Inference for Log-Gamma Distribution Based on Progressively Type-II Censored Data

DOI10.1080/03610920600692789zbMath1105.62025OpenAlexW2158741947MaRDI QIDQ3424158

Sam J. S. Wu, Chien-Tai Lin, Narayanaswamy Balakrishnan

Publication date: 15 February 2007

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920600692789

zbMATH Keywords

EM algorithmMonte Carlo simulationsNewton-Raphson methodfixed-point iterationFisher informationnormal distributionextreme value distributionmaximum likelihood estimatorspivotal quantitiesapproximate maximum likelihood estimatorsmodified EM algorithmprobability coverages

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Point estimation (62F10) Monte Carlo methods (65C05)

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Cites Work

• Estimation of parameters from progressively censored data using EM algorithm
• Approximate MLEs for the location and scale parameters of the extreme value distribution with censoring
• Tolerance Bounds for Log Gamma Regression Models
• Approximate Inference for the Generalized Gamma Distribution
• Bias Correction for a Generalized Log-Gamma Regression Model
• Computing Maximum-Likelihood Parameter Estimates of the Generalized Gamma Distribution by Numerical Root Isolation
• Rank regression with log gamma residuals
• Inference in the Generalized Gamma and Log Gamma Distributions
• A log gamma model and its maximum likelihood estimation
• A Study of Distributional Shape in Life Testing
• Interval Estimation of Parameters of Life from Progressively Censored Data
• Inference for the extreme value distribution under progressive Type-II censoring
• Log-gamma regression modeling 'through regression trees
• Inferences Based on Censored Sampling from the Weibull or Extreme-Value Distribution
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