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In this paper, we propose systematic approaches for learning imbalanced data based on a two-regime process: regime 0, which generates excess zeros (majority class), and regime 1, which contributes to generating an outcome of one (minority class). The proposed model contains two latent equations: a split probit (logit) equation in the first stage and an ordinary probit (logit) equation in the second stage. Because boosting improves the accuracy of prediction versus using a single classifier, we combined a boosting strategy with the two-regime process. Thus, we developed the zero-inflated probit boost (ZIPBoost) and zero-inflated logit boost (ZILBoost) methods. We show that the weight functions of ZIPBoost have the desired properties for good predictive performance. Like AdaBoost, the weight functions upweight misclassified examples and downweight correctly classified examples. We show that the weight functions of ZILBoost have similar properties to those of LogitBoost. The algorithm will focus more on examples that are hard to classify in the next iteration, resulting in improved prediction accuracy. We provide the relative performance of ZIPBoost and ZILBoost, which rely on the excess kurtosis of the data distribution. Furthermore, we show the convergence and time complexity of our proposed methods. We demonstrate the performance of our proposed methods using a Monte Carlo simulation, mergers and acquisitions (M&A) data application, and imbalanced datasets from the Keel repository. The results of the experiments show that our proposed methods yield better prediction accuracy compared to other learning algorithms. [ABSTRACT FROM AUTHOR]

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility. [ABSTRACT FROM AUTHOR]

Background and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. Methods: In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. $\mathtt {SAS}$ programs to implement these newly proposed hospital compare models were thoroughly developed. The $\mathtt {SAS}$ programs are freely available via a GitHub (https:\\www.GitHub.com) repository. Results: We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Conclusions: Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.

Understanding how habitat characteristics influence common bottlenose dolphin, Tursiops truncatus, distribution and behavior can be useful for conservation. The dolphin community in Roanoke Sound, North Carolina primarily exhibits seasonal residency and there is limited information on their habitat use. The objectives of this study were to increase habitat use knowledge and determine the relationship between habitat characteristics and dolphin distribution using standardized photographic-identification data (2009 – 2017). A hot spot (Getis-Ord Gi*) analysis showed dolphins frequently use the southern region containing the mouth of the estuary for feeding and traveling. Habitat characteristics were modeled with zero-altered gamma (ZAG), generalized linear (GLM), and generalized additive (GAM) models to predict dolphin group density. Models showed that groups were more likely to be present in areas with greater benthic slope variation and shallow areas closer to land, and that different habitat characteristics were associated with feed, social, and travel activities. This study suggests that Roanoke Sound provides a seasonal foraging area and travel corridor between the estuaries and coastal waters. This information contributes baseline knowledge of how habitat potentially influences dolphin distribution and behavior which can be useful for management and conservation, especially in areas where habitat changes and impacts need to be assessed.

• The highest number of eggs laid by Ae. albopictus in the Botanical Garden of Sóller was in September. • Aedes albopictus showed preference for oviposition in laurel forest and croplands. • Zero-inflated models showed that large leaves and flowering had a positive effect on the oviposition of Ae. albopictus. Mosquitoes are capable of transmitting pathogens of both medical and veterinary significance. Addressing the nuisance and vector roles of Aedes albopictus through surveillance and control programs is a primary concern for European countries. Botanical gardens provide suitable habitats for the development of Ae. albopictus and represent typical points of entry of invasive species. To assess the oviposition preferences alongside various biotic parameters (plant species community, shade index, and flowering), we conducted a study in a botanical garden of Sóller (Mallorca, Balearic Islands, Spain). A total of 6,368 Ae. albopictus eggs were recorded in 36 ovitraps positioned and revised every 15 days in seven different habitats over six months in 2016. Zero-inflated and generalized linear mixed-effects models were used to analyse Ae. albopictus habitat preferences. The number of eggs increased throughout the sampling period, peaking in September. The oviposition rates showed a patchy distribution, with Ae. albopictus showing preference for oviposition in laurel forest and cropland habitats. A positive effect of large leaves and presence of flowers on the oviposition of Ae. albopictus were also recorded. This study provides valuable information into the behaviour of Ae. albopictus in botanical gardens, which is essential data for informing surveillance and control programs. [Display omitted] [ABSTRACT FROM AUTHOR]

The statistical modelling of discrete extremes has received less attention than their continuous counterparts in the extreme value theory (EVT) literature. One approach to the transition from continuous to discrete extremes is the modelling of threshold exceedances of integer random variables by the discrete version of the generalized Pareto distribution. However, the optimal choice of thresholds defining exceedances remains a problematic issue. Moreover, in a regression framework, the treatment of the majority of non-extreme data below the selected threshold is either ignored or separated from the extremes. To tackle these issues, we expand on the concept of employing a smooth transition between the bulk and the upper tail of the distribution. In the case of zero inflation, we also develop models with an additional parameter. To incorporate possible predictors, we relate the parameters to additive smoothed predictors via an appropriate link, as in the generalized additive model (GAM) framework. A penalized maximum likelihood estimation (MLE) procedure is implemented. We illustrate our modelling proposal with a real dataset of avalanche activity in the French Alps. With the advantage of bypassing the threshold selection step, our results indicate that the proposed models are more flexible and robust than competing models, such as the negative binomial distribution. [ABSTRACT FROM AUTHOR]

