Heterogeneous Capture-Recapture Models with Covariates: A Partial Likelihood Approach for Closed Populations

Stoklosa, Jakub; Hwang, Wen‐Han; Wu, Sheng-Hai; Huggins, Richard

doi:10.1111/j.1541-0420.2011.01596.x

Cited by 16 publications

(29 citation statements)

References 37 publications

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“…The reason for this is that neither the logistic function H(·) nor the probability P * i (θ) are analytically tractable. An alternative approach is to use an approximated correct score or other models given in Stoklosa et al (2011) to help solve this difficulty. Another extension for the proposed study is to consider more general capture-recapture models where both capture and recapture probabilities are related to temporal and behavioral effects (Huggins, 1989)-e.g., behavioral effects can be included as dummy variables in the form of Z i .…”

Section: Discussionmentioning

confidence: 99%

“…These data have been previously analysed in Huang et al (2011) and Stoklosa et al (2011). Captures of mice were collected across 14 sampling occasions where upon capture, additional measurements, such as body weight, gender, head-to-tail length, etc.…”

Section: Example 1: Harvest Mouse Datamentioning

confidence: 99%

“…When the measurement error variance is constant across individual subjects, a variety of measurement error methods have been developed to address this problem, including: simulationextrapolation (Gould et al, 1999;Stoklosa et al, 2011), regression calibration (Hwang and Huang, 2003), and conditional score (Hwang et al, 2007). However, the measurement error variance of an observed covariate often depends on the capture frequency which is the response variable for modelling capture probabilities.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Closed-population capture--recapture models with measurement error and missing observations in covariates

Stoklosa¹,

Lee²,

Hwang³

2019

STAT SINICA

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In capture-recapture experiments, covariates collected on individuals, such as body weight and length, are often measured imprecisely or are missing at random. Furthermore, the number of recorded covariate measurements collected on each observed individual is usually equal to or less than the individual's capture frequency. Correcting for multiple error-prone covariate is seldom seen in capture-recapture models and even fewer research have considered cases where individual's have no measurements at all. In this paper, we develop an unbiased estimating equation using the conditional score within the capture-recapture framework. We then extend this approach to simultaneously account for both measurement error and missing data using two well-known missing data methods: (1) inverse probability weighting; and (2) multiple imputation. These new methods are shown to yield consistent and asymptotically normal estimators, * Corresponding author Email: wenhan@nchu.edu.tw 1 Statistica Sinica: Newly accepted Paper (accepted version subject to English editing) the two approaches are shown to be asymptotically equivalent. We evaluated these methods on simulated and real capture-recapture data. Our results show improvements in both precision and efficiency when using the proposed methods.

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Section: Discussionmentioning

confidence: 99%

Section: Example 1: Harvest Mouse Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Closed-population capture--recapture models with measurement error and missing observations in covariates

Stoklosa¹,

Lee²,

Hwang³

2019

STAT SINICA

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“…For example, we can use the posbinomial family in the R package VGAM package (Yee, 2010) to allow the use of standard GLM methods in the analysis of capture‐recapture experiments. Stoklosa et al (2011) have recently developed a partial likelihood method that allows the direct use of standard GLM methodology such as SIMEX (Cook, 1994) and GAMS (Wood, 2006). The cost of this is that the partial likelihood conditions on the first capture and some information is lost.…”

Section: Discussionmentioning

confidence: 99%

A Review of the Use of Conditional Likelihood in Capture-Recapture Experiments

Huggins¹,

Hwang²

2011

International Statistical Review

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We present a modern perspective of the conditional likelihood approach to the analysis of capture-recapture experiments, which shows the conditional likelihood to be a member of generalized linear model (GLM). Hence, there is the potential to apply the full range of GLM methodologies. To put this method in context, we first review some approaches to capture-recapture experiments with heterogeneous capture probabilities in closed populations, covering parametric and non-parametric mixture models and the use of covariates. We then review in more detail the analysis of capture-recapture experiments when the capture probabilities depend on a covariate

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“…For example, Stoklosa et al. () demonstrated the applications of GAMs, generalized linear mixed models, and simulation–extrapolation. Here, we give another application using variable selection with many covariates.…”

Section: Applicationsmentioning

confidence: 99%

A weighted partial likelihood approach for zero‐truncated models

2019

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Zero‐truncated data arises in various disciplines where counts are observed but the zero count category cannot be observed during sampling. Maximum likelihood estimation can be used to model these data; however, due to its nonstandard form it cannot be easily implemented using well‐known software packages, and additional programming is often required. Motivated by the Rao–Blackwell theorem, we develop a weighted partial likelihood approach to estimate model parameters for zero‐truncated binomial and Poisson data. The resulting estimating function is equivalent to a weighted score function for standard count data models, and allows for applying readily available software. We evaluate the efficiency for this new approach and show that it performs almost as well as maximum likelihood estimation. The weighted partial likelihood approach is then extended to regression modelling and variable selection. We examine the performance of the proposed methods through simulation and present two case studies using real data.

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Heterogeneous Capture-Recapture Models with Covariates: A Partial Likelihood Approach for Closed Populations

Cited by 16 publications

References 37 publications

Closed-population capture--recapture models with measurement error and missing observations in covariates

Closed-population capture--recapture models with measurement error and missing observations in covariates

A Review of the Use of Conditional Likelihood in Capture-Recapture Experiments

A weighted partial likelihood approach for zero‐truncated models

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