Inferences on the Means of Two Log-Normal Distributions: A Computational Approach Test

In this article, we propose a novel approach for testing the equality of two log-normal populations using a computational approach test (CAT) that does not require explicit knowledge of the sampling distribution of the test statistic. Simulation studies demonstrate that the proposed approach can perform hypothesis testing with satisfying actual size even at small sample sizes. Overall, it is superior to other existing methods. Also, a CAT is proposed for testing about reliability of two log-normal populations when the means are the same. Simulations show that the actual size of this new approach is close to nominal level and better than the score test. At the end, the proposed methods are illustrated using two examples.     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

The identification of logistic regression models with errors in the variables

The simple logistic regression model with normal measurement error and normal regressor is shown to be identifiable without any extra information about the measurement error. The multiple logistic regression model with more than one regressor variable measured with error is not identifiable. If the covariance matrix of the measurement error is known up to a scalar factor, the model is identified. Further we discuss why in spite of the identifiability the models cannot be estimated in a reasonable way without extra information about the measurement error.     

On social work and what gender has got to do with it

Living in gendered societies makes it necessary to take into account the specific ways in which social problems and social issues affect the lives of women and men, girls and boys. Not only are social problems and social issues very often gender biased; this is also true for the professions dealing with these problems and issues, and for the training programmes for professions like social work. Sociological analysis of society and philosophical understanding of a good life are permeated by gender-specific perceptions -- often without those involved being aware of them. Furthermore, social policies reinforce gender-specific biases because they are based on gender-specific (i.e. male) standards. Gender as an analytical category has been brought into scientific and public discourse by feminist theoreticians and activists making women -- and thus also men -- visible with regard to structure and subject issues. The development of gender theory and research highlights the significance of gender arrangements to understand social phenomena and to support gender-equality ways of thinking and policies. At the same time, men and women are not only confronted with gendered structures, but via socialization processes and personal development they also reproduce these gender differences in their daily lives by 'doing gender'.     

Editorial

Ohne Zusammenfassung

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Editorial