This article presents the results of the implementation of a forecasting model, to predict the relief materials needed for assisting in decisions prior to natural disasters, thus filling a gap in the exploration of Generalized Linear Mixed Models (GLMM) in a humanitarian context. Demand information from the State of Sao Paulo, Brazil was used to develop the Zero Inflated Negative Binomial Multilevel (ZINBM) model, which gets to handle the excess of zeros in the count data and considers the nested structure of the data set. Strategies for selecting predictor variables were based on the understanding of the needs for relief supplies; consequently, they were derived from vulnerability indicators, demographic factors, and occurrences of climatic anomalies. The model presents coefficients that are statistically significant, and the results show the importance of considering the nested structure of the data and the zero-inflated nature of the outcome variable. To validate the fitness of the ZINBM model, it was compared against the Poisson, Negative Binomial (NB), Zero Inflated Poisson (ZIP), and Zero Inflated Negative Binomial (ZINB) models. [ABSTRACT FROM AUTHOR]

Intersection safety continues to be a crucial issue throughout the United States. In 2016, 27% of the 37,461 traffic fatalities on U.S. roadways occurred at or near intersections. Nearly 70% of intersection-related fatalities occurred at unsignalized intersections. At such intersections, vehicles stopping or slowing to turn create speed differentials between vehicles traveling in the same direction. This is particularly problematic on two-lane highways. Research was performed to analyze safety performance for intersections on rural, two-lane roadways, with stop control on the minor roadway. Roadway, traffic, and crash data were collected from 4148 stop-controlled intersections of all 64 Parishes (counties) statewide in Louisiana, for the period of 2013 to 2017. Four count approaches, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) were used to model the number of intersection crashes for different severity levels. The results indicate that ZIP models provide a better fit than all other models. In addition to traffic volume, larger curve radii of major and minor roads and wider lane widths of major roads led to significantly smaller crash occurrences. However, higher speed limits of major roads led to significantly greater crash occurrences. Four-leg stop-controlled intersections have 35% greater total crashes, 49% greater fatal and injury crashes, and 25% greater property damage only (PDO) crashes, relative to three-leg intersections.

We propose a tree-structured hierarchical architecture for estimating count variables that exhibit both excessive zeros and over-dispersion, which is commonly observed in practice. The underlying statistical model is based on the Mixture-of-experts (MoE) model, a generalization of finite mixture regression models. With two levels of experts, the proposed model can efficiently model both excessive zeros and the long right-tail, controlled by two gating networks at different levels in the hierarchy. We develop the general form of the maximum likelihood estimator for the proposed model based on the EM algorithm with two latent variables, which allows a natural interpretation and convenient optimization for the likelihood function. As a numerical application, we apply the proposed model to a well-known real dataset and found that it shows superior performance and allows better interpretations compared to other existing alternatives. [ABSTRACT FROM AUTHOR]

This paper explores the link between air transport safety and profitability. Traditionally, the empirical literature has used Poisson regression models to estimate the expected number of accidents given the profitability of the airline. However, there are two major deficiencies in this analysis related to the statistical properties of the data. First, the equi-dispersion assumed in Poisson models hardly holds in the airline data. Second, accidents are rare cases, so the data has an excess of zeros. In this paper, we propose the use of a zero-inflated negative binomial model to deal with these shortcomings. Our results show several interesting facts. On the one hand, they show that airlines with higher levels of profitability are less likely to have an accident. On the other hand, when an accident occurs, there is a higher expected number of accidents in airlines with higher profitability. Finally, the severity of an accident has an inverse relationship with profitability. • The paper contributes to the literature by addressing the overdispersion and excess of zeros problems. • The relationship between safety and profits and cost is not linear. • Companies with higher profits and lower cost are less likely to have an accident. • However, after an accident those companies are more likely to have more accidents. [ABSTRACT FROM AUTHOR]

Zero-inflated count data are frequently encountered in public health and epidemiology research. Two-parts model is often used to model the excessive zeros, which are a mixture of two components: a point mass at zero and a count distribution, such as a Poisson distribution. When the rate of events per unit exposure is of interest, offset is commonly used to account for the varying extent of exposure, which is essentially a predictor whose regression coefficient is fixed at one. Such an assumption of exposure effect is, however, quite restrictive for many practical problems. Further, for zero-inflated models, offset is often only included in the count component of the model. However, the probability of excessive zero component could also be affected by the amount of 'exposure'. We, therefore, proposed incorporating the varying exposure as a covariate rather than an offset term in both the probability of excessive zeros and conditional counts components of the zero-inflated model. A real example is used to illustrate the usage of the proposed methods, and simulation studies are conducted to assess the performance of the proposed methods for a broad variety of situations. [ABSTRACT FROM AUTHOR]

