Extreme quantile estimation for partial functional linear regression models with heavy‐tailed distributions

2021 ◽  
Author(s):  
Hanbing Zhu ◽  
Yehua Li ◽  
Baisen Liu ◽  
Weixin Yao ◽  
Riquan Zhang
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Quantile Estimation ◽  
Linear Regression Models ◽  
Extreme Quantile ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Functional Linear Regression

scholarly journals Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xuedong Chen ◽  
Qianying Zeng ◽  
Qiankun Song
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Expectation Maximization Algorithm ◽  
Simulated Data ◽  
Likelihood Method ◽  
Skew Normal Distribution ◽  
Linear Regression Models ◽  
Heavy Tailed Distributions ◽  
Random Term ◽  
Heavy Tailed

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew-tdistribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing thel1penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.

Estimation and inference for covariate adjusted partially functional linear regression models

2021 ◽  
Vol 14 (4) ◽  
pp. 359-371
Author(s):  
Zhiqiang Jiang ◽  
Zhensheng Huang ◽  
Hanbing Zhu
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Functional Linear Regression
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