Robust testing for superiority between two regression curves

The problem of testing the null hypothesis that the regression functions of two populations are equal versus one-sided alternatives under a general nonparametric homoscedastic regression model is considered. To protect against atypical observations, the test statistic is based on the residuals obtai...

Descripción completa

Detalles Bibliográficos
Autor principal: Boente, Graciela Lina
Publicado: 2016
Materias:
Hypothesis testing
Nonparametric regression models
Robust inference
Smoothing techniques
Sampling
Sensitivity analysis
Statistical tests
Statistics
Asymptotic distributions
Non-parametric regression
Regression curve
Regression function
Regression model
Regression analysis
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v97_n_p151_Boente
http://hdl.handle.net/20.500.12110/paper_01679473_v97_n_p151_Boente
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_01679473_v97_n_p151_Boente
record_format dspace
spelling paper:paper_01679473_v97_n_p151_Boente2023-06-08T15:17:11Z Robust testing for superiority between two regression curves Boente, Graciela Lina Hypothesis testing Nonparametric regression models Robust inference Smoothing techniques Sampling Sensitivity analysis Statistical tests Statistics Asymptotic distributions Hypothesis testing Non-parametric regression Regression curve Regression function Regression model Robust inference Smoothing techniques Regression analysis The problem of testing the null hypothesis that the regression functions of two populations are equal versus one-sided alternatives under a general nonparametric homoscedastic regression model is considered. To protect against atypical observations, the test statistic is based on the residuals obtained by using a robust estimate for the regression function under the null hypothesis. The asymptotic distribution of the test statistic is studied under the null hypothesis and under root-n local alternatives. A Monte Carlo study is performed to compare the finite sample behaviour of the proposed tests with the classical one obtained using local averages. A sensitivity analysis is carried on a real data set. © 2015 Elsevier B.V. Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v97_n_p151_Boente http://hdl.handle.net/20.500.12110/paper_01679473_v97_n_p151_Boente
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Hypothesis testing
Nonparametric regression models
Robust inference
Smoothing techniques
Sampling
Sensitivity analysis
Statistical tests
Statistics
Asymptotic distributions
Hypothesis testing
Non-parametric regression
Regression curve
Regression function
Regression model
Robust inference
Smoothing techniques
Regression analysis
spellingShingle Hypothesis testing
Nonparametric regression models
Robust inference
Smoothing techniques
Sampling
Sensitivity analysis
Statistical tests
Statistics
Asymptotic distributions
Hypothesis testing
Non-parametric regression
Regression curve
Regression function
Regression model
Robust inference
Smoothing techniques
Regression analysis
Boente, Graciela Lina
Robust testing for superiority between two regression curves
topic_facet Hypothesis testing
Nonparametric regression models
Robust inference
Smoothing techniques
Sampling
Sensitivity analysis
Statistical tests
Statistics
Asymptotic distributions
Hypothesis testing
Non-parametric regression
Regression curve
Regression function
Regression model
Robust inference
Smoothing techniques
Regression analysis
description The problem of testing the null hypothesis that the regression functions of two populations are equal versus one-sided alternatives under a general nonparametric homoscedastic regression model is considered. To protect against atypical observations, the test statistic is based on the residuals obtained by using a robust estimate for the regression function under the null hypothesis. The asymptotic distribution of the test statistic is studied under the null hypothesis and under root-n local alternatives. A Monte Carlo study is performed to compare the finite sample behaviour of the proposed tests with the classical one obtained using local averages. A sensitivity analysis is carried on a real data set. © 2015 Elsevier B.V.
author Boente, Graciela Lina
author_facet Boente, Graciela Lina
author_sort Boente, Graciela Lina
title Robust testing for superiority between two regression curves
title_short Robust testing for superiority between two regression curves
title_full Robust testing for superiority between two regression curves
title_fullStr Robust testing for superiority between two regression curves
title_full_unstemmed Robust testing for superiority between two regression curves
title_sort robust testing for superiority between two regression curves
publishDate 2016
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v97_n_p151_Boente
http://hdl.handle.net/20.500.12110/paper_01679473_v97_n_p151_Boente
work_keys_str_mv AT boentegracielalina robusttestingforsuperioritybetweentworegressioncurves
_version_ 1768543320274894848

Ejemplares similares