Simplified maximum likelihood inference based on the likelihood decomposition for missing data

Sangah Jung; Sangun Park

doi:10.1111/anzs.12040

Simplified maximum likelihood inference based on the likelihood decomposition for missing data

Sangah Jung, Sangun Park

Department of Applied Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

Summary: In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non-monotone missing data. Numerical examples are presented to illustrate this method.

Original language	English
Pages (from-to)	271-283
Number of pages	13
Journal	Australian and New Zealand Journal of Statistics
Volume	55
Issue number	3
DOIs	https://doi.org/10.1111/anzs.12040
Publication status	Published - 2013 Sep

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1111/anzs.12040

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abstract = "Summary: In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non-monotone missing data. Numerical examples are presented to illustrate this method.",

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AB - Summary: In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non-monotone missing data. Numerical examples are presented to illustrate this method.

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Simplified maximum likelihood inference based on the likelihood decomposition for missing data

Abstract

All Science Journal Classification (ASJC) codes

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