Aspirations and Setbacks of Working Children in Addis Ababa: Can They Realise Their Futures?

Based on interviews with 45 children, this article looks at the life situations and aspirations of working children in Addis Ababa, Ethiopia, including their hopes, efforts and struggles to make their dreams come true. Pushed by the need for survival, the children work and nearly half go to school. They demonstrate agency, taking care of themselves, supporting families and aspiring to change their lives. However, they also face challenges that make achieving their aspirations difficult. The authors argue that agency alone is not sufficient to help them realise their aspirations unless buffered by supportive structures and systems.     

An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data

Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square statistic to its degrees of freedom is greater than one; however, there is currently no fixed threshold for declaring the need for statistical intervention. We consider simulated cross-sectional and longitudinal datasets containing varying magnitudes of overdispersion caused by outliers or zero inflation, as well as real datasets, to determine an appropriate threshold value of this statistic which indicates when overdispersion should be addressed.     

生态环境损害赔偿的博弈分析

为了破解我国在环境保护方面存在的“企业污染、群众受害、政府买单”的困局,文章在“谁使用谁付费、谁保护谁收益”原则的指导下提出由造成生态环境污染的企业向政府进行赔偿来修复受损的生态环境.基于生态资本的自我增殖和时间价值,在传统的“零净损失”的政策目标下提出一种新的生态赔偿的政策目标.通过静态贝叶斯博弈分析发现:由于双方存在信息不对称,企业对政府政策目标的不确定性改变了政府的效用函数,双方对自身利益最大化的追求将导致两者产生冲突.最后提出完善企业与政府之间的生态环境损害赔偿制度的建议.     

Joint modeling of multivariate hearing thresholds measured longitudinally at multiple frequencies

Pure-tone thresholds are used to estimate hearing acuity and, when measured longitudinally, can characterize age-related changes in hearing. Measured at multiple-frequencies, multiple-irregular time points, for right and left ears, these longitudinal studies of age-related hearing loss produce data of inherent complexity due to: 1) multivariate outcomes at different frequencies; 2) longitudinal measurements taken at subject-specific time intervals; and 3) inter-ear correlations due to clustering and nesting. To address limitations in existing methods, we propose a multivariate generalized linear mixed model (mGLMM) and assess its performance. We demonstrate its application using a unique dataset from a cohort study of age-related hearing loss.     

Multivariate generalized linear mixed models with random intercepts to analyze cardiovascular risk markers in type-1 diabetic patients

Statistical approaches tailored to analyzing longitudinal data that have multiple outcomes with different distributions are scarce. This paucity is due to the non-availability of multivariate distributions that jointly model outcomes with different distributions other than the multivariate normal. A plethora of research has been done on the specific combination of binary-Gaussian bivariate outcomes but a more general approach that allows other mixtures of distributions for multiple longitudinal outcomes has not been thoroughly demonstrated and examined. Here, we study a multivariate generalized linear mixed models approach that jointly models multiple longitudinal outcomes with different combinations of distributions and incorporates the correlations between the various outcomes through separate yet correlated random intercepts. Every outcome is linked to the set of covariates through a proper link function that allows the incorporation and joint modeling of different distributions. A novel application was demonstrated on a cohort study of Type-1 diabetic patients to jointly model a mix of longitudinal cardiovascular outcomes and to explore for the first time the effect of glycemic control treatment, plasma prekallikrein biomarker, gender and age on cardiovascular risk factors collectively.     

Latent class based multiple imputation approach for missing categorical data

In this paper we propose a latent class based multiple imputation approach for analyzing missing categorical covariate data in a highly stratified data model. In this approach, we impute the missing data assuming a latent class imputation model and we use likelihood methods to analyze the imputed data. Via extensive simulations, we study its statistical properties and make comparisons with complete case analysis, multiple imputation, saturated log-linear multiple imputation and the Expectation–Maximization approach under seven missing data mechanisms (including missing completely at random, missing at random and not missing at random). These methods are compared with respect to bias, asymptotic standard error, type I error, and 95% coverage probabilities of parameter estimates. Simulations show that, under many missingness scenarios, latent class multiple imputation performs favorably when jointly considering these criteria. A data example from a matched case–control study of the association between multiple myeloma and polymorphisms of the Inter-Leukin 6 genes is considered.     

Spatially explicit survival modeling for small area cancer data

In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results registry prostate cancer data.