In healthcare economics count datasets often exhibit excessive zeros or right-skewed tails. When covariates are available, such datasets are typically modelled using the zero-inflated (ZI) or finite mixture (FM) regression models. However, neither model performs adequately when the dataset has both excessive zeros and a long tail, which is often the case in practice. In this paper we combine these two models to create a more flexible, versatile class of ZIFM models. With this model we perform a comprehensive analysis on the number of visits to a physician's office using the US healthcare demand dataset that has been used in numerous healthcare studies in the literature. After comparing to other existing models which have been reported to perform well on this dataset, we find that the ZIFM model substantially outperforms alternative models. In addition, the model offers a new interpretation that is in contrast to previous empirical findings regarding the factors associated with the demand for the physicians, which can shed a fresh light on the healthcare utilisation policies. [ABSTRACT FROM AUTHOR]

Abstract Background Psychiatric disorders may occur as a single episode or be persistent and relapsing, sometimes leading to suicidal behaviours. The exact causes of psychiatric disorders are hard to determine but easy access to health care services can help to reduce their severity. The aim of this study was to investigate the factors associated with repeated hospitalizations among the patients with psychiatric illness, which may help the policy makers to target the high-risk groups in a more focused manner. Methods A large linked administrative database consisting of 200,537 patients with psychiatric diagnosis in the years of 2008-2012 was used in this analysis. Various counts regression models including zero-inflated and hurdle models were considered for analyzing the hospitalization rate among patients with psychiatric disorders within three months follow-up since their index visit dates. The covariates for this study consisted of socio-demographic and clinical characteristics of the patients. Results The results show that the odds of hospitalization are significantly higher among registered Indians, male patients and younger patients. Hospitalization rate depends on the patients’ disease types. Having previously visited a general physician served a protective role for psychiatric hospitalization during the study period. Patients who had seen an outpatient psychiatrist were more likely to have a higher number of psychiatric hospitalizations. This may indicate that psychiatrists tend to see patients with more severe illnesses, who require hospital-based care for managing their illness. Conclusions Providing easier access to registered Indian people and youth may reduce the need for hospital-based care. Patients with mental health conditions may benefit from greater and more timely access to primary care.

Intersection safety continues to be a crucial issue throughout the United States. In 2016, 27% of the 37,461 traffic fatalities on U.S. roadways occurred at or near intersections. Nearly 70% of intersection-related fatalities occurred at unsignalized intersections. At such intersections, vehicles stopping or slowing to turn create speed differentials between vehicles traveling in the same direction. This is particularly problematic on two-lane highways. Research was performed to analyze safety performance for intersections on rural, two-lane roadways, with stop control on the minor roadway. Roadway, traffic, and crash data were collected from 4148 stop-controlled intersections of all 64 Parishes (counties) statewide in Louisiana, for the period of 2013 to 2017. Four count approaches, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) were used to model the number of intersection crashes for different severity levels. The results indicate that ZIP models provide a better fit than all other models. In addition to traffic volume, larger curve radii of major and minor roads and wider lane widths of major roads led to significantly smaller crash occurrences. However, higher speed limits of major roads led to significantly greater crash occurrences. Four-leg stop-controlled intersections have 35% greater total crashes, 49% greater fatal and injury crashes, and 25% greater property damage only (PDO) crashes, relative to three-leg intersections. • Poisson, NB, ZIP and ZINB are used to model the number of intersection crashes for different severity levels. • Vuong test results show that ZIP models provide a better fit than all other models. • AADT, curve radius, lane widths, speed limits, and number of legs have significant impact on intersection safety. [ABSTRACT FROM AUTHOR]

This article presents the results of the implementation of a forecasting model, to predict the relief materials needed for assisting in decisions prior to natural disasters, thus filling a gap in the exploration of Generalized Linear Mixed Models (GLMM) in a humanitarian context. Demand information from the State of Sao Paulo, Brazil was used to develop the Zero Inflated Negative Binomial Multilevel (ZINBM) model, which gets to handle the excess of zeros in the count data and considers the nested structure of the data set. Strategies for selecting predictor variables were based on the understanding of the needs for relief supplies; consequently, they were derived from vulnerability indicators, demographic factors, and occurrences of climatic anomalies. The model presents coefficients that are statistically significant, and the results show the importance of considering the nested structure of the data and the zero-inflated nature of the outcome variable. To validate the fitness of the ZINBM model, it was compared against the Poisson, Negative Binomial (NB), Zero Inflated Poisson (ZIP), and Zero Inflated Negative Binomial (ZINB) models.

The literature on the peer-to-peer hospitality market largely assumes that Airbnb hosts are loyal to the platform. However, similar to hoteliers, Airbnb hosts have access to various platforms to diversify their distribution channels. In cases where hosts significantly diversify their distribution channels, they should be considered rather as professional competitors mimicking hoteliers. Proposing an original image-recognition approach to obtain a proxy of the number of platforms used by Airbnb hosts, this paper assesses the probability that a home-sharer practices multichannel distribution. Drawing on a sample of more than 3900 hosts from the region of Corsica in France, we show that a multichannel distribution strategy is commonly adopted. Furthermore, a significant fraction of small hosts uses three or more channels.

