Generalized empirical likelihood inference in partial linear regression model for longitudinal data

Statistics ◽  
2017 ◽  
Vol 51 (5) ◽  
pp. 988-1005 ◽  
Author(s):  
Ruiqin Tian ◽  
Liugen Xue
Keyword(s):  
Linear Regression ◽  
Longitudinal Data ◽  
Regression Model ◽  
Empirical Likelihood ◽  
Linear Regression Model ◽  
Likelihood Inference ◽  
Generalized Empirical Likelihood ◽  
Partial Linear

scholarly journals A Literature Review of Semi-functional Partial Linear Regression Models

2021 ◽  
Author(s):  
Mohammad Fayaz
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Missing Values ◽  
Electricity Consumption ◽  
Confidence Regions ◽  
Mixed Data ◽  
Linear Regression Models ◽  
Partial Linear ◽  
Functional Linear Regression

Background: In the functional data analysis (FDA), the hybrid or mixed data are scalar and functional datasets. The semi-functional partial linear regression model (SFPLR) is one of the first semiparametric models for the scalar response with hybrid covariates. Various extensions of this model are explored and summarized. Methods: Two first research articles, including “semi-functional partial linear regression model”, and “Partial functional linear regression” have more than 300 citations in Google Scholar. Finally, only 106 articles remained according to the inclusion and exclusion criteria such as 1) including the published articles in the ISI journals and excluding 2) non-English and 3) preprints, slides, and conference papers. We use the PRISMA standard for systematic review. Results: The articles are categorized into the following main topics: estimation procedures, confidence regions, time series, and panel data, Bayesian, spatial, robust, testing, quantile regression, varying Coefficient Models, Variable Selection, Single-index model, Measurement error, Multiple Functions, Missing values, Rank Method and Others. There are different applications and datasets such as the Tecator dataset, air quality, electricity consumption, and Neuroimaging, among others. Conclusions: SFPLR is one of the most famous regression modeling methods for hybrid data that has a lot of extensions among other models.

ARMA Model and Wavelet-Based ARMA Model Application

2011 ◽  
Vol 121-126 ◽  
pp. 1799-1803 ◽  
Author(s):  
Wei Wei ◽  
Hao Ma
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Arma Model ◽  
Optimal Model ◽  
Unknown Parameters ◽  
Model Application ◽  
Partially Linear ◽  
Modeling Theory ◽  
Partial Linear

This article discusses some of the linear regression model from the modeling theory, focusing on the modeling method of selecting the optimal model, choose the best bandwidth criteria. Then, given some of the partial linear regression model from the estimates and the partial residual nuclear smooth estimates, and estimate the model using the partial residual in the unknown parameters and to estimate the unknown function. Finally, the establishment of the Shanghai Index and Shenzhen Component Index Partially linear regression model.

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