Identifiable finite mixtures of location models for clustering mixed-mode data

For clustering mixed categorical and continuous data, Lawrence and Krzanowski (1996) proposed a finite mixture model in which component densities conform to the location model. In the graphical models literature the location model is known as the homogeneous Conditional Gaussian model. In this paper it is shown that their model is not identifiable without imposing additional restrictions. Specifically, for g groups and m locations, (g!)m–1 distinct sets of parameter values (not including permutations of the group mixing parameters) produce the same likelihood function. Excessive shrinkage of parameter estimates in a simulation experiment reported by Lawrence and Krzanowski (1996) is shown to be an artifact of the model's non-identifiability. Identifiable finite mixture models can be obtained by imposing restrictions on the conditional means of the continuous variables. These new identified models are assessed in simulation experiments. The conditional mean structure of the continuous variables in the restricted location mixture models is similar to that in the underlying variable mixture models proposed by Everitt (1988), but the restricted location mixture models are more computationally tractable.     

Likelihood Factorizations for Mixed Discrete and Continuous Variables

Some general remarks are made about likelihood factorizations, distinguishing parameter-based factorizations and concentration-graph factorizations. Two parametric families of distributions for mixed discrete and continuous variables are discussed. Conditions on graphs are given for the circumstances under which their joint analysis can be split into separate analyses, each involving a reduced set of component variables and parameters. The result shows marked differences between the two families although both involve the same necessary condition on prime graphs. This condition is both necessary and sufficient for simplified estimation in Gaussian and for discrete log linear models.     

Linear Increments with Non‐monotone Missing Data and Measurement Error

Linear increments (LI) are used to analyse repeated outcome data with missing values. Previously, two LI methods have been proposed, one allowing non‐monotone missingness but not independent measurement error and one allowing independent measurement error but only monotone missingness. In both, it was suggested that the expected increment could depend on current outcome. We show that LI can allow non‐monotone missingness and either independent measurement error of unknown variance or dependence of expected increment on current outcome but not both. A popular alternative to LI is a multivariate normal model ignoring the missingness pattern. This gives consistent estimation when data are normally distributed and missing at random (MAR). We clarify the relation between MAR and the assumptions of LI and show that for continuous outcomes multivariate normal estimators are also consistent under (non‐MAR and non‐normal) assumptions not much stronger than those of LI. Moreover, when missingness is non‐monotone, they are typically more efficient.     

On conditional risk estimation considering model risk

Usually, parametric procedures used for conditional variance modelling are associated with model risk. Model risk may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. Hence, non-parametric artificial intelligence models can be considered as alternative models given that they do not rely on an explicit form of the volatility. In this paper, we consider the least-squares support vector regression (LS-SVR), weighted LS-SVR and Fixed size LS-SVR models in order to handle the problem of conditional risk estimation taking into account issues of model risk. A simulation study and a real application show the performance of proposed volatility and VaR models.     

Model selection in high-dimensional noisy data: a simulation study

In many practical applications, high-dimensional regression analyses have to take into account measurement error in the covariates. It is thus necessary to extend regularization methods, that can handle the situation where the number of covariates p largely exceed the sample size n, to the case in which covariates are also mismeasured. A variety of methods are available in this context, but many of them rely on knowledge about the measurement error and the structure of its covariance matrix. In this paper, we set the goal to compare some of these methods, focusing on situations relevant for practical applications. In particular, we will evaluate these methods in setups in which the measurement error distribution and dependence structure are not known and have to be estimated from data. Our focus is on variable selection, and the evaluation is based on extensive simulations.     

‘Occupation Children’ in Germany after World War II — Problems and Coping Strategies

The article focuses on the autobiographical memories of people born in Germany after the Second World War, whose mothers were local women and whose fathers were members of the allied forces. Based on the analysis of narrative interviews, we ask how these people experienced their growing up and their sociocultural environment in the post‐war era, and what kind of coping strategies they developed to deal with adverse living conditions.     

Reducing attitudes of prejudice and discrimination through literature: a quasi-experiment on the upper key-stage 2 students of the British schools in Karachi,Pakistan

This study aims to explore the efficacy of literature relating to Anne Frank to reduce children’s attitudes of prejudice and discrimination with respect to specific dimensions, including ethnicity and race, religion and gender. A quasi-experiment was employed with pre-post control design. The sample consisted of 100 students of the upper key-stage 2 (UKS2) level, recruited from the two British schools in Karachi. These schools represented an Experimental Group (EG) and a matched Comparison Group (CG). A research instrument titled ‘Muggles world of differences’ (MWD) was developed and validated. It was administered on CG and EG, both before and after intervention. The intervention plan ‘Reading of Young Anne’s Literature’ (ROYAL) was developed based on selected readings from Anne Frank related literature (AFL). Intervention was provided to EG, while CG studied similar concepts using a more standard method. Results of the pretest revealed a high level of prejudice and discrimination in both the EG and CG, with no significant difference (p > 0.05) in the overall scores, as well as in the specific content domains. However, the post-test of the two groups revealed that the students in the EG had outperformed their CG counterparts in overall MWD scores, as well as in specific content domains. The difference was found to be significant (p < 0.05) with the large effect size (r = ?0.63). Results support the efficacy of teaching Anne Frank related literature in reducing attitudes of prejudice and discrimination.     

关爱抑或歧视——男女不同龄退休制度分析

男女不同龄退休制度是就业领域中的性别歧视,但是对此性别歧视的认定不能简单地归结为基于性别的差别对待的存在,因为并非所有的差别对待都是歧视。应该在对就业歧视的构成要素及抗辩事由进行阐释的基础上,对男女不同龄退休制度的歧视性进行分析。     

Unreasonable adjustments: the additional unpaid labour of academics with disabilities

Abstract

Two recent contributions to this section have drawn attention to the barriers which academics with disabilities have to navigate in academia where ableism “is endemic” (Brown and Leigh, 2018 Brown, N & Leigh, J (2018) Abelism in academia? Where are the disabled and ill academics. Disability & Society, 33 (6): 985–989.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]: 4). Hannam-Swain (2018 Hannam-Swain, S (2018) The additional labour of a disabled PhD student. Disability & Society, 33 (1): 138–142.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) highlighted the additional intellectual, emotional and physical labour required of her as a disabled PhD student, and Brown and Leigh (2018 Brown, N & Leigh, J (2018) Abelism in academia? Where are the disabled and ill academics. Disability & Society, 33 (6): 985–989.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) queried “where are all the disabled and ill academics?” However, Brown and Leigh primarily focus on those with invisible “conditions” and the dilemmas raised by disclosure in a context where such conditions negate academic status and credibility. In contrast, since my “disability” is visible, I do not share the dilemma/“luxury” of secrecy. My presence announces my status before me, and this negates my personhood altogether in academic settings. It also places a burden of additional unpaid labour upon me which has significant mental health and career impacts as well as violating principles of equality.