Welfare gains of the poor: An endogenous Bayesian approach with spatial random effects

We introduce a Bayesian instrumental variable procedure with spatial random effects that handles endogeneity, and spatial dependence with unobserved heterogeneity. We find through a limited Monte Carlo experiment that our proposal works well in terms of point estimates and prediction. We apply our method to analyze the welfare effects generated by a process of electricity tariff unification on the poorest households. In particular, we deduce an Equivalent Variation measure where there is a budget constraint for a two-tiered pricing scheme, and find that 10% of the poorest municipalities attained welfare gains above 2% of their initial income.     

Sexual Dysfunction among Male Patients with Tramadol abuse: A Case-Control Study

Objective: Here we have investigated the association between tramadol abuse and male sexual function. Methods: Eighty-two male tramadol abuse patients and 55 healthy controls (20- to 45-years-old) were included in the study. Data from a sociodemographic questionnaire and the International Index of Erectile Function Questionnaire were collected and analyzed. Results: The tramadol abuse patients were 5 times more likely than their healthy counterparts to have erectile dysfunction. The tramadol abuse patients also had worse scores in the orgasmic, sexual desire, intercourse satisfaction, and overall satisfaction domains compared to the controls. Conclusion: Sexual dysfunctions are common among tramadol abuse patients.     

Balancing fidelity,contextualisation, and innovation: learning from an adaption of SASA! to prevent violence against women in the Dadaab refugee camp

ABSTRACT

This article focuses on SASA!, a community mobilisation approach that was developed in Uganda by Raising Voices, with the aim of preventing violence against women. SASA! proved effective in reducing intimate partner violence against women, and has since been used in over 25 countries worldwide. In this article, we draw on recent research into the International Refugee Committee’s implementation of SASA! in Dadaab, Kenya. In particular, we focus on how the refugee camp setting shapes the adaptation and delivery of the SASA! programme and explore the balance to be struck between fidelity to the SASA! methodology, and adaptations to make it suitable for use in this specific humanitarian context.     

Postponement implementation in integrated production and inventory plan under deterioration effects: a case study of a juice producer with limited storage capacity

Abstract

The aim of this study is to develop a production and inventory plan for a fresh apple juice producer. During harvest season, the producer extracts premium juice from freshly picked apples and keeps an inventory of these premium apples in his temperature-controlled storage facility. The producer extracts also regular juice from regular apples purchased from third-party storages. To satisfy future demand, the producer carries inventories of juice and apple. The levels of apple inventories are constrained by the producer’s limited storage space, but he can replenish regular apples. To assist the producer in facing demand uncertainty and deterioration of apples, we develop a production and inventory plan that incorporates postponement to mitigate demand uncertainty. As postponement decisions are shaped by the cost structure of inventories, we integrate in one model the postponement decisions and the deterioration mitigation decisions that can alter the inventory costs. We build multi-period stochastic programming with recourse model to determine juice batch sizes and apple inventories that maximize the producer’s expected profit. The optimal solution illustrates the use of time and form postponements. We discuss the interactions between postponement implementation and decisions to mitigate apples deterioration. We compare the production and inventory plans with and without postponement and with and without apple deterioration. We also present sensitivity analyses for the plan under varying inventory cost and storage space.     

Modelling Poisson marked point processes using bivariate mixture transition distributions

A class of bivariate continuous-discrete distributions is proposed to fit Poisson dynamic models in a single unified framework via bivariate mixture transition distributions (BMTDs). Potential advantages of this class over the current models include its ability to capture stretches, bursts and nonlinear patterns characterized by Internet network traffic, high-frequency financial data and many others. It models the inter-arrival times and the number of arrivals (marks) in a single unified model which benefits from the dependence structure of the data. The continuous marginal distributions of this class include as special cases the exponential, gamma, Weibull and Rayleigh distributions (for the inter-arrival times), whereas the discrete marginal distributions are geometric and negative binomial. The conditional distributions are Poisson and Erlang. Maximum-likelihood estimation is discussed and parameter estimates are obtained using an expectation–maximization algorithm, while the standard errors are estimated using the missing information principle. It is shown via real data examples that the proposed BMTD models appear to capture data features better than other competing models.     

A generalized binomial exponential 2 distribution: modeling and applications to hydrologic events

Developing statistical methods to model hydrologic events is always interesting for both statisticians and hydrologists, because of its importance in hydraulic structures design and water resource planning. Because of this, a flexible 3-parameter generalization of the exponential distribution is introduced based on the binomial exponential 2 (BE2) distribution [2 H.S. Bakouch, M. Aghababaei Jazi, S. Nadarajah, A. Dolati, and R. Roozegar, A lifetime model with increasing failure rate, Appl. Math. Model. 38 (2014), pp. 5392–5406. doi: 10.1016/j.apm.2014.04.028[Crossref], [Web of Science ®] , [Google Scholar]]. The proposed distribution involving the exponential, gamma and BE2 distributions as submodels; and it exhibits decreasing, increasing and bathtub-shaped hazard rates, so it turns out to be quite flexible for analyzing non-negative real life data. Some statistical properties, parameters estimation and information matrix of the distribution are investigated. The proposed distribution, Gumbel, generalized Logistic and other distributions are utilized to model and fit two hydrologic data sets. The distribution is shown to be more appropriate to the data than the compared distributions using the selection criteria: average scaled absolute error, Akaike information criterion, Bayesian information criterion and Kolmogorov–Smirnov statistics. As a result, some hydrologic parameters of the data are obtained such as return level, conditional mean, mean deviation about the return level and the rth moments of order statistics.     

Fitting probability distributions to economic growth: a maximum likelihood approach

The growth rate of the gross domestic product (GDP) usually carries heteroscedasticity, asymmetry and fat-tails. In this study three important and significantly heteroscedastic GDP series are examined. A Normal, normal-mixture, normal-asymmetric Laplace distribution and a Student's t-Asymmetric Laplace (TAL) distribution mixture are considered for distributional fit comparison of GDP growth series after removing heteroscedasticity. The parameters of the distributions have been estimated using maximum likelihood method. Based on the results of different accuracy measures, goodness-of-fit tests and plots, we find out that in the case of asymmetric, heteroscedastic and highly leptokurtic data the TAL-distribution fits better than the alternatives. In the case of asymmetric, heteroscedastic but less leptokurtic data the NM fit is superior. Furthermore, a simulation study has been carried out to obtain standard errors for the estimated parameters. The results of this study might be used in e.g. density forecasting of GDP growth series or to compare different economies.     

A discrete model for the segmentation of chains and strings

A statistical model is presented that can be used to represent a discrete breakage process. It considers a fixed length chain or string made up of n +1 pieces joined together. The chain is stressed at each connection or link andrupture occurs at some of the links. Models are developed to answer the question, “what is the expected proportion of chain segments of a given size?” The model is modified to handle those experiments where only thetotal weight of a given size segment is known. Expressions are obtained for the expected value and variance of the number of segments of a given size. The model is used to predict the expected number of segments which results when a fixed length DNA molecule chain is subjected to carcinogenic agents and is applied to industrial examples.     

Toward re-engineering models and algorithms of facility layout

Models and algorithms are continuously being developed for the facility layout problem in various manufacturing settings. However, there could be practices and obstacles that weaken them and adversely impact the effectiveness of the layout. Thus, they should be obliterated in order to advance the layout problem. This paper suggests a set of guidelines that are directed at the process inherent in developing layout models, algorithms, expert systems, and software applications to assist in improving them and developing better layouts. Such guidelines are lacking in the literature of facility layout. Examples on the suitability and applicability of the suggested guidelines are given.