A universal robust method for analysing bivariate continuous and proportion data

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105699

Title:	A universal robust method for analysing bivariate continuous and proportion data
Authors:	鄒宗山;Tsou, Tsung-Shan
Contributors:	理學院統計研究所
Keywords:	AACR;Computation;Computer simulation;Drought;Droughts;Hinges;Hydrology;Inference;Mathematical models;model misspecification;negative binomial model;Probability distribution;robust likelihood;Simulation;Variables
Date:	2015-01-01
Issue Date:	2026-04-23 12:48:22 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Abingdon: Taylor & Francis
Abstract:	摘要： Traditionally, analysis of Hydrology employs only one hydrological variable. Recently, Nadarajah [A bivariate distribution with gamma and beta marginals with application to drought data. J Appl Stat. 2009;36:277-301] proposed a bivariate model with gamma and beta as marginal distributions to analyse the drought duration and the proportion of drought events. However, the validity of this method hinges on fulfilment of stringent assumptions. We propose a robust likelihood approach which can be used to make inference for general bivariate continuous and proportion data. Unlike the gamma-beta (GB) model which is sensitive to model misspecification, the new method provides legitimate inference without knowing the true underlying distribution of the bivariate data. Simulations and the analysis of the drought data from the State of Nebraska, USA, are provided to make contrasts between this robust approach and the GB model. 出版者： Abingdon: Taylor & Francis 出版日期： 2015-12-12 出處： Journal of statistical computation and simulation, 2015-12, Vol.85 (18), p.3700-3707 版權： 2015 Taylor & Francis 2015 版權： Copyright Taylor & Francis Ltd. 2015 識別號： ISSN: 0094-9655 識別號： EISSN: 1563-5163 識別號： DOI: 10.1080/00949655.2014.996151
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	11	View/Open

社群 sharing

Loading...