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On the Nonidentifiability of Population Sizes
Author(s) -
Mao Chang Xuan
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01078.x
Subject(s) - biometrics , population , mixing (physics) , subfamily , population size , statistics , nonparametric statistics , mixture model , mathematics , construct (python library) , econometrics , computer science , artificial intelligence , biology , demography , physics , genetics , quantum mechanics , sociology , gene , programming language
Summary When a nonparametric mixture model is adopted to deal with the heterogeneity among individual capture probabilities, the population size is nonidentifiable (Link, 2003, Biometrics 59 , 1123–1130). Holzmann, Munk, and Zucchini (2006, Biometrics 62, 934–936) discussed the conditions under which a subfamily of mixing distributions is identifiable. Link (2006, Biometrics 92 , 936–939) found that the nonidentifiability occurs across identifiable subfamilies. It is shown that there is a subfamily in which each mixing distribution is determined by its mixture, and the population size admits estimable lower bounds that can be used to construct lower confidence limits.