Robust likelihood inference for multivariate correlated count data

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105893

Title:	Robust likelihood inference for multivariate correlated count data
Authors:	鄒宗山;Tsou, Tsung-Shan
Contributors:	理學院統計研究所
Keywords:	Asymptotic properties;Biomedical research;Computer simulation;Construction;Convulsions & seizures;Correlation;Counting;Data analysis;Data processing;Economic Theory/Quantitative Economics/Mathematical Methods;Expected values;Hypothesis testing;Mathematical models;Mathematics;Mathematics and Statistics;Original Paper;Probability and Statistics in Computer Science;Probability Theory and Stochastic Processes;Random variables;Regression;Statistics;Studies
Date:	2016-09-01
Issue Date:	2026-04-23 13:00:10 (UTC+8)
Publisher:	Springer Verlag;Berlin/Heidelberg: Springer Berlin Heidelberg
Abstract:	摘要： A parametric robust approach for analyzing correlated count data is introduced. This method enables one to construct an asymptotically valid likelihood for the regression parameter when knowledge about the joint distribution for data is scarce or not available. We use simulations and real data analysis to demonstrate the merit of the proposed robust likelihood method. 其他題名： Comput Stat 出版者： Berlin/Heidelberg: Springer Berlin Heidelberg 出版日期： 2016-09 出處： Computational statistics, 2016-09, Vol.31 (3), p.845-857 資源來源： ABI/INFORM Collection 版權： Springer-Verlag Berlin Heidelberg 2015 版權： Springer-Verlag Berlin Heidelberg 2016 識別號： ISSN: 0943-4062 識別號： EISSN: 1613-9658 識別號： DOI: 10.1007/s00180-015-0589-3
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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