Statistical diagnosis for non-parametric regression models with random right censorship based on the empirical likelihood method

2015 ◽  
Vol 42 (6) ◽  
pp. 1367-1373
Author(s):  
Shuling Wang ◽  
Xiaoyan Wang ◽  
Jiangtao Dai
Keyword(s):  
Empirical Likelihood ◽  
Regression Models ◽  
Likelihood Method ◽  
Parametric Regression ◽  
Empirical Likelihood Method ◽  
Right Censorship ◽  
Parametric Regression Models ◽  
Non Parametric

scholarly journals Empirical Likelihood Inference for Partial Functional Linear Regression Models Based on B-spline

2018 ◽  
Vol 8 (1) ◽  
pp. 135
Author(s):  
Mingao Yuan ◽  
Yue Zhang
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Confidence Regions ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
B Spline ◽  
Empirical Likelihood Method ◽  
Regression Parameters ◽  
Functional Linear Regression

In this paper, we apply empirical likelihood method to infer for the regression parameters in the partial functional linear regression models based on B-spline. We prove that the empirical log-likelihood ratio for the regression parameters converges in law to a weighted sum of independent chi-square distributions. Our simulation shows that the proposed empirical likelihood method produces more accurate confidence regions in terms of coverage probability than the asymptotic normality method.

Using linear and non-parametric regression models to describe the contribution of non-linear loads on the voltage harmonic distortions in the electrical grid

2016 ◽  
Vol 10 (8) ◽  
pp. 1825-1832 ◽  
Author(s):  
Edson Ortiz de Matos ◽  
Allan Rodrigo Arrifano Manito ◽  
Ubiratan Holanda Bezerra ◽  
Benjamim Cordeiro Costa ◽  
Thiago Mota Soares ◽  
...  
Keyword(s):  
Regression Models ◽  
Parametric Regression ◽  
Electrical Grid ◽  
Non Linear ◽  
Parametric Regression Models ◽  
Harmonic Distortions ◽  
Non Parametric
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