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.

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