Asymptotic properties of the maximum-likelihood estimator in zero-inflated binomial regression

The zero-inflated binomial (ZIB) regression model was proposed to account for excess zeros in binomial regression. Since then, the model has been applied in various fields, such as ecology and epidemiology. In these applications, maximum-likelihood estimation (MLE) is used to derive parameter estimates. However, theoretical properties of the MLE in ZIB regression have not yet been rigorously established. The current paper fills this gap and thus provides a rigorous basis for applying the model. Consistency and asymptotic normality of the MLE in ZIB regression are proved. A consistent estimator of the asymptotic variance–covariance matrix of the MLE is also provided. Finite-sample behavior of the estimator is assessed via simulations. Finally, an analysis of a data set in the field of health economics illustrates the paper.     

Job Satisfaction in the Coastal Pelagic Fisheries of Senegal

The marine fishery in Senegal, West Africa, is a major source of employment and food security. It currently faces the consequences of ecological degradation. This paper examines job satisfaction among small-scale purse seine fishers, who constitute one of the dominant fishing métiers in Senegal. The research sample consists of 80 purse seine fishers in three locations along the coast with varied degrees of urbanisation. The findings suggest that purse seine fishers are generally satisfied with the extent to which the fishery meets their Social Needs and Self-Actualisation. They are dissatisfied, however, with the degree to which Basic Needs are met, with the way in which Management is carried out, and??to a lesser extent??with the condition of the natural environment. However, the study shows significant differences between the more rural and urbanised settlements. These are most pronounced with regard to the willingness to change fishing type, move to another profession or advise a young person to enter fishing.     

Estimation in zero-inflated binomial regression with missing covariates

We investigate inverse-probability-weighted (IPW) maximum likelihood estimation in zero-inflated binomial regression with missing-at-random covariates. Large sample properties (consistency, asymptotic normality) of the IPW estimator are established. Finite sample properties are assessed via simulations. The methodology is illustrated on a real data set.     

Simulation-based Inference in a Zero-inflated Bernoulli Regression Model

The logistic regression model has become a standard tool to investigate the relationship between a binary outcome and a set of potential predictors. When analyzing binary data, it often arises that the observed proportion of zeros is greater than expected under the postulated logistic model. Zero-inflated binomial (ZIB) models have been developed to fit binary data that contain too many zeros. Maximum likelihood estimators in these models have been proposed and their asymptotic properties established. Several aspects of ZIB models still deserve attention however, such as the estimation of odds-ratios and event probabilities. In this article, we propose estimators of these quantities and we investigate their properties both theoretically and via simulations. Based on these results, we provide recommendations about the range of conditions (minimum sample size, maximum proportion of zeros in excess) under which a reliable statistical inference on the odds-ratios and event probabilities can be obtained in a ZIB regression model. A real-data example illustrates the proposed estimators.     

Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations

We carry out finite sample size parameter estimation methods for long-memory parameters of the class of seasonal fractional ARIMA with stable innovations. In particular, we consider the semiparametric method studied in Reisen et al. (2006) [27] and two Whittle approaches: the classical Whittle method and a method based on a Markov Chains Monte Carlo (MCMC) procedure. The performance of the methods is discussed using a Monte Carlo simulation.     

Consistency of a nonparametric conditional mode estimator for random fields

Given a stationary multidimensional spatial process $\left\{ Z_{\mathbf{i}}=\left( X_{\mathbf{i}},\ Y_{\mathbf{i}}\right) \in \mathbb R ^d\right. \left. \times \mathbb R ,\mathbf{i}\in \mathbb Z ^{N}\right\} $ , we investigate a kernel estimate of the spatial conditional mode function of the response variable $Y_{\mathbf{i}}$ given the explicative variable $X_{\mathbf{i}}$ . Consistency in $L^p$ norm and strong convergence of the kernel estimate are obtained when the sample considered is a $\alpha $ -mixing sequence. An application to real data is given in order to illustrate the behavior of our methodology.