Fitting Mixtures of Erlangs to Censored and Truncated Data Using the EM Algorithm

ASTIN Bulletin, 2015, 45(3), 729-758.

28 Pages Posted: 21 Jan 2014 Last revised: 17 May 2017

See all articles by Verbelen Roel

Verbelen Roel

KU Leuven

Gong Lan

University of Toronto

Katrien Antonio

KU Leuven; University of Amsterdam

Andrei Badescu

University of Toronto - Department of Statistics

X. Sheldon Lin

Department of Statistical Sciences, University of Toronto

Date Written: January 24, 2014

Abstract

We present a calibration procedure for fitting mixtures of Erlangs to censored and truncated data by iteratively using the EM algorithm. Mixtures of Erlangs form a very versatile, yet analytically tractable, class of distributions making them suitable for modeling purposes.

The effectiveness of the proposed algorithm is demonstrated on simulated data as well as real data sets.

Keywords: Mixture of Erlang distributions with a common scale parameter; Censoring; Truncation; Expectation-maximization algorithm; Maximum likelihood

Suggested Citation

Roel, Verbelen and Lan, Gong and Antonio, Katrien and Badescu, Andrei and Lin, Xiaodong Sheldon, Fitting Mixtures of Erlangs to Censored and Truncated Data Using the EM Algorithm (January 24, 2014). ASTIN Bulletin, 2015, 45(3), 729-758.. Available at SSRN: https://ssrn.com/abstract=2381838 or http://dx.doi.org/10.2139/ssrn.2381838

Verbelen Roel

KU Leuven ( email )

Naamsestraat 69
B-3000 Leuven, Vlaams-Brabant 3000
Belgium

Gong Lan

University of Toronto ( email )

Toronto, Ontario M5S 3G8
Canada

University of Amsterdam ( email )

Roetersstraat 11
Amsterdam, 1018 WB
Netherlands

Andrei Badescu

University of Toronto - Department of Statistics ( email )

100 St. George St.
Toronto, Ontario M5S 3G3
Canada

Xiaodong Sheldon Lin

Department of Statistical Sciences, University of Toronto ( email )

Department of Statistical Sciences
100 St George Street
Toronto, Ontario M5S 3G3
Canada

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