Logistic Regression in Capture-Recapture Models

Alho, Juha M.

doi:10.2307/2532083

Cited by 190 publications

(171 citation statements)

References 17 publications

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“…Maximum likelihood estimation where the heterogeneity is modeled as a ®nite mixture distribution (usually with two or three support points) (Norris and Pollock, 1996;Pledger, 2000) is a recent development. Another approach to modeling heterogeneity uses covariates (Huggins, 1989;Alho, 1990). The original approach to model selection for this series of models in Otis et al (1978) does not work well.…”

Section: Capture±recapture Modelsmentioning

confidence: 99%

Large scale wildlife monitoring studies: statistical methods for design and analysis

et al. 2002

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SUMMARYTechniques for estimation of absolute abundance of wildlife populations have received a lot of attention in recent years. The statistical research has been focused on intensive small-scale studies. Recently, however, wildlife biologists have desired to study populations of animals at very large scales for monitoring purposes. Population indices are widely used in these extensive monitoring programs because they are inexpensive compared to estimates of absolute abundance. A crucial underlying assumption is that the population index (C) is directly proportional to the population density (D). The proportionality constant, , is simply the probability of`detection' for animals in the survey. As spatial and temporal comparisons of indices are crucial, it is necessary to also assume that the probability of detection is constant over space and time. Biologists intuitively recognize this when they design rigid protocols for the studies where the indices are collected. Unfortunately, however, in many ®eld studies the assumption is clearly invalid. We believe that the estimation of detection probability should be built into the monitoring design through a double sampling approach. A large sample of points provides an abundance index, and a smaller sub-sample of the same points is used to estimate detection probability. There is an important need for statistical research on the design and analysis of these complex studies. Some basic concepts based on actual avian, amphibian, and ®sh monitoring studies are presented in this article.

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Section: Capture±recapture Modelsmentioning

confidence: 99%

Large scale wildlife monitoring studies: statistical methods for design and analysis

et al. 2002

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“…These covariates can be easily incorporated in log-linear or multinomial logit models. Accounting for observable heterogeneity has been shown to minimize the bias of the estimate of the population size (see Alho, 1990). In this presentation we evaluate the usefulness of partly overlapping covariates in the multiple system estimator.…”

Section: Introductionmentioning

confidence: 99%

The Use of Score Tests for Inference on Variance Components

Verbeke¹,

Molenberghs²

2003

Biometrics

203

144

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Summary Whenever inference for variance components is required, the choice between one‐sided and two‐sided tests is crucial. This choice is usually driven by whether or not negative variance components are permitted. For two‐sided tests, classical inferential procedures can be followed, based on likelihood ratios, score statistics, or Wald statistics. For one‐sided tests, however, one‐sided test statistics need to be developed, and their null distribution derived. While this has received considerable attention in the context of the likelihood ratio test, there appears to be much confusion about the related problem for the score test. The aim of this paper is to illustrate that classical (two‐sided) score test statistics, frequently advocated in practice, cannot be used in this context, but that well‐chosen one‐sided counterparts could be used instead. The relation with likelihood ratio tests will be established, and all results are illustrated in an analysis of continuous longitudinal data using linear mixed models.

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“…This option is possible if there is an observed categorical covariate that is closely related to the probability of being in each of the lists. Later Alho (1990) and Huggins (1991) generalized this approach by proposing a model where the capture probabilities of each list are a function of observed covariates that can be categorical or continuous. These two strategies have in common that they allow for observed heterogeneity, i.e., the heterogeneity of capture probabilities can be taken into account by observed covariates.…”

Section: Introductionmentioning

confidence: 99%

A Multiple‐Record Systems Estimation Method that Takes Observed and Unobserved Heterogeneity into Account

Stanghellini¹,

Heijden²

2004

Biometrics

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Summary. We present a model to estimate the size of an unknown population from a number of lists that applies when the assumptions of (a) homogeneity of capture probabilities of individuals and (b) marginal independence of lists are violated. This situation typically occurs in epidemiological studies, where the heterogeneity of individuals is severe and researchers cannot control the independence between sources of ascertainment. We discuss the situation when categorical covariates are available and the interest is not only in the total undercount, but also in the undercount within each stratum resulting from the crossclassification of the covariates. We also present several techniques for determining confidence intervals of the undercount within each stratum using the profile log likelihood, thereby extending the work of Cormack (1992, Biometrics 48, 567-576).

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Logistic Regression in Capture-Recapture Models

Cited by 190 publications

References 17 publications

Large scale wildlife monitoring studies: statistical methods for design and analysis

Large scale wildlife monitoring studies: statistical methods for design and analysis

The Use of Score Tests for Inference on Variance Components

A Multiple‐Record Systems Estimation Method that Takes Observed and Unobserved Heterogeneity into Account

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