The Sexual Body of the Young Jew as an Arena of Ideological Struggle, 1821–1948

This article analyzes professional discourse on masturbation to illustrate how child sexuality serves as a site for symbolic ideological struggles. Sex instruction manuals written in central Europe in the nineteenth century Palestine and Israel in the twentieth century are the basis for broader discussions on religious and scientific discourse on child and adolescent sexuality within the Jewish communities. By tracing the development of new forms of expert knowledge, we show how expert discourse on masturbation gradually transformed it from a symbolic moral evil into a medical disease and a psychological problem, before declaring it a legitimized behavior.     

ON THE DESIGN OF HIERARCHICAL PRODUCTION PLANNING SYSTEMS

To provide effective managerial support for decisions related to production planning and scheduling processes, it is useful to partition the set of decisions into a hierarchical framework. In the resulting system, higher level decisions impose constraints on lower level actions, and lower level decisions provide the necessary feed-back to reevaluate higher level actions. The purpose of this paper is to suggest optimum procedures to deal with the resulting subproblems and to analyze the interaction mechanisms among the different hierarchical levels. Computational results are given.     

THE FAILURE TO PREDICT THE GREAT RECESSION—A VIEW THROUGH THE ROLE OF CREDIT

Much has been written about why economists failed to predict the latest crisis. Reading the literature, it seems that this crisis was so obvious that economists must have been blind not to see it coming. We approach this failure by looking at one of the key variables in this analysis, the evolution of credit. We compare the conclusions reached in the recent literature with those that could have been drawn from an ex‐ante analysis. We show that the effect of credit on the business cycle cannot be exploited from a policymaker's point of view.     

A trichotomic segmentation approach for estimating composite indicators

Composite indicators (CIs) are commonly used for benchmarking of countries over the years, summarizing in a single measurement, complex social, economic, environmental etc. concepts by involving several thematically related sub-indicators. When estimating  CIs and for a few specific countries, it is possible to have a strong indication and belief about their performance, prior to obtaining their scores. Based on that, a number of countries are likely to occupy the top-ranking positions; some will remain at the bottom list, while others may range in intermediate places. This initial preference information imposes a trichotomic segmentation that divides the countries under assessment into categories of superior, inferior and of ambiguous future performance. In this paper, we introduce the trichotomic segmentation as initial preference information to estimate the values of the CIs. We build on the popular Benefit of the Doubt (BoD) method with common weights and develop a two-goal linear programming model, that next to the evaluation of the common weights for the sub-indicators, estimates cut-off points for the CI values that distinguish the superior and inferior countries. The proposed model maintains the advantages of the common variable weighting and produces scores that induce better discrimination of the countries, having also a significant correlation with the original CI scores. The proposed methodology is applied to reassess the Digital Economy and the Society Index.

     

A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models

When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been applied to UCM; however, in most cases, these procedures are unable to identify the heteroscedastic component correctly. The main issue is that test statistics are affected by the presence of serial correlation, causing the distribution of the statistic under conditional homoscedasticity to remain unknown. We propose a nonparametric statistic for testing heteroscedasticity based on the well-known Wilcoxon''s rank statistic. We study the asymptotic validation of the statistic and examine bootstrap procedures for approximating its finite sample distribution. Simulation results show an improvement in the size of the homoscedasticity tests and a power that is clearly comparable with the best alternative in the literature. We also apply the test on real inflation data. Looking for the presence of a conditionally heteroscedastic effect on the error terms, we arrive at conclusions that almost all cases are different than those given by the alternative test statistics presented in the literature.     

Copula structure analysis

Summary.  We extend the standard approach of correlation structure analysis for dimension reduction of high dimensional statistical data. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a model for the copula. For elliptical copulas a correlation-like structure remains, but different margins and non-existence of moments are possible. After introducing the new concept and deriving some theoretical results we observe in a simulation study the performance of the estimators: the theoretical asymptotic behaviour of the statistics can be observed even for small sample sizes. Finally, we show our method at work for a financial data set and explain differences between our copula-based approach and the classical approach. Our new method yielear models also.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

<Emphasis Type="BoldItalic">MAD-STEC</Emphasis>: a method for multiple automatic detection of space-time emerging clusters

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.