Efficiency of ranked set sampling in tests for normality

In this article, we consider the ranked set sampling (RSS) and investigate seven tests for normality under RSS. Each test is described and then power of each test is obtained by Monte Carlo simulations under various alternatives. Finally, the powers of the tests based on RSS are compared with the powers of the tests based on the simple random sampling and the results are discussed.     

Testing the validity of the logistic model based on the empirical distribution function

The logistic distribution is one of the fundamental distribution and is widely used for describing model growth curves in survival analysis and biological studies. Applications of this distribution are presented in statistical literature. In this article, goodness of fit tests for the logistic distribution based on the empirical distribution function (EDF) are considered. In order to compute the test statistics, because the MLEs cannot be obtained explicitly, we use the approximate maximum likelihood estimates (AMLEs) suggested by Balakrishnan and Cohen (1990 Balakrishnan, N., Cohen, A. C. (1990). Order Statistics and Inference: Estimation Methods. Boston: Academic Press. [Google Scholar]), which are simple explicit estimators. Power comparisons of the considered tests are carried out via simulations. Finally, two illustrative examples are presented and analyzed.     

The relationship between environmental exposures and hormonal abnormalities in pregnant women: An epidemiological study in Yazd,Iran

Problem

The process of industrialization and lifestyle changes have gradually exposed human ?societies to a larger number of environmental risk factors, which may cause hormonal ?abnormalities and congenital anomalies.

Gender as a Moderator of the Relation Among Social Support Functions and Life Satisfaction in Older Malaysians

The aim of this study was to examine the moderating role of gender on relations among social support functions and life satisfaction in older Malaysians. The study sample was 1,800 older residents in a community; all were at least 60 years old. This study was a cross-sectional and corelational survey, and the data were collected by multistage stratified sampling. This study revealed that fewer social support functions, and therefore less life satisfaction, were available for females than for males. The results of moderated regression analyses demonstrated that gender interacted only on the relationship between positive social interaction support and tangible support with life satisfaction. Specifically, at a high tangible support level, females had lower life satisfaction when compared to a low tangible support level. There may be a need for new programs and services to provide other aspects of social support to older female adults to improve and maintain life satisfaction in later life.     

Tests of goodness of fit based on Phi-divergence

In this paper, we introduce a general goodness of fit test based on Phi-divergence. Consistency of the proposed test is established. We then study some special cases of tests for normal, exponential, uniform and Laplace distributions. Through Monte Carlo simulations, the power values of the proposed tests are compared with some known competing tests under various alternatives. Finally, some numerical examples are presented to illustrate the proposed procedure.     

Gini index based goodness-of-fit test for the logistic distribution

To model growth curves in survival analysis and biological studies the logistic distribution has been widely used. In this article, we propose a goodness-of-fit test for the logistic distribution based on an estimate of the Gini index. The exact distribution of the proposed test statistic and also its asymptotic distribution are presented. In order to compute the proposed test statistic, parameters of the logistic distribution are estimated by approximate maximum likelihood estimators (AMLEs), which are simple explicit estimators. Through Monte Carlo simulations, power comparisons of the proposed test with some known competing tests are carried. Finally, an illustrative example is presented and analyzed.     

Identification of multiple high leverage points in logistic regression

Leverage values are being used in regression diagnostics as measures of unusual observations in the X-space. Detection of high leverage observations or points is crucial due to their responsibility for masking outliers. In linear regression, high leverage points (HLP) are those that stand far apart from the center (mean) of the data and hence the most extreme points in the covariate space get the highest leverage. But Hosemer and Lemeshow [Applied logistic regression, Wiley, New York, 1980] pointed out that in logistic regression, the leverage measure contains a component which can make the leverage values of genuine HLP misleadingly very small and that creates problem in the correct identification of the cases. Attempts have been made to identify the HLP based on the median distances from the mean, but since they are designed for the identification of a single high leverage point they may not be very effective in the presence of multiple HLP due to their masking (false–negative) and swamping (false–positive) effects. In this paper we propose a new method for the identification of multiple HLP in logistic regression where the suspect cases are identified by a robust group deletion technique and they are confirmed using diagnostic techniques. The usefulness of the proposed method is then investigated through several well-known examples and a Monte Carlo simulation.     

How fair can employers be?

In this article we examine three concepts of fairness in employment decisions. Two of these concepts are widely known in the literature as “Fairness 1”and “Fairness 2”. The third concept, which we refer to as “Fairness 0”, is defined and introduced here. Fairness 0 applies to the hiring stage, whereas Fairness 1 and Fairness 2 apply to the placement or promotion stages of employment. Our results have important policy implications. We show that the three concepts of fairness can only rarely be achieved simultaneously.     

Some properties of,and relationships among,several uncorrelated and homoscedastic residual vectors

In this paper we examine the properties of four types of residual vectors, arising from fitting a linear regression model to a set of data by least squares. The four types of residuals are (i) the Stepwise residuals (Hedayat and Robson, 1970), (ii) the Recursive residuals (Brown, Durbin, and Evans, 1975), (iii) the Sequentially Adjusted residuals (to be defined herein), and (iv) the BLUS residuals (Theil, 1965, 1971). We also study the relationships among the four residual vectors. It is found that, for any given sequence of observations, (i) the first three sets of residuals are identical, (ii) each of the first three sets, being identical, is a member of Thei’rs (1965, 1971) family of residuals; specifically, they are Linear Unbiased with a Scalar covariance matrix (LUS) but not Best Linear Unbiased with a Scalar covariance matrix (BLUS). We find the explicit form of the transformation matrix and show that the first three sets of residual vectors can be written as an orthogonal transformation of the BLUS residual vector. These and other properties may prove to be useful in the statistical analysis of residuals.