A fresh imputing survey methodology using sensible constraints on study and auxiliary variables: dubious random non-response

In this paper, we introduce a fresh methodology for imputing missing values by making use of sensible constraints on both a study variable and auxiliary variables that are correlated with the variable of interest. The resultant estimator based on these imputed values is shown to lead to the regression type method of imputation in survey sampling. Furthermore, when the data are hybrid of both that missing at random and missing complexly at random, the resultant estimator is shown to be a consistent estimator that has asymptotic mean squared error equal to that of the linear regression method of imputation. A generalization to any type of method of imputation is possible and has been included at the end.     

Inequality and Growth in Tunisia: New Evidence from Threshold Regression

Social Indicators Research - This paper uses a threshold regression model to test for threshold effect in the relationship between income inequality and growth for Tunisia over the period...     

No Contagion,Only Interdependence During the US Sub-Primes Crisis

In this paper, we have interested to testing the contagion exists that is caused by the sub-prime crisis, in order to distinguish between the cases of interdependences and “shift contagion” during this crisis. The analysis was conducted on a sample of four highly transition countries: Brazil, Russia, India and china, and the market source of crisis, taking into consideration the index as an aggregate indicator of financial market. The study of contagion is based on the tests of adjusted correlation coefficient and the non-linear error correction models. Indeed, these techniques reject the contagion hypothesis between these markets in order to conclude “no contagion, only interdependence”.     

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Predictive modelling provides a way to increase profitability and customer satisfaction in the financial services sector. Following on from Career Story , Omar Mohamed describes the successful use of this technique in recent marketing activities for the M&S "&More" credit card.     

Preservation properties for the Laplace transform ordering of residual lives

Several preservation results for the Laplace transform ordering of residual lives (Lt−rl) are given. In particular, we show that theTt−rl-order is preserved under convolutions, mixtures and weak convergence. Some examples of interest in reliability theory are also presented.     

Study of Cases of Child Sexual Abuse Referred for Medico-Legal Examination in Cairo and Giza,Egypt,2007–2011

ABSTRACT

Child sexual abuse is a complex problem that results in detrimental effects on its victims. This study aimed to investigate the pattern of child sexual abuse in cases referred for medico-legal examination in Cairo and Giza governorates in the period between 2007–2011. The study included 813 cases of child sexual abuse. Female gender was more predominant than male gender (52.8%, 4.29 cases), and adolescents were the most frequent age group identified as victims of sexual abuse (43.8%, 356 cases). All perpetrators were male and most of them were persons outside the victims’ families (89.9%, 731 cases). It was recommended to teach and encourage children to tell their caregivers if they have been assaulted and to educate parents and caregivers on how to respond when the child discloses sexual abuse.     

Comparative Study of Blu and ML Estimators of Location and Scale Parameters of an Extreme Value Distribution for Small Samples and Type I Censorship

The Maximum Likelihood (ML) and Best Linear Unbiased (BLU) estimators of the location and scale parameters of an extreme value distribution (Lawless [1982]) are compared under conditions of small sample sizes and Type I censorship. The comparisons were made in terms of the mean square error criterion. According to this criterion, the ML estimator of σ in the case of very small sample sizes (n < 10) and heavy censorship (low censoring time) proved to be more efficient than the corresponding BLU estimator. However, the BLU estimator for σ attains parity with the corresponding ML estimator when the censoring time increases even for sample sizes as low as 10. The BLU estimator of σ attains equivalence with the ML estimator when the sample size increases above 10, particularly when the censoring time is also increased. The situation is reversed when it came to estimating the location parameter μ, as the BLU estimator was found to be consistently more efficient than the ML estimator despite the improved performance of the ML estimator when the sample size increases. However, computational ease and convenience favor the ML estimators.     

An EM-Based Viterbi Approximation Algorithm for Mixed-State Latent Factor Models

In this article, a state-space model based on an underlying hidden Markov chain model (HMM) with factor analysis observation process is introduced. The HMM generates a piece-wise constant state evolution process and the observations are produced from the state vectors by a conditionally heteroscedastic factor analysis observation process. More specifically, we concentrate on situations where the factor variances are modeled by univariate Generalized Quadratic Autoregressive Conditionally Heteroscedastic processes (GQARCH). An expectation maximization (EM) algorithm combined with a mixed-state version of the Viterbi algorithm is derived for maximum likelihood estimation. The various regimes, common factors, and their volatilities are supposed unobservable and the inference must be carried out from the observable process. Extensive Monte Carlo simulations show promising results of the algorithms, especially for segmentation and tracking tasks.     

Estimation of P(Y < X) for the Three-Parameter Generalized Exponential Distribution

This article considers the estimation of R = P(Y < X) when X and Y are distributed as two independent three-parameter generalized exponential (GE) random variables with different shape parameters but having the same location and scale parameters. A modified maximum likelihood method and a Bayesian technique are used to estimate R on the basis of independent complete samples. The Bayes estimator cannot be obtained in explicit form, and therefore it has been determined using an importance sampling procedure. An analysis of a real life data set is presented for illustrative purposes.