Approximate Bayesian computation for finite mixture models

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http://hdl.handle.net/10138/329485

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Simola , U , Cisewski-Kehe , J & Wolpert , R L 2020 , ' Approximate Bayesian computation for finite mixture models ' , Journal of Statistical Computation and Simulation , vol. 91 , no. 6 , pp. 1155-1174 . https://doi.org/10.1080/00949655.2020.1843169

Title: Approximate Bayesian computation for finite mixture models
Author: Simola, Umberto; Cisewski-Kehe, Jessi; Wolpert, Robert L.
Contributor: University of Helsinki, Department of Mathematics and Statistics
Date: 2020-11-06
Language: eng
Number of pages: 20
Belongs to series: Journal of Statistical Computation and Simulation
ISSN: 0094-9655
URI: http://hdl.handle.net/10138/329485
Abstract: Finite mixture models are used in statistics and other disciplines, but inference for mixture models is challenging due, in part, to the multimodality of the likelihood function and the so-called label switching problem. We propose extensions of the Approximate Bayesian Computation?Population Monte Carlo (ABC?PMC) algorithm as an alternative framework for inference on finite mixture models. There are several decisions to make when implementing an ABC?PMC algorithm for finite mixture models, including the selection of the kernels used for moving the particles through the iterations, how to address the label switching problem and the choice of informative summary statistics. Examples are presented to demonstrate the performance of the proposed ABC?PMC algorithm for mixture modelling. The performance of the proposed method is evaluated in a simulation study and for the popular recessional velocity galaxy data.
Subject: 111 Mathematics
112 Statistics and probability
Approximate Bayesian computation
label switching
finite mixture models
perturbation kernels
summary statistics
SEQUENTIAL MONTE-CARLO
DENSITY-ESTIMATION
INFERENCE
DISTRIBUTIONS
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