Error density estimation in high-dimensional sparse linear model

2018 ◽  
Vol 72 (2) ◽  
pp. 427-449
Author(s):  
Feng Zou ◽  
Hengjian Cui
Keyword(s):  
Linear Model ◽  
Density Estimation ◽  
High Dimensional ◽  
Error Density

High-dimensional linear model selection motivated by multiple testing

Statistics ◽  
2019 ◽  
Vol 54 (1) ◽  
pp. 152-166
Author(s):  
Konrad Furmańczyk ◽  
Wojciech Rejchel
Keyword(s):  
Model Selection ◽  
Linear Model ◽  
Multiple Testing ◽  
High Dimensional

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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