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Simplified Maximum Likelihood Inference Based on the Likelihood Decomposition for Missing Data
Author(s) -
Jung Sangah,
Park Sangun
Publication year - 2013
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12040
Subject(s) - missing data , mathematics , estimator , inference , weighting , statistics , maximum likelihood , maximum likelihood sequence estimation , restricted maximum likelihood , fisher information , expectation–maximization algorithm , monotone polygon , sample (material) , computer science , artificial intelligence , medicine , chemistry , geometry , chromatography , radiology
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.