Likelihood inference for lognormal data with left truncation and right censoring with an illustration

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51872

Title:	Likelihood inference for lognormal data with left truncation and right censoring with an illustration
Authors:	Balakrishnan,N;Mitra,D
Contributors:	統計研究所
Keywords:	EM ALGORITHM;MODEL
Date:	2011
Issue Date:	2012-03-27 19:08:03 (UTC+8)
Publisher:	國立中央大學
Abstract:	The lognormal distribution is quite commonly used as a lifetime distribution. Data arising from life-testing and reliability studies are often left truncated and right censored. Here, the EM algorithm is used to estimate the parameters of the lognormal model based on left truncated and right censored data. The maximization step of the algorithm is carried out by two alternative methods, with one involving approximation using Taylor series expansion (leading to approximate maximum likelihood estimate) and the other based on the EM gradient algorithm (Lange, 1995). These two methods are compared based on Monte Carlo simulations. The Fisher scoring method for obtaining the maximum likelihood estimates shows a problem of convergence under this setup, except when the truncation percentage is small. The asymptotic variance-covariance matrix of the MLEs is derived by using the missing information principle (Louis, 1982), and then the asymptotic confidence intervals for scale and shape parameters are obtained and compared with corresponding bootstrap confidence intervals. Finally, some numerical examples are given to illustrate all the methods of inference developed here. (C) 2011 Elsevier B.V. All rights reserved.
Relation:	JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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