Variational Bayes for Mixture Models with Censored Data

Kohjima, Masahiro; Matsubayashi, Tatsushi; Toda, Hiroyuki

doi:10.1007/978-3-030-10928-8_36

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The crime data was recently analyzed in [29] using variational Bayes estimated finite mixture models. For clustering we used all n = 432 observations and the following seven attributes: (1) financial aid (no, yes), (2) full-time work experience before incarceration (no, yes), (3) marital status at time of release (married, not married), (4) released on parole (no, yes), ( 5) number of convictions prior to current incarceration, (6) age in years at time of release and (7) week of first arrest after release (73.6% censored).…”

Section: Clustering Survival Datamentioning

confidence: 99%

Minimum Message Length Inference of the Exponential Distribution with Type I Censoring

Makalic

Schmidt

2021

Entropy

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Data with censoring is common in many areas of science and the associated statistical models are generally estimated with the method of maximum likelihood combined with a model selection criterion such as Akaike’s information criterion. This manuscript demonstrates how the information theoretic minimum message length principle can be used to estimate statistical models in the presence of type I random and fixed censoring data. The exponential distribution with fixed and random censoring is used as an example to demonstrate the process where we observe that the minimum message length estimate of mean survival time has some advantages over the standard maximum likelihood estimate.

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Section: Clustering Survival Datamentioning

confidence: 99%