M-test in linear models with negatively superadditive dependent errors

This paper is concerned with the testing hypotheses of regression parameters in linear models in which errors are negatively superadditive dependent (NSD). A robust M-test base on M-criterion is proposed. The asymptotic distribution of the test statistic is obtained and the consistent estimates of t...

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Detalles Bibliográficos
Autores principales: Yu, Yuncai, Hu, Hongchang, Liu, Ling, Huang, Shouyou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5610259/
https://www.ncbi.nlm.nih.gov/pubmed/29026280
http://dx.doi.org/10.1186/s13660-017-1509-6
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author Yu, Yuncai
Hu, Hongchang
Liu, Ling
Huang, Shouyou
author_facet Yu, Yuncai
Hu, Hongchang
Liu, Ling
Huang, Shouyou
author_sort Yu, Yuncai
collection PubMed
description This paper is concerned with the testing hypotheses of regression parameters in linear models in which errors are negatively superadditive dependent (NSD). A robust M-test base on M-criterion is proposed. The asymptotic distribution of the test statistic is obtained and the consistent estimates of the redundancy parameters involved in the asymptotic distribution are established. Finally, some Monte Carlo simulations are given to substantiate the stability of the parameter estimates and the power of the test, for various choices of M-methods, explanatory variables and different sample sizes.
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spelling pubmed-56102592017-10-10 M-test in linear models with negatively superadditive dependent errors Yu, Yuncai Hu, Hongchang Liu, Ling Huang, Shouyou J Inequal Appl Research This paper is concerned with the testing hypotheses of regression parameters in linear models in which errors are negatively superadditive dependent (NSD). A robust M-test base on M-criterion is proposed. The asymptotic distribution of the test statistic is obtained and the consistent estimates of the redundancy parameters involved in the asymptotic distribution are established. Finally, some Monte Carlo simulations are given to substantiate the stability of the parameter estimates and the power of the test, for various choices of M-methods, explanatory variables and different sample sizes. Springer International Publishing 2017-09-22 2017 /pmc/articles/PMC5610259/ /pubmed/29026280 http://dx.doi.org/10.1186/s13660-017-1509-6 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Yu, Yuncai
Hu, Hongchang
Liu, Ling
Huang, Shouyou
M-test in linear models with negatively superadditive dependent errors
title M-test in linear models with negatively superadditive dependent errors
title_full M-test in linear models with negatively superadditive dependent errors
title_fullStr M-test in linear models with negatively superadditive dependent errors
title_full_unstemmed M-test in linear models with negatively superadditive dependent errors
title_short M-test in linear models with negatively superadditive dependent errors
title_sort m-test in linear models with negatively superadditive dependent errors
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5610259/
https://www.ncbi.nlm.nih.gov/pubmed/29026280
http://dx.doi.org/10.1186/s13660-017-1509-6
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AT huhongchang mtestinlinearmodelswithnegativelysuperadditivedependenterrors
AT liuling mtestinlinearmodelswithnegativelysuperadditivedependenterrors
AT huangshouyou mtestinlinearmodelswithnegativelysuperadditivedependenterrors

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