Mixed spline smoothing and kernel estimator in biresponse nonparametric regression
type: Item
instance of: scholarly article
title: Mixed spline smoothing and kernel estimator in biresponse nonparametric regression
zbMATH Open document ID: 1486.62120
DOI: 10.1155/2021/6611084
author: Dyah P. Rahmawati
author: I. N. Budiantara
author: Dedy D. Prastyo
author: Made A. D. Octavanny
published in: International Journal of Mathematics and Mathematical Sciences
publication date: 2021-06-17T00:00:00Z
review text: Summary: Mixed estimators in nonparametric regression have been developed in models with one response. The biresponse cases with different patterns among predictor variables that tend to be mixed estimators are often encountered. Therefore, in this article, we propose a biresponse nonparametric regression model with mixed spline smoothing and kernel estimators. This mixed estimator is suitable for modeling biresponse data with several patterns (response vs. predictors) that tend to change at certain subintervals such as the spline smoothing pattern, and other patterns that tend to be random are commonly modeled using kernel regression. The mixed estimator is obtained through two-stage estimation, i.e., penalized weighted least square (PWLS) and weighted least square (WLS). Furthermore, the proposed biresponse modeling with mixed estimators is validated using simulation data. This estimator is also applied to the percentage of the poor population and human development index data. The results show that the proposed model can be appropriately implemented and gives satisfactory results.
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 7360735
zbMATH Keywords: mixed estimators
zbMATH Keywords: nonparametric regression
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2021/6611084
OpenAlex ID: W3136892137
cites work: Q3999325
cites work: Q4252680
cites work: Using Penalized Splines to Model Age‐ and Season‐of‐Birth‐Dependent Effects of Childhood Mortality Risk Factors in Rural Burkina Faso
cites work: Q4488841
cites work: Nonparametric kernel regression subject to monotonicity constraints
cites work: Non-parametric kernel regression for multinomial data
cites work: Nonparametic kernel regression with multiple predictors and multiple shape constraints
cites work: Nonparametric density estimation based on beta prime kernel
cites work: Fourier smoother and additive models
cites work: Fourier series approximation of separable models
cites work: The regression curve estimation by using mixed smoothing spline and kernel (MsS-K) model
cites work: Combination estimation of smoothing spline and Fourier series in nonparametric regression
cites work: Nonparametric regression model for longitudinal data with mixed truncated spline and Fourier series
cites work: New Ridge Regression Estimator in Semiparametric Regression Models
cites work: Efficiency of the QR class estimator in semiparametric regression models to combat multicollinearity
cites work: Efficiency of the generalized-difference-based weighted mixed almost unbiased two-parameter estimator in partially linear model
cites work: Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
cites work: Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
cites work: Q3996207
cites work: Optimal partial ridge estimation in restricted semiparametric regression models
cites work: Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion
cites work: Ridge estimation in semi-parametric regression models under the stochastic restriction and correlated elliptically contoured errors
description: scientific article; zbMATH DE number 7360735