Fitting parametric frailty and mixture models under biased sampling

DOI10.1080/02664760802382525zbMath1473.62080OpenAlexW2089450915MaRDI QIDQ3183866

Chrys Caroni, Polychronis Economou

Publication date: 21 October 2009

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664760802382525

zbMATH Keywords

EM algorithmfrailtyWeibull distributionBurr distributionbiased samplingfinite mixtureweighted distributiontype-I right censoring

Mathematics Subject Classification ID

Point estimation (62F10) Characterization and structure theory of statistical distributions (62E10) Reliability and life testing (62N05)

Related Items (5)

Characterization properties of the log-normal distribution obtained with the help of divergence measures ⋮ Kullback-Leibler divergence measure based tests concerning the biasness in a sample ⋮ Statistical inference for the size-biased Weibull distribution ⋮ Sample Tests for Detection of Size-Biased Sampling Mechanism ⋮ Hypothesis testing for the population mean and variance based on r-size biased samples

Cites Work

• Using mixtures of Weibull distributions to estimate mixing proportions
• Preservation of classes of life distributions and stochastic orders under weighting
• Parametric Estimation from Weighted Samples
• Life table methods for heterogeneous populations: Distributions describing the heterogeneity
• ESTIMATION OF PARAMETERS OF MIXED EXPONENTIALLY DISTRIBUTED FAILURE TIME DISTRIBUTIONS FROM CENSORED LIFE TEST DATA
• Relations for reliability measures of weighted distributions
• The unobserved heterogeneity distribution in duration analysis
• Econometric Methods for the Duration of Unemployment

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