Zero-inflated-censored Weibull and gamma regression models to estimate wild boar population dispersal distance

The dynamics of the wild boar population has become a pressing issue not only for ecological purposes, but also for agricultural and livestock production. The data related to the wild boar dispersal distance can have a complex structure, including excess of zeros and right-censored observations, thus being challenging for modeling. In this sense, we propose two different zero-inflated-right-censored regression models, assuming Weibull and gamma distributions. First, we present the construction of the likelihood function, and then, we apply both models to simulated datasets, demonstrating that both regression models behave well. The simulation results point to the consistency and asymptotic unbiasedness of the developed methods. Afterwards, we adjusted both models to a simulated dataset of wild boar dispersal, including excess of zeros, right-censored observations, and two covariates: age and sex. We showed that the models were useful to extract inferences about the wild boar dispersal, correctly describing the data mimicking a situation where males disperse more than females, and age has a positive effect on the dispersal of the wild boars. These results are useful to overcome some limitations regarding inferences in zero-inflated-right-censored datasets, especially concerning the wild boar’s population. Users will be provided with an R function to run the proposed models.

Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques

PurposeThe purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources.Design/methodology/approachSince the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros.FindingsA comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time.Originality/valueThe findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.

Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. This approach has interesting features: first, it allows for overdispersion and the zero-inflated model represents a special case, and second, it allows for an elegant interpretation based on the typical clustering application of finite mixture models. k-finite mixture models are applied to a car insurance claim dataset in order to analyse whether the problem of unobserved heterogeneity requires a richer structure for risk classification. Our results show that the data consist of two subpopulations for which the regression structure is different.

Analysis of Antenatal Care Visit Data in Bangladesh Using Zero Modified Count Regression Model

In medical science, pharmaceutical studies, public health and socio-economic researches we often encounter the situation of excess of zeros in count data. This preponderance of zeros leads to overdispersion. In such cases traditional count data regression models like Poisson and negative binomial (NB) regression may not be pertinent for inference. The two most commonly used types of model that have been developed to adjust for excessivezeros in count data are Hurdle and zero-inflated models. In this study we have analyzed the antenatal care (ANC) visit data of pregnant women in Bangladesh using traditional and zero-modified count models. Based on the model selection criteria, we found that negative binomial hurdle model fits the data best. Through this analysis,we have perceived that the variables age of mother, division, birth order (order a child is born), place of residence, economic condition, media exposure of the mother, mainaccess road to village and education gap between husband and wife have significant impact on the mean number of ANC visits taken. Dhaka Univ. J. Sci. 67(2): 117-122, 2019 (July)

Modelling of zero-inflation improves inference of metagenomic gene count data

Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.

Modeling-Related Processes With an Excess of Zeros

Political science research frequently models binary or ordered outcomes involving related processes. However, traditional modeling of these outcomes ignores common data issues and cannot capture nuances. There is often an excess of zeros, the observed outcomes for different actors are inherently related, and competing actors may respond to the same factors differently. This paper extends existing models and develops a zero-inflated multivariate ordered probit to simultaneously address these issues. This model performs better than existing models at capturing the true parameters of interest, estimates the nature of the related processes, and captures the differences in actors’ decision-making. I demonstrate these benefits through simulation exercises and an application to party behavior in Mexico.

Heterogeneity and Number of Export Destinations of Italian Firms: A Hurdle Negative Binomial Regression Approach

This article addresses on an aspect of firms internationalization so far little explored, the choice of the number of export destinations and a proxy of the complexity of the export activity. As the outcome variable is a count with an excess of zeros, we use a hurdle regression model for count data that also allow disentangling the aspect of heterogeneity related to the decision to export from those measuring the number of markets served. Some differences arise by the comparison between the estimates regarding the propensity to export model and those of the model describing the number of export destinations. Regarding the propensity to export, the estimated models support the familiar evidences already presented in literature: exporters are larger, more productive, more innovative and invest more. With reference to the number of export destinations, it seems that not only the larger the number of markets served the more productive, large and willing to invest is the firm but also firms engaged in multiple markets seem to be older, financially stable, and willing to support organizational and managerial innovations.