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...     

When Working Hard Is Not Enough for Female and Racial/Ethnic Minority Apprentices in the Highway Trades

Drawing on employment records, qualitative interviews, and a survey, we explore the experiences of apprentices in the highway trades in Oregon. We demonstrate that female and racial/ethnic minority apprentices have lower rates of recruitment and retention and disproportionately face challenges with interpersonal interactions, hiring practices, and supervisory practices. Yet, we find a pervasive narrative that attributes apprentices' success to “hard work,” which contributes to the legitimacy of these inequalities. Consistent with the conceptualization of work organizations as inequality regimes, we argue that the apprenticeship system has policies, practices, and ideologies that are on the surface gender and race/ethnicity neutral, yet lead to the perpetuation of inequalities.     

A new generalized Weibull distribution in income economic inequality curves

Researchers have been developing various extensions and modified forms of the Weibull distribution to enhance its capability for modeling and fitting different data sets. In this note, we investigate the potential usefulness of the new modification to the standard Weibull distribution called odd Weibull distribution in income economic inequality studies. Some mathematical and statistical properties of this model are proposed. We obtain explicit expressions for the first incomplete moment, quantile function, Lorenz and Zenga curves and related inequality indices. In addition to the well-known stochastic order based on Lorenz curve, the stochastic order based on Zenga curve is considered. Since the new generalized Weibull distribution seems to be suitable to model wealth, financial, actuarial and especially income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. Also, the estimation of parameters by maximum likelihood and moment methods is discussed. Finally, this distribution has been fitted to United States and Austrian income data sets and has been found to fit remarkably well in compare with the other widely used income models.     

Can Children Recognize Bodily Expressions of Emotion?

Journal of Nonverbal Behavior - Past research has demonstrated that children understand distinct emotion concepts and can accurately recognize facial expressions of distinct emotions by a young...     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

Beyond Piketty: A new perspective on poverty and inequality in India

The socio-economic literature has focused much on how overall inequality in income distribution (frequently measured by the Gini coefficient) undermines the “trickle down” effect. In other words, the higher the inequality in the income distribution, the lower is the growth elasticity of poverty. However, with the publication of Piketty’s magnum opus (2014), and a subsequent study by Chancel and Piketty (2017) of evolution of income inequality in India since 1922, the focus has shifted to the income disparity between the richest 1% (or 0.01%) and the bottom 50%. Their central argument is that the rapid growth of income at the top end of millionaires and billionaires is a by-product of growth. The present study extends this argument by linking it to poverty indices in India. Based on the India Human Development Survey 2005–12 – a nationwide panel survey-we examine the links between poverty and income inequality, especially in the upper tail relative to the bottom 50%, state affluence (measured in per capita income) and their interaction or their joint effect. Another feature of our research is that we analyse their effects on the FGT class of poverty indices. The results are similar in as much as direction of association is concerned but the elasticities vary with the poverty index. The growth elasticities are negative and significant for all poverty indices. In all three cases, the disparity between the income share of the top 1% and share of the bottom 50% is associated with greater poverty. These elasticities are much higher than the (absolute) income elasticities except in the case of the poverty gap. The largest increase occurs in the poverty gap squared – a 1% greater income disparity is associated with a 1.24% higher value of this index. Thus the consequences of even a small increase in the income disparity are alarming for the poorest.