Mixed model and estimating equation approaches for zero inflation in clustered binary response data with application to a dating violence study

Fulton, Kara A.; Liu, Danping; Haynie, Denise L.; Albert, Paul S.

doi:10.1214/14-aoas791

Cited by 5 publications

(10 citation statements)

References 29 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This finding agrees with observations by Su, Tom and Farewell (2009), who considered two-part models for semicontinuous data, as well as those of Fulton et al (2015), who modeled multivariate binary responses. As noted, an incorrect assumption of independence between the random parts of the model produces biases in the parameter estimates, in particular, the intercept for the abundance part, because correlated random effects are informative about cluster size (since parameters in the binary part influence the number of observations FIG.…”

Section: Results For Parameter Estimationsupporting

confidence: 91%

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

Cantoni¹,

Flemming²,

Welsh³

2017

Ann. Appl. Stat.

View full text Add to dashboard Cite

Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable species such as sharks, is a major challenge for contemporary fisheries management worldwide. Bycatch data, most often collected by at-sea observers during fishing trips, are clustered by trip and/or vessel and typically involve a large number of zero counts and very few positive counts. Though hurdle models are very popular for count data with excess zeros, models for clustered forms have received far less attention. Here we present a novel random-effects hurdle model for bycatch data that makes available accurate estimates of bycatch probabilities as well as other clusterspecific targets. These are essential for informing conservation and management decisions as well as for identifying bycatch hotspots, often considered the first step in attempting to protect endangered marine species. We validate our methodology through simulation and use it to analyze bycatch data on critically endangered hammerhead sharks from the U.S. National Marine Fisheries Service Pelagic Observer Program.

show abstract

Section: Results For Parameter Estimationsupporting

confidence: 91%

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

Cantoni¹,

Flemming²,

Welsh³

2017

Ann. Appl. Stat.

View full text Add to dashboard Cite

show abstract

“…() There are also mixed effects models for zero‐inflated clustered data that use random effects to introduce dependence. () However, these methods are typically limited to the equidispersed or overdispersed cases because of the properties of Poisson and negative binomial distributions.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach for analyzing zero‐inflated clustered count data with dispersion

Choo‐Wosoba

Gaskins

Levy

et al. 2017

Statistics in Medicine

View full text Add to dashboard Cite

In practice, count data may exhibit varying dispersion patterns and excessive zero values; additionally, they may appear in groups or clusters sharing a common source of variation. We present a novel Bayesian approach for analyzing such data. To model these features, we combine the Conway-Maxwell-Poisson distribution, which allows both overdispersion and underdispersion, with a hurdle component for the zeros and random effects for clustering. We propose an efficient Markov chain Monte Carlo sampling scheme to obtain posterior inference from our model. Through simulation studies, we compare our hurdle Conway-Maxwell-Poisson model with a hurdle Poisson model to demonstrate the effectiveness of our Conway-Maxwell-Poisson approach. Furthermore, we apply our model to analyze an illustrative dataset containing information on the number and types of carious lesions on each tooth in a population of 9-year-olds from the Iowa Fluoride Study, which is an ongoing longitudinal study on a cohort of Iowa children that began in 1991.

show abstract

“…A zero-modified model allows a range of negative values of p so that the value of the null parameter being on the boundary for testing for zero: inflation is avoided (Jansakul and Hinde, 2002). In the zero-inflation test, one of the proper methods for handling the boundary issue is to use likelihood ratio test (LRT) statistics, which asymptotically follows an equal mixture of point mass at 0 and a chi-squared distribution with one degree of freedom (Fulton et. al., 2015).…”

Section: Methodsmentioning

confidence: 99%

“…This is commonly referred to as zero inflation. In general, a Poisson distribution and/or a negative binomial distribution has been applied to a zero-inflated mixed effects model framework (Fulton, Liu, Haynie and Albert, 2015; Hall, 2000; Hasan, Sneddon and Ma, 2009, Yau, Wang and Lee, 2003; Rodrigues-Motta, Gianola and Heringstad, 2010; etc.). However, zero-inflated mixed effects models with these distributions is only suitable for count data with a selective range of data dispersion.…”

Section: Introductionmentioning

confidence: 99%

Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution

Choo‐Wosoba

Datta

2017

Journal of Applied Statistics

View full text Add to dashboard Cite

In recent years, data analysis techniques have been developed in biological and medical research areas with different types of count distributions. In particular, zero-inflated versions of parametric count distributions have been used to model excessive zeros that are often present in these assays. Perhaps, the most common count distribution which has been used for analyzing such data is the Poisson distribution. However, a Poisson distribution, having a single underlying parameter, cannot cope with any other data dispersion pattern besides equidispersion. A negative binomial distribution is capable of modeling overdispersed, but not underdispersed data. However, a Conway-Maxwell-Poisson (CMP) distribution (Conway, R. W., and Maxwell, W. L., 1962) can handle not only overdispersion but also underdispersion. We show with an illustrative data set on next generation sequencing of maize hybrids that both underdispersion and overdispersion can be present in genetic data. Furthermore, if count data consists of clustered observations, one of the most efficient statistical technique is to introduce a cluster specific random effect term. Once again, the maize hybrids data presents such a situation. We develop inference procedures for a zero-inflated CMP regression that incorporates a cluster specific random effect term. Unlike, the Gaussian models, the underlying likelihood is computationally challenging. We use a numerical approximation via a Gaussian quadrature to circumvent this issues. A test for checking zero-inflation has also been developed in our setting. Finite sample properties of our estimators and test have been investigated by extensive simulations. Finally, the statistical methodology has been applied to analyze the maize data mentioned before.

show abstract

Mixed model and estimating equation approaches for zero inflation in clustered binary response data with application to a dating violence study

Cited by 5 publications

References 29 publications

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

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

A Bayesian approach for analyzing zero‐inflated clustered count data with dispersion

Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution

Contact Info

Product

Resources

About