Bone tumours are rarely found in children and adolescents (0- 19 years old), but there are reports from some provinces in Iraq indicating possible increases in the incidence of childhood bone cancer. Since counts are very low and often zero, or near zero, we fitted zero-inflated Poisson, zero-inflated negative binomial, Poisson hurdle, and negative binomial hurdle regression models to investigate these changes. We used data covering the 2000-2015 period taking age, gender and province into account with the aim of identifying potential health disparities. The results indicate that the zero-inflated Poisson is the most appropriate approach. We also found that, the incidence rate ratio of bone tumours for age groups of 5-9, 10-14 and 15-19 years were 134%, 490% and 723% higher, respectively, compared to the 0-4 year olds. The incidence rate was higher by 49% higher in males compared to females. Compared to 2000-2004, the rate was higher during 2005-2009 and 2010-2015 by 23% and 50%, respectively. In addition, the provinces Al-Muthana and Al-Diwaniyah in the South were found to have a higher incidence rate than other provinces. Join point analysis showed that the age-adjusted incidence rate had a significant, increasing trend, with an average percentage change of 3.1% during 2000-2015. The study suggests that further research into childhood tumours, bone tumours in particular, is needed. Reference to the effect of environmental factors in this group of medical disorders would be of special interest.

Count data are ubiquitous in ecology and the Poisson generalized linear model (GLM) is commonly used to model the association between counts and explanatory variables of interest. When fitting this model to the data, one typically proceeds by first confirming that the model assumptions are satisfied. If the residuals appear to be overdispersed or if there is zero‐inflation, key assumptions of the Poison GLM may be violated and researchers will then typically consider alternatives to the Poison GLM. An important question is whether the potential model selection bias introduced by this data‐driven multi‐stage procedure merits concern.Here we conduct a large‐scale simulation study to investigate the potential consequences of model selection bias that can arise in the simple scenario of analysing a sample of potentially overdispersed, potentially zero‐inflated, count data. Specifically, we investigate model selection procedures recently recommended by Blasco‐Moreno et al. (2019) using either a series of score tests or information theoretic criteria to select the best model.We find that, when sample sizes are small, model selection based on preliminary score tests (or information theoretic criteria, e.g. AIC, BIC) can lead to potentially substantial inflation of false positive rates (i.e. type 1 error inflation). When sample sizes are sufficiently large, model selection based on preliminary score tests, is not problematic.Ignoring the possibility of overdispersion and zero‐inflation during data analyses can lead to invalid inference. However, if one does not have sufficient power to test for overdispersion and zero‐inflation, post hoc model selection may also lead to substantial bias. This 'catch‐22' suggests that, if sample sizes are small, a healthy skepticism is warranted whenever one rejects the null hypothesis of no association between a given outcome and covariate. [ABSTRACT FROM AUTHOR]

A growing body of scholarship has been focusing on the determinants of subsidy spending. Yet, this literature has mostly overlooked sectoral approaches to explain variation in aid allocations, which can provide useful insights about state-business relations that macro-analyses cannot. The article explores the political determinants of state aid in a key sector of the European economy, the automotive industry, which represents a crucial test case to analyse the relationship between big firms and the state. The article argues that there are two mechanisms through which governments may choose to allocate aid: achievement of policy goals and electoral competition. The resulting hypotheses are tested on an original dataset of over 120 state aid measures in 16 member states of the European Union (EU) where an automotive industry is present between 1992 and 2011. The results show that aid allocations are seldom found to be determined by a government's preferences over its policy objectives. Instead, electoral rules such as the cultivation of a personal reputation by distribution of targeted benefits to a constituency may help legislators in their chances of being re-elected. The article concludes with limitations of the present study and suggestions for future research. [ABSTRACT FROM AUTHOR]

In most of the longitudinal studies, involving count responses, excess zeros are common in practice. Usually, the current response measurement in a longitudinal sequence is a function of previous outcomes. For example, in a study about acute renal allograft rejection, the number of acute rejection episodes for a patient in current time is a function of this outcome at previous follow-up times. In this paper, we consider a transition model for accounting the dependence of current outcome on the previous outcomes in the presence of excess zeros. We propose the use of the generalized Poisson distribution as a flexible distribution for considering overdispersion (or underdispersion). The maximum likelihood estimates of the parameters are obtained using the EM algorithm. Some simulation studies are performed for illustration of the proposed methods. Also, analysis of a real data set of a kidney allograft rejection study illustrates the application of the proposed model.

In prophylactic vaccine studies in healthy populations, many subjects do not experience a single adverse event (AE). Thus, the number of AEs observed in such clinical trials may be difficult to model because of an excess of zeroes relative to the parametric distributions assumed. To determine which type of modeling provides a better fit for observed AE data, a variety of models were applied to data from an integrated safety database from clinical trials of the meningococcal vaccine MenB-FHbp (Trumenba®, bivalent rLP2086; Pfizer Inc, Philadelphia, PA). MenB-FHbp was the first vaccine approved in the United States to prevent meningococcal serogroup B disease in individuals aged 10 to 25 years. Specifically, this report presents an integrated analysis of AEs from 8 randomized controlled trials that compared MenB-FHbp to placebo or active controls. The number of AEs occurring from dose one to 30 days after the last dose was analyzed. Six models were compared: standard Poisson and negative binomial models and their corresponding zero-inflation and hurdle models. Models were evaluated for their ability to predict the number of AEs and by goodness-of-fit statistics. Models based on the Poisson distribution were a poor fit. The zero-inflated negative binomial model and negative binomial hurdle model provided the closest fit. These results suggest that zero-inflated or hurdle models may provide a better fit to AE data from healthy populations compared with conventional parametric models.

