A Stepwise Variable Selection Procedure for Nonlinear Regression Models

Biometrics ◽  
1980 ◽  
Vol 36 (3) ◽  
pp. 511 ◽  
Author(s):  
P. N. Peduzzi ◽  
R. J. Hardy ◽  
T. R. Holford
Keyword(s):  
Variable Selection ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Selection Procedure ◽  
Nonlinear Regression Models ◽  
Stepwise Variable Selection ◽  
Variable Selection Procedure

Penalized Estimation Based Variable Selection for Semiparametric Regression Models with Endogenous Covariates

2014 ◽  
Vol 1079-1080 ◽  
pp. 843-846
Author(s):  
Pei Xin Zhao
Keyword(s):  
Variable Selection ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Selection Procedure ◽  
Semiparametric Regression ◽  
Variable Selection Procedure ◽  
Endogenous Variables ◽  
Endogenous Covariates ◽  
Variable Selection Problem ◽  
Penalized Empirical Likelihood

In this paper, we study the variable selection problem for the parametric components of semiparametric regression models with endogenous variables. Based on the penalized empirical likelihood technology and the bias adjustment method, we propose a penalized empirical likelihood based variable selection procedure. Simulation studies show that the proposed variable selection procedure is workable, and the resulting estimator is consistent.

Nonlinear regression models for correlated count data

1992 ◽  
Vol 3 (2) ◽  
pp. 211-222 ◽  
Author(s):  
R. T. Burnett ◽  
J. Shedden ◽  
D. Krewski
Keyword(s):  
Nonlinear Regression ◽  
Count Data ◽  
Regression Models ◽  
Nonlinear Regression Models

Homogeneity diagnostics for skew-normal nonlinear regression models

2009 ◽  
Vol 79 (6) ◽  
pp. 821-827 ◽  
Author(s):  
Feng-Chang Xie ◽  
Bo-Cheng Wei ◽  
Jin-Guan Lin
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Nonlinear Regression Models ◽  
Skew Normal
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