A comparison of different methods of zero-inflated data analysis and an application in health surveys

Yang, Shuzhen; Harlow, Lisa L.; Puggioni, Gavino; Redding, Colleen A.

doi:10.22237/jmasm/1493598600

Cited by 65 publications

(43 citation statements)

References 26 publications

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“…Second, we used zero‐inflated population models described by Wenger and Freeman () and Yang et al () to estimate species abundance from the RSF. To minimize processing times in estimating count models statewide, we converted viewsheds to a 30‐m continuous raster dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we used a regression model to estimate the final statewide population sizes and 95% confidence intervals for the extrapolation model. Second, we used zero-inflated population models described by Wenger and Freeman (2008) and Yang et al (2017) to estimate species abundance from the RSF. To minimize processing times in estimating count models statewide, we converted viewsheds to a 30-m continuous raster dataset.…”

Section: Population Estimationmentioning

confidence: 99%

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Using spotlight observations to predict resource selection and abundance for white‐tailed deer

2019

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Spotlight surveys for white‐tailed deer (Odocoileus virginianus) can yield large presence‐only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat‐based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence‐only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county‐level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat‐based density estimator (extrapolation) and zero‐inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence‐only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society.

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Section: Methodsmentioning

confidence: 99%

Section: Population Estimationmentioning

confidence: 99%

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

2019

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“…As stated earlier, we propose two models for this research, one exploring attendance or not at live flamenco shows and another analysing frequency of participation. In fact, it is a twopart or "hurdle" model (Atoche, 2017, Lyashevska et al, 2016, Yang et al, 2017. These are two-component models: a first binary choice component, and a truncated count model, such as the ordinal logit model, used for positive counts.…”

Section: Application Of Logistic Regression Modelmentioning

confidence: 99%

“…What are the reasons behind this lack of involvement? Adopting an econometric approach based on constructing a logit discrete choice model divided into two parts, one binomial and another ordered (hurdle model), we explore the determinants of this reality (Atoche, 2017;Lyashevska, Brus & van der Meer, 2016;Yang, Harlow, Puggioni & Redding, 2017). Data were gathered from an "ad hoc" survey conducted during the 2016-2017 academic year amongst 452 University of Seville students, affiliated to different faculties.…”

Section: Introductionmentioning

confidence: 99%

Explanatory factors of university student participation in flamenco

Navarro

Martos

Oliver-Alfonso

2019

Economics &Amp; Sociology

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The present work offers a study exploring University of Seville students' cultural participation and how often they attend live flamenco shows. Based on the statistical yearbook of this university, a sample of 452 students from different fields was selected and, by applying a questionnaire, a binomial logit model and an ordered finance model were constructed. Our empirical findings offer descriptive, explanatory and predictive statistical results regarding participation and frequency. For example, the results evidence that 43% of the University of Seville students have never attended a live flamenco show and that one of the main issues influencing attendance is human and cultural capital.

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“…In this case, zero-inflated model might be suitable to model the data. Zero-inflated model assumes that zero value on the response variable were generated by two processes, namely the random zeros and structural zeros [1]. ZIP consists of two stages modeling: the first step to differentiate structural zeros with the counting process, and the second stage is to model the counting process, given the structural zeros have been sorted out from the data.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Zero Inflated Negative Binomial Regression Model for The Parkinson Data

Shafira¹,

Abdullah²,

Lestari³

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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Excess zeros can be solved by Zero Inflated Poisson (ZIP). If over-dispersion still exists in the data, the ZIP model is no longer suitable. Replacing the Poisson distribution with negative binomial distribution in the counting process may provide an alternative solution. Zero Inflated Negative Binomial (ZINB) regression model is estimated using the Bayesian method. Conjugate non-informative priors were used. Sampling parameters from posterior distribution is conducted using Markov Chain Monte Carlo (MCMC) simulation with 50,000 burn-in and 150,000 iterations. The model was then implemented to Parkinson's disease data obtained from the Parkinson's Progression Markers Initiative (PPMI) program. The MCMC result showed the convergence of the parameters. The result of the inspection of motoric aspect was significant in explaining does Parkinson's patients have to consume drugs or not. The result of the inspection of non-motoric aspect and body response were significant in explaining motoric complication in Parkinson's disease sufferers.

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A comparison of different methods of zero-inflated data analysis and an application in health surveys

Cited by 65 publications

References 26 publications

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

Explanatory factors of university student participation in flamenco

Bayesian Zero Inflated Negative Binomial Regression Model for The Parkinson Data

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