A random-effects hurdle model for predicting bycatch of endangered marine species

Cantoni, Eva; Flemming, Joanna Mills; Welsh, A. H.

doi:10.1214/17-aoas1074

Cited by 9 publications

(15 citation statements)

References 33 publications

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“…This situation would arise when unobserved differences between states affect state-level prevalence and concentration in the same way, and could bias the results if not addressed. In response, Min and Agresti (2005) suggest fitting the hurdle model with random effects following a bivariate normal distribution and estimating the model via approximations (see also Cantoni et al 2017).…”

Section: Analytical Strategymentioning

confidence: 99%

“…Unfortunately, the software available at INEGI did not permit the fitting of a hurdle model with bivariate normal random effects. While this could potentially lead to some bias in the estimates, (Cantoni et al 2017(Cantoni et al , p. 2191) note that estimated coefficients and standard errors are generally robust to this misspecification (see also McCulloch and Neuhaus 2011). Furthermore, Cantoni et al (2017) note that when there is no correlation between cluster prevalence and concentration, estimates from independently estimated hurdle models are unbiased.…”

Section: Analytical Strategymentioning

confidence: 99%

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Are Repeatedly Extorted Businesses Different? A Multilevel Hurdle Model of Extortion Victimization

2020

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Objectives Research consistently shows that crime concentrates on a few repeatedly victimized places and targets. In this paper we examine whether the same is true for extortion against businesses. We then test whether the factors that explain the likelihood of becoming a victim of extortion also explain the number of incidents suffered by victimized businesses. The alternative is that extortion concentration is a function of event dependence. Methods Drawing on Mexico’s commercial victimization survey, we determine whether repeat victimization occurs by chance by comparing the observed distribution to that expected under a Poisson process. Next, we utilize a multilevel negative binomial-logit hurdle model to examine whether area- and business-level predictors of victimization are also associated with the number of repeat extortions suffered by businesses. Results Findings suggest that extortion is highly concentrated, and that the predictors of repeated extortion differ from those that predict the likelihood of becoming a victim of extortion. While area-level variables showed a modest association with the likelihood of extortion victimization, they were not significant predictors of repeat incidents. Similarly, most business-level variables significantly associated with victimization risk showed insignificant (and sometimes contrary) associations with victimization concentration. Overall, unexplained differences in extortion concentration at the business-level were unaffected by predictors of extortion prevalence. Conclusions The inconsistent associations of predictors across the hurdle components suggest that extortion prevalence and concentration are fueled by two distinct processes, an interpretation congruent with theoretical expectations regarding extortion that considers that repeats are likely fueled by a process of event dependence.

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Section: Analytical Strategymentioning

confidence: 99%

Section: Analytical Strategymentioning

confidence: 99%

Are Repeatedly Extorted Businesses Different? A Multilevel Hurdle Model of Extortion Victimization

2020

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“…In addition, the data may include an excess of zeros. For example, in a study of garden bird abundance, average count, which is effectively continuous, spikes at zero for many of the species (Swallow et al 2016), in a study of precipitation, there are some months without rain (Harvey and Van der Merwe 2012;Fuentes et al 2008;Sun and Stein 2015), and in a study of bycatch data, the endangered hammerhead shark is often missing (Cantoni et al 2017). Our application is an agricultural plant protection experiment, where the weed of interest does not grow in every plot.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Nonnegative Data Using Dependency-Extended Two-Part Models

Rodrigues-Motta

Forkman

2021

JABES

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This article is motivated by the challenge of analysing an agricultural field experiment with observations that are positive on a continuous scale or zero. Such data can be analysed using two-part models, where the distribution is a mixture of a positive distribution and a Bernoulli distribution. However, traditional two-part models do not include any dependencies between the two parts of the model. Since the probability of zero is anticipated to be high when the expected value of the positive part is low, and the other way around, this article introduces dependency-extended two-part models. In addition, these extensions allow for modelling the median instead of the mean, which has advantages when distributions are skewed. The motivating example is an incomplete block trial comparing ten treatments against weed. Gamma and lognormal distributions were used for the positive response, although any density on the support of real numbers can be accommodated. In a cross-validation study, the proposed new models were compared with each other and with a baseline model without dependencies. Model performance and sensitivity to choice of priors were investigated through simulation. A dependency-extended two-part model for the median of the lognormal distribution performed best with regard to mean square error in prediction. Supplementary materials accompanying this paper appear online.

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“…In both models, covariates are used parametrically to describe the two components of the model. Nonparametric extensions to the hurdle model have been proposed by Barry and Welsh (2002), whereas parametric versions with random effects are a current research topic (e.g., Cantoni et al. 2017).…”

Section: Introductionmentioning

confidence: 99%

Stochastic variable selection strategies for zero-inflated models

Cantoni

Auda

2017

Statistical Modelling

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When count data exhibit excess zero, that is more zero counts than a simpler parametric distribution can model, the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) models are often used. Variable selection for these models is even more challenging than for other regression situations because the availability of p covariates implies 4 p possible models. We adapt to zero-inflated models an approach for variable selection that avoids the screening of all possible models. This approach is based on a stochastic search through the space of all possible models, which generates a chain of interesting models. As an additional novelty, we propose three ways of extracting information from this rich chain and we compare them in two simulation studies, where we also contrast our approach with regularization (penalized) techniques available in the literature. The analysis of a typical dataset that has motivated our research is also presented, before concluding with some recommendations.

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A random-effects hurdle model for predicting bycatch of endangered marine species

Cited by 9 publications

References 33 publications

Are Repeatedly Extorted Businesses Different? A Multilevel Hurdle Model of Extortion Victimization

Are Repeatedly Extorted Businesses Different? A Multilevel Hurdle Model of Extortion Victimization

Bayesian Analysis of Nonnegative Data Using Dependency-Extended Two-Part Models

Stochastic variable selection strategies for zero-inflated models

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