A robust version of the hurdlemodel

The excess of zeros is not a rare feature in count data. Statisticians advocate the Poisson-type hurdle model (among other techniques) as an interesting approach to handle this data peculiarity. However, the frequency of gross errors and the complexity intrinsic to some considered phenomena may render this classical model unreliable and too limiting. In this paper, we develop a robust version of the Poisson hurdle model by extending the robust procedure for GLM of Cantoni and Ronchetti (2001) to the truncated Poisson regression model. The performance of the new robust approach is then investigated via a simulation study, a real data application and a sensitivity analysis. The results show the reliability of the new technique in the neighborhood of the truncated Poisson model. This robust modelling approach is therefore a valuable complement to the classical one, providing a tool for reliable statistical conclusions and to take more effective decisions.     

Implicit Estimation for the Stochastic Volatility Model

Efficient, accurate, and fast Markov Chain Monte Carlo estimation methods based on the Implicit approach are proposed. In this article, we introduced the notion of Implicit method for the estimation of parameters in Stochastic Volatility models.

Implicit estimation offers a substantial computational advantage for learning from observations without prior knowledge and thus provides a good alternative to classical inference in Bayesian method when priors are missing.

Both Implicit and Bayesian approach are illustrated using simulated data and are applied to analyze daily stock returns data on CAC40 index.     

A control chart for monitoring process variation using multiple dependent state sampling

A control chart for monitoring process variation by using multiple dependent state (MDS) sampling is constructed in the present article. The operational formulas for in-control and out-of-control average run lengths (ARLs) are derived. Control constants are established by considering the target in-control ARL at a normal process. The extensive ARL tables are reported for various parameters and shifted values of process parameters. The performance of the proposed control chart has been evaluated with several existing charts in regard of ARLs, which empowered the presented chart and proved far better for timely detection of assignable causes. The application of the proposed concept is illustrated with a real-life industrial example and a simulation-based study to elaborate strength of the proposed chart over the existing concepts.     

Recursive distribution estimator defined by stochastic approximation method using Bernstein polynomials

We propose a recursive distribution estimator using Robbins-Monro's algorithm and Bernstein polynomials. We study the properties of the recursive estimator, as a competitor of Vitale's distribution estimator. We show that, with optimal parameters, our proposal dominates Vitale's estimator in terms of the mean integrated squared error. Finally, we confirm theoretical result throught a simulation study.     

Estimating the Standard Deviation From the Range: a Replication of Analysis of Demographic Data Reported in Marriage & Family Review, 2016-2017

Abstract

A major concern in the social sciences is lack of replication of previous studies. An important methodological concern in the social sciences is the ability to determine effect sizes in addition to statistical significance levels. Effect sizes cannot be easily calculated in the absence of sufficient data; usually standard deviations are needed. If standard deviations are not available, how can they be estimated? Various proposals have been offered to solve this question. One solution is to divide the range (maximum–minimum) by four; a variety of more complicated solutions, based on sample size or the skew of the variable’s distribution, have been suggested (Schumm, Higgins, et al., 2017). Here, 30 cases involving the demographic variable of age, from 23 articles published in Marriage & Family Review between 2016 and 2017, are assessed to replicate the previous report of Schumm, Higgins et al. (2017). Our results indicated that both linear and power functions significantly predicted the size of standard deviations, with larger samples featuring smaller standard deviations. Aside from sample size, the best solution appears to be to divide the range by 4.5–5.0; although for very small samples (N?<?50), it is probably better to divide by 3.5–4.0 whereas for larger samples, especially those that involve higher levels of skew, it may be better to divide by 5.0 or higher. The Wan et al. (2014) estimation procedure appears to be approximately a power function of sample size. For samples up to several thousand in size, the range of divisors appears to run between 3.0 and 8.0, extremes that could be used to determine the largest or smallest possible standard deviations, respectively. Values far below 3.0 or above 8.0 may reflect typographical errors in data reports or possibly be evidence of artificially generated data, if not scientific fraud. When a variable is split into subsamples, its standard deviations should usually increase for the subsamples compared with the total sample. Similar assessments remain in progress for non-demographic variables in social sciences.     

Coalition Formation and Cost Allocation for Joint Replenishment Systems

This paper focuses on the issues of coalition formation and cost allocation in a joint replenishment system involving a set of independent and freely interacting retailers purchasing an item from one supplier to meet a deterministic demand. The papers dealing with this problem are mainly focused on supperadditive games, where the cost savings associated with a coalition increase with the number of players in the coalition. The most relevant question addressed then is how to allocate the savings to the players. In this paper, we propose to go further by dealing with a non‐supperadditive game, where a set of independent retailers have the common understanding to share the cost savings according to the cost‐based proportional rule. In this setting, the global cost optimization is no longer a relevant approach to identify appealing coalitions for any retailer. Here, we provide an iterative procedure to form the so‐called efficient coalition structure and we show that this coalition structure has the nice properties of being (i) weakly stable in the sense of the coalition structure core and (ii) strongly stable under a given assumption. An exact fractional programming based solution is also given to generate such efficient coalitions.     

A doubly-inflated Poisson regression for correlated count data

Count data have emerged in many applied research areas. In recent years, there has been a considerable interest in models for count data. In modelling such data, it is common to face a large frequency of zeroes. The data are regarded as zero-inflated when the frequency of observed zeroes is larger than what is expected from a theoretical distribution such as Poisson distribution, as a standard model for analysing count data. Data analysis, using the simple Poisson model, may lead to over-dispersion. Several classes of different mixture models were proposed for handling zero-inflated data. But they do not apply to cases when inflated counts happen at some other points, in addition to zero. In these cases, a doubly-inflated Poisson model has been suggested which only be used for cross-sectional data and cannot consider correlations between observations. However, correlated count data have a large application, especially in the health and medical fields. The present study aims to introduce a Doubly-Inflated Poisson models with random effect for correlated doubly-inflated data. Then, the best performance of the proposed method is shown via different simulation scenarios. Finally, the proposed model is applied to a dental study.KEYWORDS: Count data, doubly-inflated, Poisson regression, zero-inflated, correlated data     

Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model

ABSTRACT

Conditional risk measuring plays an important role in financial regulation and depends on volatility estimation. A new class of parameter models called Generalized Autoregressive Score (GAS) model has been successfully applied for different error's densities and for different problems of time series prediction in particular for volatility modeling and VaR estimation. To improve the estimating accuracy of the GAS model, this study proposed a semi-parametric method, LS-SVR and FS-LS-SVR applied to the GAS model to estimate the conditional VaR. In particular, we fit the GAS(1,1) model to the return series using three different distributions. Then, LS-SVR and FS-LS-SVR approximate the GAS(1,1) model. An empirical research was performed to illustrate the effectiveness of the proposed method. More precisely, the experimental results from four stock indexes returns suggest that using hybrid models, GAS-LS-SVR and GAS-FS-LS-SVR provides improved performances in the VaR estimation.     

When and Where Global meets Local: A multilevel analysis of determinants of social trust in the Emirate of Abu Dhabi

Abu Dhabi's transformation to a modern society in recent decades provides an ideal context to explore the interplay of tradition, transition, and modernization at various levels, which shapes the trajectory of the development of social trust. This study offers multilevel analyses of the effects of social, psychological, and ethnical factors on social trust by using data from Abu Dhabi General Social Survey conducted in 2018. The results sustain the validity of both social capital and social network theories in explaining social trust. Contrary to the findings in other Middle East countries, in Abu Dhabi, the full‐time employed and people who are more satisfied with their household income tend to show a higher level of trust. A negative but insignificant relationship is found between community ethnic fragmentation and social trust.