Variations of Sexual Scripts Relating to Concurrency by Race,Class, and Gender in South Africa

It is unclear whether higher rates of sexual partner concurrency in Black South Africans are due to socioeconomic or cultural factors. We used a nationally representative sample of 9,728 individuals aged 16 to 55 from a study conducted in 2009 to examine how the norms pertaining to concurrency and the practice of concurrency vary by race, class, and gender. The percentage of men reporting point concurrency was 14%, 6.5%, and 2.5% in Blacks, coloreds, and Whites, respectively (p < 0.001). These percentages increased to 45.7%, 24.7%, and 11.7%, respectively, for those reporting lifetime concurrency (p < 0.001). In all the racial groups, men exhibited more favorable attitudes toward concurrency than women did. For a range of indicators, White men and women had less favorable attitudes toward concurrency than Black men and women. These differences remained after controlling for a range of confounding variables. In the adjusted logistic regression model, reported concurrency in men was associated with a younger age, Black race, being in the lowest income tertile, not being in a stable relationship, and expressing various positive attitudes toward concurrency.     

Statistical inference for α-series process with gamma distribution

The explicit estimators of the parameters α, μ?and?σ² are obtained by using the methodology known as modified maximum likelihood (MML) when the distribution of the first occurrence time of an event is assumed to be Weibull in series process. The efficiencies of the MML estimators are compared with the corresponding nonparametric (NP) estimators and it is shown that the proposed estimators have higher efficiencies than the NP estimators. In this study, we extend these results to the case, where the distribution of the first occurrence time is Gamma. It is another widely used and well-known distribution in reliability analysis. A real data set taken from the literature is analyzed at the end of the study for better understanding the methodology presented in this paper.     

The information for the direction of dependence in l1 regression

Fitting a linear regression for a response variable by minimising the sum of absolute deviations, L₁ regression, may be viewed as a maximum likelihood procedure applied to the Laplace distribution. An interesting bivariate case is where the conditional distribution of the response X₂ given X₁ and the marginal distribution of the explanatory variable X₁ are both Laplace. In this context we show there is information to distinguish the direction of dependence between X₁ and X₂ from observations. That is we may distinguish the model in which X₁ is dependent on X₂ from that in which X₂ is dependent on X₁ This is not true for L₂ regression based on the Normal distribution.