Disparities in Gender Preference and Fertility: Southeast Asia and Latin America in a Comparative Perspective

Population Research and Policy Review - A preference for sons and a sex selection against females are widespread in vast regions of the world, including a great number of Asian and East European...     

Minimum Φ-divergence estimator in logistic regression models

A general class of minimum distance estimators for logistic regression models based on the ϕ-divergence measures is introduced: The minimum ϕ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. Its asymptotic properties are studied as well as its behaviour in small samples throught a simulation study. This work was supported partially by Grant DGI (BMF2003-00892).     

Order-restricted Dose-related Trend Phi-divergence Tests for Generalized Linear Models

In this paper a new family of test statistics is presented for testing the independence between the binary response Y and an ordered categorical explanatory variable X (doses) against the alternative hypothesis of an increase dose-response relationship between a response variable Y and X (doses). The properties of these test statistics are studied. This new family of test statistics is based on the family of φ-divergence measures and contains as a particular case the likelihood ratio test. We pay special attention to the family of test statistics associated with the power divergence family. A simulation study is included in order to analyze the behavior of the power divergence family of test statistics.     

Tests for the variance parameter in the Fay–Herriot model

The Fay–Herriot model is a linear mixed model that plays a relevant role in small area estimation (SAE). Under the SAE set-up, tools for selecting an adequate model are required. Applied statisticians are often interested on deciding if it is worthwhile to use a mixed effect model instead of a simpler fixed-effect model. This problem is not standard because under the null hypothesis the random effect variance is on the boundary of the parameter space. The likelihood ratio test and the residual likelihood ratio test are proposed and their finite sample distributions are derived. Finally, we analyse their behaviour under simulated scenarios and we also apply them to real data.     

More than an urban legend: the short- and long-run effects of unplanned fertility shocks

This paper examines the short- and long-run effects of a quasi-exogenous variation in fertility behavior due to a yearlong period of power rationing in Colombia in 1992. We show that power shortages caused a mini baby boom and that the increase in fertility was unplanned and persistent: the time in between births was reduced and overall lifetime fertility increased. We also present evidence suggesting that women who had a baby due to the outage found themselves in worse socioeconomic conditions 12 years later.     

A comparison of uniformity tests

Problems of goodness-of-fit to a given distribution can usually be reduced to test uniformity. The uniform distribution appears due to natural random events or due to the application of methods for transforming samples from any other distribution to the samples with values uniformly distributed in the interval (0, 1). Thus, one can solve the problem of testing if a sample comes from a given distribution by testing whether its transformed sample is distributed according to the uniform distribution. For this reason, the methods of testing for goodness-of-fit to a uniform distribution have been widely investigated. In this paper, a comparative power analysis of a selected set of statistics is performed in order to give suggestions on which one to use for testing uniformity against the families of alternatives proposed by Stephens [Stephens, M.A., 1974, EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69, 730–737.]. Definition and some relevant features of the considered test statistics are given in section 1. Implemented numerical processes to calculate percentage points of every considered statistic are described in section 2. Finally, a Monte Carlo simulation experiment has been carried out to fulfill the mentioned target of this paper.     

Asymptotic distributions of weighted divergence between discrete distributions

A divergence measure between discrete probability distributions introduced by Csiszar (1967) generalizes the Kullback-Leibler information and several other information measures considered in the literature. We introduce a weighted divergence which generalizes the weighted Kullback-Leibler information considered by Taneja (1985). The weighted divergence between an empirical distribution and a fixed distribution and the weighted divergence between two independent empirical distributions are here investigated for large simple random samples, and the asymptotic distributions are shown to be either normal or equal to the distribution of a linear combination of independent X²-variables     

Small-sample comparisons for the Rukhin goodness-of-fit-statistics

Rukhin's statistic family for goodness-of-fit, under the null hypothesis, has asymptotic chi-squared distribution; however, for small samples the chi-squared approximation in some cases does not well agree with the exact distribution. In this paper we consider this approximation and other three to get appropriate test levels in comparison with the exact level. Moreover, exact power comparisons for several values of the parameter under specified alternatives provide that the classical Pearson's statistic, obtained as a particular case of Rukhin statistic, can be improved by choosing other statistics from the family. An explanation is proposed in terms of the effects of individual cell frequencies on the Rukhin statistic. This work was supported in part by the DGCYT grants No. PR156/97-7159 and PB96-0635     

The future for wool

Single dimensional projections are inadequate to determine what the future holds. A wide variety of economic, technological and social changes will influence the future environment for wool and other fibres. In these circumstances, and bearing in mind that, for planning purposes, strategic assumptions are often markedly superior to firm predictions, a set of alternative scenarios is a useful instrument. Consideration of some brief scenarios leads to the conclusion that wool's future depends, in no small measure, upon the actions of the wool-growing industry and governments of wool exporting countries.     

Empirical phi-divergence test statistics for testing simple and composite null hypotheses

The main purpose of this paper is to introduce first a new family of empirical test statistics for testing a simple null hypothesis when the vector of parameters of interest is defined through a specific set of unbiased estimating functions. This family of test statistics is based on a distance between two probability vectors, with the first probability vector obtained by maximizing the empirical likelihood (EL) on the vector of parameters, and the second vector defined from the fixed vector of parameters under the simple null hypothesis. The distance considered for this purpose is the phi-divergence measure. The asymptotic distribution is then derived for this family of test statistics. The proposed methodology is illustrated through the well-known data of Newcomb's measurements on the passage time for light. A simulation study is carried out to compare its performance with that of the EL ratio test when confidence intervals are constructed based on the respective statistics for small sample sizes. The results suggest that the ‘empirical modified likelihood ratio test statistic’ provides a competitive alternative to the EL ratio test statistic, and is also more robust than the EL ratio test statistic in the presence of contamination in the data. Finally, we propose empirical phi-divergence test statistics for testing a composite null hypothesis and present some asymptotic as well as simulation results for evaluating the performance of these test procedures.