131-2149 ◽  
Author(s):  
Long Feng ◽  
Changliang Zou ◽  
Zhaojun Wang ◽  
Bin Chen
Keyword(s):  
Regression Coefficients ◽  
High Dimensional ◽  
Score Tests ◽  
High Dimensional Regression

scholarly journals High-dimensional regression over disease subgroups

2016 ◽  
Author(s):  
Frank Dondelinger ◽  
Sach Mukherjee ◽  
Keyword(s):  
Related Problem ◽  
Regression Models ◽  
Simulated Data ◽  
Additional Term ◽  
Regression Coefficients ◽  
High Dimensional ◽  
High Dimensional Regression ◽  
Disease Subtypes ◽  
Problem Instances ◽  
Lateral Sclerosis

AbstractWe consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where disease subtypes, for example, may differ with respect to underlying regression models, but sample sizes at the subgroup-level may be limited. We focus on the case in which subgroup-specific models may be expected to be similar but not necessarily identical. Our approach is to treat subgroups as related problem instances and jointly estimate subgroup-specific regression coefficients. This is done in a penalized framework, combining an ℓ1 term with an additional term that penalizes differences between subgroup-specific coefficients. This gives solutions that are globally sparse but that allow information-sharing between the subgroups. We present algorithms for estimation and empirical results on simulated data and using Alzheimer’s disease, amyotrophic lateral sclerosis and cancer datasets. These examples demonstrate the gains our approach can offer in terms of prediction and the ability to estimate subgroup-specific sparsity patterns.

Empirical likelihood for high-dimensional partially functional linear model

2019 ◽  
Vol 09 (04) ◽  
pp. 2050017
Author(s):  
Zhiqiang Jiang ◽  
Zhensheng Huang ◽  
Guoliang Fan
Keyword(s):  
Linear Model ◽  
Empirical Likelihood ◽  
Likelihood Ratio Statistic ◽  
Real Data ◽  
Regression Coefficients ◽  
High Dimensional ◽  
Regularity Conditions ◽  
Functional Linear Model ◽  
Data Set ◽  
Functional Predictors

This paper considers empirical likelihood inference for a high-dimensional partially functional linear model. An empirical log-likelihood ratio statistic is constructed for the regression coefficients of non-functional predictors and proved to be asymptotically normally distributed under some regularity conditions. Moreover, maximum empirical likelihood estimators of the regression coefficients of non-functional predictors are proposed and their asymptotic properties are obtained. Simulation studies are conducted to demonstrate the performance of the proposed procedure and a real data set is analyzed for illustration.

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