Robust likelihood inferences about regression parameters for general bivariate continuous data

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51838

Title:	Robust likelihood inferences about regression parameters for general bivariate continuous data
Authors:	Tsou,TS
Contributors:	統計研究所
Date:	2010
Issue Date:	2012-03-27 19:07:23 (UTC+8)
Publisher:	國立中央大學
Abstract:	This paper introduces a way of modifying the bivariate normal likelihood function. One can use the adjusted likelihood to generate valid likelihood inferences for the regression parameter of interest, even if the bivariate normal assumption is fallacious. The retained asymptotic legitimacy requires no knowledge of the true underlying joint distributions so long as their second moments exist. The extension to the multivariate situations is straightforward in theory and yet appears to be arduous computationally. Nevertheless, it is illustrated that the implementation of this seemingly sophisticated procedure is almost effortless needing only outputs from existing statistical software. The efficacy of the proposed parametric approach is demonstrated via simulations.
Relation:	METRIKA
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	419	View/Open

社群 sharing