In health services research, endogenous healthcare utilization refers to the notion that the choice of utilizing health services is endogenous due to its correlation with the intensity of utilization outcomes, such as the number of emergency room visits. Greene in (Empir Econ 36:133–173, 2009) extended four conventional two-part models for zero-abundant count utilization to the two-part models that account for endogenous utilization. However, statistical inference on the (average) marginal and incremental effects in these models has not been carefully studied. The present article provides the estimation formulations for (average) marginal and incremental effects of the four two-part models: zero-inflated Poisson and negative binomial models, and hurdle Poisson and negative binomial models with correlated errors that characterize endogenous healthcare utilization. The variance estimation derived from the delta method is provided to facilitate the statistical inference of these effects. We then perform simulation studies to numerically justify our methodology. An empirical study is presented to investigate the average effects of household income and health insurance status on healthcare utilization with the German Scocioeconomic Panel data. The four models give consistent results regarding interpretations for moral hazards and adverse selection in the study. [ABSTRACT FROM AUTHOR]

Microorganisms play critical roles in human health and disease. They live in diverse communities in which they interact synergistically or antagonistically. Thus for estimating microbial associations with clinical covariates, such as treatment effects, joint (multivariate) statistical models are preferred. Multivariate models allow one to estimate and exploit complex interdependencies among multiple taxa, yielding more powerful tests of exposure or treatment effects than application of taxon-specific univariate analyses. Analysis of microbial count data also requires special attention because data commonly exhibit zero inflation, i.e., more zeros than expected from a standard count distribution. To meet these needs, we developed a Bayesian variable selection model for multivariate count data with excess zeros that incorporates information on the covariance structure of the outcomes (counts for multiple taxa), while estimating associations with the mean levels of these outcomes. Though there has been much work on zero-inflated models for longitudinal data, little attention has been given to high-dimensional multivariate zero-inflated data modeled via a general correlation structure. Through simulation, we compared performance of the proposed method to that of existing univariate approaches, for both the binary ("excess zero") and count parts of the model. When outcomes were correlated the proposed variable selection method maintained type I error while boosting the ability to identify true associations in the binary component of the model. For the count part of the model, in some scenarios the univariate method had higher power than the multivariate approach. This higher power was at a cost of a highly inflated false discovery rate not observed with the proposed multivariate method. We applied the approach to oral microbiome data from the Pediatric HIV/AIDS Cohort Oral Health Study and identified five (of 44) species associated with HIV infection. [ABSTRACT FROM AUTHOR]

Defects generated in semiconductor manufacturing processes have serious effects on the yield of integrated circuits (ICs). Accurate modeling of the defect counts on IC chips is crucial for predicting the yield. The conventional Poisson yield model tends to underestimate the true yield by ignoring overdispersed patterns of defects on the wafer. This article uses various models based on the generalized Poisson (GP) distribution and/or HZ distributions to explore the overdispersed defect counts on semiconductor wafers. Real wafer map data are used to compare the performance of both nonregression and regression modeling approaches in terms of the log-likelihood, AIC, and relative bias for yield estimation. Analytical results indicate that the GP distribution is a competitive alternative to the negative binomial (NB) distribution for modeling defect counts on IC chips because the GP distribution can model overdispersion, underdispersion, or no dispersion. In particular, HZ models based on the NB and GP distributions show good potential for predicting the yield of IC chips on wafers. [ABSTRACT FROM AUTHOR]

This study was aimed at examining the performance of count data models under various outliers and zero inflation situations with simulated data. Poisson, Negative Binomial, Zero-inflated Poisson, Zero-inflated Negative Binomial, Poisson Hurdle and Negative Binomial Hurdle models were considered to test how well each of the model fits the selected datasets having outliers and excess zeros. We found that Zero-inflated Negative Binomial and Negative Binomial Hurdle models were found to be more successful than other count data models. Also the results indicated that in some scenarios, the Negative Binomial model outperformed other models in the presence of outliers and/or excess zeros. [ABSTRACT FROM AUTHOR]

By starting from the one-parameter Bell distribution proposed recently in the statistic literature, we introduce the zero-inflated Bell family of distributions. Additionally, on the basis of the proposed zero-inflated distribution, a novel zero-inflated regression model is proposed, which is quite simple and may be an interesting alternative to usual zero-inflated regression models for count data. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the zero-inflated Bell regression parameters. Monte Carlo simulations indicate that the maximum likelihood method is quite effective to estimate the zero-inflated Bell regression parameters. We also propose the Pearson residuals for the new zero-inflated regression model to assess departures from model assumptions. Additionally, the global and local influence methods are discussed. In particular, the normal curvature for studying local influence is derived under case weighting perturbation scheme. Finally, an application to the count of infected blood cells is considered to illustrate the usefulness of the zero-inflated Bell regression model in practice. The results suggest that the new zero-inflated Bell regression is more appropriate to model these count data than other familiar zero-inflated (or not) regression models commonly used in practice. [ABSTRACT FROM AUTHOR]

