Journal ArticleDOI
Bayesian methods for estimation of the size of a closed population
David Madigan,Jeremy C. York +1 more
TLDR
In this paper, a Bayesian methodology for estimating the size of a closed population from multiple incomplete administrative lists is proposed, which allows for a variety of dependence structures between the lists, can make use of covariates, and explicitly accounts for model uncertainty.Abstract:
SUMMARY A Bayesian methodology for estimating the size of a closed population from multiple incomplete administrative lists is proposed. The approach allows for a variety of dependence structures between the lists, can make use of covariates, and explicitly accounts for model uncertainty. Interval estimates from this approach are compared to frequentist and previously published Bayesian approaches. Several examples are considered.read more
Citations
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Journal ArticleDOI
Estimating Animal Abundance: Review III
Carl J. Schwarz,G. A. F. Seber +1 more
TL;DR: The literature describing methods for estimating animal abundance and related parameters continues to grow as mentioned in this paper, and recent developments in the subject over the past seven years and updates two previous reviews are reviewed in this paper.
Journal ArticleDOI
Rcapture: Loglinear Models for Capture-Recapture in R
TL;DR: Rcapture as discussed by the authors is an R package for capture-recapture experiments, which can fit three types of models: Cormack-Jolly-Seber, open population and robust design models.
Journal ArticleDOI
Bayesian inference for categorical data analysis
Alan Agresti,David B. Hitchcock +1 more
TL;DR: Bayesian methods for categorical data analysis, with primary emphasis on contingency table analysis, is surveyed, with main emphasis on generalized linear models such as logistic regression for binary and multi-category response variables.
Journal ArticleDOI
Bayesian Animal Survival Estimation
TL;DR: The Bayesian procedures are shown to be straightforward and provide a convenient framework for model-averaging, which incorporates the uncertainty due to model selection into the inference process.
References
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Book
Algorithmic graph theory and perfect graphs
TL;DR: This new Annals edition continues to convey the message that intersection graph models are a necessary and important tool for solving real-world problems and remains a stepping stone from which the reader may embark on one of many fascinating research trails.
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Mathematica: a system for doing mathematics by computer (2nd ed.)
TL;DR: This new edition maintains the format of the original book and is the single most important user guide and reference for Mathematica--all users ofMathematica will need this edition.
Journal ArticleDOI
Accurate Approximations for Posterior Moments and Marginal Densities
Luke Tierney,Joseph B. Kadane +1 more
TL;DR: These approximations to the posterior means and variances of positive functions of a real or vector-valued parameter, and to the marginal posterior densities of arbitrary parameters can also be used to compute approximate predictive densities.
Journal ArticleDOI
A universal prior for integers and estimation by minimum description length
TL;DR: In this article, the minimum description length (MDL) criterion is used to estimate the total number of binary digits required to rewrite the observed data, when each observation is given with some precision.