A New Algorithm for Maximum Likelihood Estimation with Progressive Type-I Interval Censored Data

DOI10.1080/03610911003637406zbMath1192.62061OpenAlexW2159631254MaRDI QIDQ3577201

Conghua Cheng, Zehui Li, Jinyuan Chen

Publication date: 5 August 2010

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610911003637406

zbMATH Keywords

tablesexponential distributionEM algorithmmaximum likelihood estimationWeibull distributionNewton-Raphson algorithmprogressive censoring

Mathematics Subject Classification ID

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

Related Items (7)

Inference on a progressive type I interval-censored truncated normal distribution ⋮ Statistical inference of Marshall-Olkin bivariate Weibull distribution with three shocks based on progressive interval censored data ⋮ Point and interval estimation under progressive type-I interval censoring with random removal ⋮ Estimation procedures for grouped data – a comparative study ⋮ Study of an imputation algorithm for the analysis of interval-censored data ⋮ Statistical analysis of competing risks model from Marshall–Olkin extended Chen distribution under adaptive progressively interval censoring with random removals ⋮ Using BBPSO Algorithm to Estimate the Weibull Parameters with Censored Data

Cites Work

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• Estimation of parameters from progressively censored data using EM algorithm
• Statistical estimation for the parameters of Weibull distribution based on progressively type-I interval censored sample
• Interval Estimation of Parameters of Life from Progressively Censored Data
• PROGRESSIVE INTERVAL CENSORING: SOME MATHEMATICAL RESULTS WITH APPLICATIONS TO INFERENCE
• Progressively Censored Samples in Life Testing
• Best Linear Invariant Estimation for Weibull Parameters under Progressive Censoring
• Linear Order Statistic Estimation for the Two-Parameter Weibull and Extreme-Value Distributions from Type II Progressively Censored Samples

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