In a wide range of applications, standard regression techniques are hard to apply because the responses may consist of a continuous part but augmented with a discrete number of additional response categories with probability greater than zero. Previous methods often assume that the process of both parts can be treated structurally independent given covariates which facilitates estimation considerably. However, this simplifying assumption is often too restrictive and questionable for the data situation at hand. To address this, we propose a novel approach for modelling mixed discrete-continuous responses where the probabilities of the boundary cases are based on integrated weighted densities of the continuous part. The weight functions themselves may depend on covariates as well as unknown parameters. We discuss different types of mixed discrete-continuous distributions and consider inferential methods in a Bayesian and maximum likelihood framework. We evaluate parameter estimation carefully in simulation studies before applying them to the analysis of income distributions using a specific instance of the novel zero-adjusted-type model. [ABSTRACT FROM AUTHOR]

OBJECTIVES Tuberculosis (TB) is a global public health problem that causes morbidity and mortality in millions of people per year. The purpose of this study was to examine the relationship of potential risk factors with TB mortality in Iran. METHODS This cross-sectional study was performed on 9,151 patients with TB from March 2017 to March 2018 in Iran. Data were gathered from all 429 counties of Iran by the Ministry of Health and Medical Education and Statistical Center of Iran. In this study, a generalized estimating equation-based zero-inflated negative binomial model was used to determine the effect of related factors on TB mortality at the community level. For data analysis, R version 3.4.2 was used with the relevant packages. RESULTS The risk of mortality from TB was found to increase with the unemployment rate (β^=0.02), illiteracy (β^=0.04), household density per residential unit (β^=1.29), distance between the center of the county and the provincial capital (β^=0.03), and urbanization (β^=0.81). The following other risk factors for TB mortality were identified: diabetes (β^=0.02), human immunodeficiency virus infection (β^=0.04), infection with TB in the most recent 2 years (β^=0.07), injection drug use (β^=0.07), long-term corticosteroid use (β^=0.09), malignant diseases (β^=0.09), chronic kidney disease (β^=0.32), gastrectomy (β^=0.50), chronic malnutrition (β^=0.38), and a body mass index more than 10% under the ideal weight (β^=0.01). However, silicosis had no effect. CONCLUSIONS The results of this study provide useful information on risk factors for mortality from TB.

Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption in 149 countries. The dataset in the mentioned study included a reasonable amount of zeros (13.19%) in the outcome variable, which is typical of this type of research, as well as quite a bit of social sciences research. In our paper, present detailed guidelines regarding the estimation of models where the data for the outcome variable includes an excess number of zeros, and the dataset has a natural nested structure. We believe our research is not likely to reject the hypothesis favoring the adoption of mixed modeling and the inflation of zeros over the original simpler framework. Instead, our results demonstrate the importance of considering random effects at country levels and the zero-inflated nature of the outcome variable.

The log-linear Poisson model, characterized by linear variance function and a logarithmic relation between means and covariates, embedded in the exponential family regression framework provided by generalized linear models (GLM) is still the standard approach for analyzing count data responses with regression models. In practice, however, count data are often overdispersed and, thus, not conducive to Poisson regression. Therefore, the main goal of this article is to introduce a log-linear model based on the P ∅ ′ lya–Aeppli (PA) distribution, which is an extension of the Poisson distribution by including a dispersion parameter ρ, to address the problem of overdispersion. Maximum likelihood (ML) estimation procedure is discussed as well as a test for determining the need for a PA regression over a standard Poisson regression. In addition, a simple EM-type algorithm for iteratively computing ML estimates is presented. In order to study departures from the error assumption as well as the presence of outliers, we perform residual analysis based on the standardized Pearson residuals. Furthermore, for different parameter settings and sample sizes, various simulations are performed. Finally, we also illustrated the new method on three real datasets, two of them are from biological researches and the other is from a violence study. [ABSTRACT FROM AUTHOR]

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Product expiration is an important problem in the consumer packaged goods (CPG) industry costing 1%–2% of gross retail sales and eroding industry profits substantially. It can be caused by several factors related to store operations, supply chain practices, and product characteristics. Existing methods used in the industry are inadequate to identify the causes of expiration, leading to inadequate efforts to reduce expiration. Using retail data for 768 SKUs and 10,000 stores (745,638 store-SKU–level observations), as well as upstream supply chain data from a CPG manufacturer, we show the extent to which expiration of products in retail stores is driven by case size, inventory aging in the supply chain, minimum order rules, manufacturers' incentive programs for the sales force, replenishment workload, and many control variables. A counterfactual analysis based on the model shows that our subject manufacturer can reduce expiration by up to $38.82 million per year by implementing four selected initiatives involving case size, supply chain aging, minimum order rules, and sales incentives. Further, targeted initiatives can be designed using combinations of these variables for subsets of products with the highest occurrence of expiration. The online appendix is available at https://doi.org/10.1287/mnsc.2018.3051. This paper was accepted by Serguei Netessine, operations management. [ABSTRACT FROM AUTHOR]

