LOCAL LINEAR ESTIMATOR IN BI-RESPONSE SEMIPARAMETRIC REGRESSION MODEL FOR ESTIMATING MEDIAN GROWTH CHARTS OF CHILDREN

2016 ◽  
Vol 99 (8) ◽  
pp. 1233-1244 ◽  
Author(s):  
N. Chamidah ◽  
M. Rifada
Keyword(s):  
Regression Model ◽  
Semiparametric Regression ◽  
Linear Estimator ◽  
Growth Charts ◽  
Semiparametric Regression Model ◽  
Local Linear ◽  
Local Linear Estimator

scholarly journals Bayesian bandwidth estimation for local linear fitting in nonparametric regression models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Han Lin Shang ◽  
Xibin Zhang
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Regression Function ◽  
Linear Estimator ◽  
Bandwidth Estimation ◽  
Nonparametric Regression Model ◽  
Mixture Density ◽  
Local Linear ◽  
Local Linear Estimator ◽  
Error Density

AbstractThis paper presents a Bayesian sampling approach to bandwidth estimation for the local linear estimator of the regression function in a nonparametric regression model. In the Bayesian sampling approach, the error density is approximated by a location-mixture density of Gaussian densities with means the individual errors and variance a constant parameter. This mixture density has the form of a kernel density estimator of errors and is referred to as the kernel-form error density (c.f. Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34.). While (Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34) use the local constant (also known as the Nadaraya-Watson) estimator to estimate the regression function, we extend this to the local linear estimator, which produces more accurate estimation. The proposed investigation is motivated by the lack of data-driven methods for simultaneously choosing bandwidths in the local linear estimator of the regression function and kernel-form error density. Treating bandwidths as parameters, we derive an approximate (pseudo) likelihood and a posterior. A simulation study shows that the proposed bandwidth estimation outperforms the rule-of-thumb and cross-validation methods under the criterion of integrated squared errors. The proposed bandwidth estimation method is validated through a nonparametric regression model involving firm ownership concentration, and a model involving state-price density estimation.

The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data

2021 ◽  
Author(s):  
Elton G. Aráujo ◽  
Julio C. S. Vasconcelos ◽  
Denize P. dos Santos ◽  
Edwin M. M. Ortega ◽  
Dalton de Souza ◽  
...  
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Semiparametric Regression ◽  
Model Application ◽  
Semiparametric Regression Model
Sign in / Sign up

Export Citation Format

Share Document