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DTSTAMP:20260416T003021Z
DESCRIPTION:Title: (Prix SSC) Full likelihood inference for abundance from 
 capture-recapture data: semiparametric efficiency and EM-algorithm.\n\nAbs
 tract: Capture-recapture experiments are widely used to collect data neede
 d to estimate the abundance of a closed population. To account for heterog
 eneity in the capture probabilities\, Huggins (1989) and Alho (1990) propo
 sed a semiparametric model in which the capture probabilities are modelled
  parametrically and the distribution of individual characteristics is left
  unspecified. A conditional likelihood method was then proposed to obtain 
 point estimates and Wald-type confidence intervals for the abundance. Empi
 rical studies show that the small-sample distribution of the maximum condi
 tional likelihood estimator is strongly skewed to the right\, which may pr
 oduce Wald-type confidence intervals with lower limits that are less than 
 the number of captured individuals or even negative.  \n	\n	In this talk\, w
 e present a full likelihood approach based on Huggins and Alho's model. We
  show that the null distribution of the empirical likelihood ratio for the
  abundance is asymptotically chi-square with one degree of freedom\, and t
 he maximum empirical likelihood estimator achieves semiparametric efficien
 cy. We further propose an expectation–maximization algorithm to numericall
 y calculate the proposed point estimate and empirical likelihood ratio fun
 ction. Simulation studies show that the empirical-likelihood-based method 
 is superior to the conditional-likelihood-based method: its confidence int
 erval has much better coverage\, and the maximum empirical likelihood esti
 mator has a smaller mean square error.\n\n \n\nColloquium Colloque des sci
 ences mathématiques du Québec\n	To get your Zoom access\, please subscribe 
 to the lists of your choice: https://forms.gle/axqFGSkRkbkdFtE68\n\nhttp:/
 /crm.umontreal.ca/colloque-sciences-mathematiques-quebec/index.htm...\n
DTSTART:20220930T193000Z
DTEND:20220930T203000Z
SUMMARY:Pengfei Li\, University of Waterloo
URL:https://www.mcgill.ca/mathstat/channels/event/pengfei-li-university-wat
 erloo-341785
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