Red tides have affected marine and coastal communities for centuries. The effects of red tides on commercial capture fisheries have rarely been evaluated, owing in part to a lack of appropriate data. Leveraging modeled maps of red tide severity, vessel monitoring systems data, and logbook records, we investigated the commercial fisheries impacts of red tide events in Florida during the period 2008–2019. We focused on the bottom longline and vertical line fisheries targeting reef fishes. Red tides were punctuated events peaking at the end of summer, with 2018 and 2019 the most intense years. The total area affected by intense red tides (concentrations> 1,000 cells/L) was 103,483 km2, about the size of the state of Kentucky. Red tides did not have negative effect on fleet-level fisheries metrics such as number of fishing vessels, trips, fishing effort or revenue (generalized mixed effect models, p > 0.5). Fishing effort was, however, displaced from the red tide areas, and fishers avoided areas characterized by intense blooms (zero-inflated negative binomial model, p < 0.001). Follow up of the long-term likely responses of the resource and the catch are needed to fully understand the consequences of red tide events in commercial fisheries in the Gulf of Mexico. • Red tides have affected the Florida coast for centuries incurring in economic losses. • Wild reef fisheries have not been affected in terms of fleet-level metrics. • But fishers avoided intense red tide blooms, which caused fisheries displacement. [ABSTRACT FROM AUTHOR]

In this paper, we consider estimating the parameters of bivariate and zero-inflated bivariate Poisson regression models using the conditional method. This method is compared with the standard method, which uses the joint probability function. Simulations and real applications show that the two methods have almost identical Akaike Information Criteria and parameter estimates, but the conditional method has a much faster execution time than the joint method. We conducted our computations using the R and SAS package. Our results also indicate that the execution time of SAS is faster than that of R.

The establishment of non-native habitat-forming seaweeds into new areas may trigger important changes in ecosystem functioning, yet their community and ecosystem-level effects remain largely understudied. Here we studied the spatial distribution of two common fish species (Xyrichtys novacula and Bothus podas) which are key components of communities in unconsolidated bottoms of temperate areas regarding the colonization of the newly-introduced tropical seaweed Halimeda incrassata in the Mediterranean Sea. We used a spatially-explicit before-after-control-impact model and a unique data-set formed by 6 years of fine-scale spatial information of fish and seaweed distribution and abundance. We demonstrate a long-term alteration on the spatial distribution of X. novacula characterized by a shift towards non-native H. incrassata beds, while no effect on B. podas. The introduction of the tropical seaweed H. incrassata has led to the re-distribution of X. novacula, potentially by harbouring a greater biodiversity of species at the base of the food-web through adding biogenic structure to an otherwise bare sediment. Our work demonstrates that non-native tropical habitat-forming species have the potential to maintain or even enhance fish abundance in unconsolidated bottoms in temperate areas potentially altering the functioning of native habitats. [ABSTRACT FROM AUTHOR]

Abstract: Operation and maintenance is one of the main cost drivers of modern wind farms and has become an emerging field of research over the past years. Understanding the failure behaviour of wind turbines (WTs) can significantly enhance operation and maintenance processes and is essential for developing reliability and strategic maintenance models. Previous research has shown that especially the environmental conditions, to which the turbines are exposed to, affect their reliability drastically. This paper compares several advanced modelling techniques and proposes a novel approach to model WT system and component failures based on the site‐specific weather conditions. Furthermore, to avoid common problems in failure modelling, procedures for variable selection and complexity reduction are discussed and incorporated. This is applied to a big failure database comprised of 11 wind farms and 383 turbines. The results show that the model performs very well in several situations such as modelling general WT failures as well as failures of specific components. The latter is exemplified using gearbox failures. [ABSTRACT FROM AUTHOR]

We establish a zero-inflated (random-effects) logistic-Gaussian model for clustered binary data in which members of clusters in one latent class have a zero response with probability one, and members of clusters in a second latent class yield correlated outcomes. Response probabilities in terms of random-effects models are formulated, and maximum marginal likelihood estimation procedures based on Gaussian quadrature are developed. Application to esophageal cancer data in Chinese families is presented. [ABSTRACT FROM AUTHOR]

