Parametric simultaneous robust inferences for regression coefficient under generalized linear models

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105869

Title:	Parametric simultaneous robust inferences for regression coefficient under generalized linear models
Authors:	鄒宗山;Chien, Li-Chu;Tsou, Tsung-Shan
Contributors:	理學院統計研究所
Keywords:	Adjustment;Asymptotic methods;Asymptotic properties;Computer simulation;Generalized linear models;Mathematical analysis;Mathematical models;Probability distribution;Regression;Regression analysis;Regression coefficients;robust gamma regression;robust normal regression;Samples
Date:	2014-01-01
Issue Date:	2026-04-23 12:58:26 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Abingdon: Taylor & Francis
Abstract:	摘要： In this article, the parametric robust regression approaches are proposed for making inferences about regression parameters in the setting of generalized linear models (GLMs). The proposed methods are able to test hypotheses on the regression coefficients in the misspecified GLMs. More specifically, it is demonstrated that with large samples, the normal and gamma regression models can be properly adjusted to become asymptotically valid for inferences about regression parameters under model misspecification. These adjusted regression models can provide the correct type I and II error probabilities and the correct coverage probability for continuous data, as long as the true underlying distributions have finite second moments. 出版者： Abingdon: Taylor & Francis 出版日期： 2014-04-03 出處： Journal of statistical computation and simulation, 2014-04, Vol.84 (4), p.850-867 版權： 2012 Taylor & Francis 2012 版權： Copyright Taylor & Francis Ltd. 2014 識別號： ISSN: 0094-9655 識別號： EISSN: 1563-5163 識別號： DOI: 10.1080/00949655.2012.731409
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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