On the existence and uniqueness of the maximum likelihood estimates of the parameters of Birnbaum–Saunders distribution based on Type-I, Type-II and hybrid censored samples

DOI10.1080/02331888.2013.800069zbMath1367.62051OpenAlexW2078033640MaRDI QIDQ2934841

Xiaojun Zhu, Narayanaswamy Balakrishnan

Publication date: 22 December 2014

Published in: Unnamed Author (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2013.800069

zbMATH Keywords

existenceuniquenessmaximum likelihood estimateBirnbaum-Saunders distributiontype-II censoringtype-I censoringhybrid censoring

Mathematics Subject Classification ID

Point estimation (62F10) Censored data models (62N01)

Related Items (13)

An exact test for exponentiality against renewal increasing mean residual life class ⋮ Maximum likelihood estimation of the parameters of student’s t Birnbaum-Saunders distribution: a comparative study ⋮ Maximum likelihood estimation of the parameters of a multiple step-stress model from the Birnbaum-Saunders distribution under time-constraint: A comparative study ⋮ Birnbaum-Saunders distribution based on Laplace kernel and some properties and inferential issues ⋮ Reliability analysis of Birnbaum–Saunders model based on progressive type-II censoring ⋮ Fast Bayesian inference for Birnbaum-Saunders distribution ⋮ Birnbaum‐Saunders distribution: A review of models, analysis, and applications ⋮ Discussion of Birnbaum‐Saunders distribution: A review of models, analysis, and applications ⋮ Bayesian analysis of Birnbaum-Saunders distribution via the generalized ratio-of-uniforms method ⋮ A stochastic expectation-maximization algorithm for the analysis of system lifetime data with known signature ⋮ An improved method of estimation for the parameters of the Birnbaum–Saunders distribution ⋮ On the existence and uniqueness of the maximum likelihood estimates of parameters of Laplace Birnbaum-Saunders distribution based on type-I, type-II and hybrid censored samples ⋮ Inference for the Birnbaum–Saunders Lifetime Regression Model with Applications

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