Presently, concentrations of fecal indicator bacteria (FIB) in raw water sources are not known before water undergoes treatment, since analysis takes approximately 24 h to produce results. Using data on water quality and environmental variables, models can be used to predict real time concentrations of FIB in raw water. This study evaluates the potentials of zero-inflated regression models (ZI), Random Forest regression model (RF) and adaptive neuro-fuzzy inference system (ANFIS) to predict the concentration of FIB in the raw water source of a water treatment plant in Norway. The ZI, RF and ANFIS faecal indicator bacteria predictive models were built using physico-chemical (pH, temperature, electrical conductivity, turbidity, color, and alkalinity) and catchment precipitation data from 2009 to 2015. The study revealed that pH, temperature, turbidity, and electrical conductivity in the raw water were the most significant factors associated with the concentration of FIB in the raw water source. Compared to the other models, the ANFIS model was superior (Mean Square Error = 39.49, 0.35, 0.09, 0.23 CFU/100 ml respectively for coliform bacteria, E. coli , Intestinal enterococci and Clostridium perfringens ) in predicting the variations of FIB in the raw water during model testing. However, the model was not capable of predicting low counts of FIB during both training and testing stages of the models. The ZI and RF models were more consistent when applied to testing data, and they predicted FIB concentrations that characterized the observed FIB concentrations. While these models might need further improvement, results of this study indicate that ZI and RF regression models have high prospects as tools for the real-time prediction of FIB in raw water sources for proactive microbial risk management in water treatment plants. [ABSTRACT FROM AUTHOR]

Abstract: We analyze the effects of opportunistic and partisan politics on the licensing of construction activities, which in turn determines the level of housing supply. In line with Political Business Cycle theory, we hypothesize that the municipal incumbent may manipulate the supply of construction permits before (general and local) elections in order to boost economic activity and voter satisfaction, or to accommodate special‐interest groups. Our findings, based on time‐series data from post‐socialist Tirana (Albania), are consistent with opportunistic and partisan incentives’ creating cycle effects in the licensing of construction permits. However, we find that the direction of opportunistic election cycles depends critically on the interaction between the municipal incumbent and the central‐level government. Our paper raises important questions about the effects of transition politics on spatial development in post‐socialist cities. [ABSTRACT FROM AUTHOR]

Summary: Distance‐based ordination methods, such as principal coordinates analysis (PCoA), are widely used in the analysis of microbiome data. However, these methods are prone to pose a potential risk of misinterpretation about the compositional difference in samples across different populations if there is a difference in dispersion effects. Accounting for high sparsity and overdispersion of microbiome data, we propose a GLM‐based Ordination Method for Microbiome Samples (GOMMS) in this article. This method uses a zero‐inflated quasi–Poisson (ZIQP) latent factor model. An EM algorithm based on the quasi‐likelihood is developed to estimate parameters. It performs comparatively to the distance‐based approach when dispersion effects are negligible and consistently better when dispersion effects are strong, where the distance‐based approach sometimes yields undesirable results. The estimated latent factors from GOMMS can be used to associate the microbiome community with covariates or outcomes using the standard multivariate tests, which can be investigated in future confirmatory experiments. We illustrate the method in simulations and an analysis of microbiome samples from nasopharynx and oropharynx. [ABSTRACT FROM AUTHOR]

Abstract: Trophic interactions and environmental conditions determine the structure of food webs and the host expansion of parasitoids into novel insect hosts. In this study, we investigate plant–insect–parasitoid food web interactions, specifically the effect of trophic resources and environmental factors on the presence of the parasitoids expanding their host range after the invasion of Chrysodeixis chalcites (Esper) (Lepidoptera: Noctuidae). We also consider potential candidates for biological control of this non‐native pest. A survey of larval stages of Plusiinae (Lepidoptera: Noctuidae) and their larval parasitoids was conducted in field and vegetable greenhouse crops in 2009 and 2010 in various locations of Essex and Chatham‐Kent counties in Ontario, Canada. Twenty‐one plant–host insect–host parasitoid associations were observed among Trichoplusia ni (Hübner) (Lepidoptera: Noctuidae), C. chalcites, and larval parasitoids in three trophic levels of interaction. Chrysodeixis chalcites, an old‐world species that had just arrived in the region, was the most common in our samples. The larval parasitoids Campoletis sonorensis (Cameron) (Hymenoptera: Ichneumonidae), Cotesia vanessae (Reinhard), Cotesia sp., Microplitis alaskensis (Ashmead), and Meteorus rubens (Nees) (all Hymenoptera: Braconidae) expanded their host range into C. chalcites changing the structure of the food web. Copidosoma floridanum (Ashmead) (Hymenoptera: Encyrtidae) was the most common parasitoid of T. ni that was not found in the invasive species. Plant species, host abundance, and agro‐ecosystem were the most common predictors for the presence of the parasitoids expanding their host range into C. chalcites. Our results indicate that C. sonorensis, C. vanessae, and C. floridanum should be evaluated for their potential use in biological control of C. chalcites and T. ni. [ABSTRACT FROM AUTHOR]

Simplex distribution has been proved useful for modelling double-bounded variables in data directly. Yet, it is not sufficient for multimodal distributions. This article addresses the problem of estimating a density when data is restricted to the (0,1) interval and contains several modes. Particularly, we propose a simplex mixture model approach to model this kind of data. In order to estimate the parameters of the model, an Expectation Maximization (EM) algorithm is developed. The parameter estimation performance is evaluated through simulation studies. Models are explored using two real datasets: i) gene expressions data of patients’ survival times and the relation to adenocarcinoma and ii) magnetic resonant images (MRI) with a view in segmentation. In the latter case, given that data contains zeros, the main model is modified to consider the zero-inflated setting. [ABSTRACT FROM AUTHOR]