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Nonparametric adaptive likelihood weights
Author(s) -
Plante JeanFrançois
Publication year - 2008
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550360308
Subject(s) - inference , mathematics , nonparametric statistics , maximization , statistics , convergence (economics) , expectation–maximization algorithm , population , entropy (arrow of time) , maximum likelihood , computer science , mathematical optimization , artificial intelligence , demography , sociology , physics , quantum mechanics , economics , economic growth
The weighted likelihood can be used to make inference about one population when data from similar populations are available. The author shows heuristically that the weighted likelihood can be seen as a special case of the entropy maximization principle. This leads him to propose the minimum averaged mean squared error (MAMSE) weights. He describes an algorithm for calculating these weights and shows its convergence using the Kuhn‐Tucker conditions. He explores the performance and properties of the weighted likelihood based on MAMSE weights through simulations.