JSM 2004 - Toronto

Abstract #300585

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Activity Number: 271
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #300585
Title: Inference on Three-parameter Lognormal Distribution Based on Progressively Censored Data
Author(s): Indrani Basak*+ and Prasanta Basak and Narayanaswamy Balakrishnan
Companies: Pennsylvania State University, Altoona and Pennsylvania State University, Altoona and McMaster University
Address: 151 CLRC, Altoona, PA, 16601,
Keywords: EM algorithm ; progressive censoring ; maximum likelihood estimators ; asymptotic distribution ; missing information principle
Abstract:

Some work has been done in the past on the three-parameter lognormal distribution estimation based on complete and censored samples. We develop the inferential methods based on Type II progressively censored samples from a three-parameter lognormal distribution. In particular, we use EM algorithm as well as some other numerical methods to determine maximum likelihood (ML) estimates of parameters. We also consider the interval estimation based on large-sample theory and determine the actual probability coverages in case of small samples. The asymptotic variances and covariances of the ML estimates are computed by using missing information principle. The methodology developed is then illustrated using a numerical example.

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