A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models

When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been applied to UCM; however, in most cases, these procedures are unable to identify the heteroscedastic component correctly. The main issue is that test statistics are affected by the presence of serial correlation, causing the distribution of the statistic under conditional homoscedasticity to remain unknown. We propose a nonparametric statistic for testing heteroscedasticity based on the well-known Wilcoxon''s rank statistic. We study the asymptotic validation of the statistic and examine bootstrap procedures for approximating its finite sample distribution. Simulation results show an improvement in the size of the homoscedasticity tests and a power that is clearly comparable with the best alternative in the literature. We also apply the test on real inflation data. Looking for the presence of a conditionally heteroscedastic effect on the error terms, we arrive at conclusions that almost all cases are different than those given by the alternative test statistics presented in the literature.     

‘Lost in transition?’ – Newly educated social workers’ reflections on their first months in practice

Entering the work of world can involve significant challenges for the beginning practitioner. The transition process from university to work can play an important role in the emerging practitioner’s development as a social worker. The present longitudinal study follows 12 Swedish social work graduates from university and over their first four years in practice. The study provides an insight into how newly educated practitioners may experience the transition from university to the world of work and considers how they can be prepared and supported to meet the challenges of practice within contemporary work contexts. The students were first interviewed just prior to leaving university [see Tham &; Lynch (2014). Prepared for practice? Graduating social work students’ reflections on their education, competence and skills. Social Work Education, 33(6), 704–717]. This paper captures the reflections of these students after four months in practice. Feelings of unpreparedness, unorganised, or even ‘chaotic’ perceptions of the workplace and uncertainty about the future were emergent themes, particularly among new practitioners employed in social services. The findings illustrate the vulnerability of these new practitioners and the importance of workplace induction and the provision of adequate support in their new professional roles.     

Understanding How Case Managers Use ‘Sabotage’ as a Frame for Clinically Difficult Situations

Case managers are among the direct service providers responsible for engaging people with severe mental illnesses. Understanding how they interpret and respond to clinically difficult situations can inform ways to intervene and reduce service disengagement. This qualitative study explored clinically difficult situations when case managers invoked the term ??sabotage??. Interviews were conducted with 21 case managers and analyzed by co-coders focusing on how case managers used the term sabotage to describe service user behavior. Themes that emerged were attributing sabotage to; fear of success, fear of attaining what you desire, avoidance of responsibility and change, and a lack of structured support. Drawing on the concepts of clinical case management and mental health recovery, we consider implications for the training and supervision of case managers and how services are structured.     

Copula structure analysis

Summary.  We extend the standard approach of correlation structure analysis for dimension reduction of high dimensional statistical data. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a model for the copula. For elliptical copulas a correlation-like structure remains, but different margins and non-existence of moments are possible. After introducing the new concept and deriving some theoretical results we observe in a simulation study the performance of the estimators: the theoretical asymptotic behaviour of the statistics can be observed even for small sample sizes. Finally, we show our method at work for a financial data set and explain differences between our copula-based approach and the classical approach. Our new method yielear models also.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

<Emphasis Type="BoldItalic">MAD-STEC</Emphasis>: a method for multiple automatic detection of space-time emerging clusters

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.     

Personality profiles of factitious disorder imposed by mothers: A comparative analysis

Factitious Disorder Imposed by Another (FDIA) can be difficult to expeditiously identify. Understanding personality characteristics of FDIA perpetrators via psychological assessment may decrease the time between initial abuse and identification of FDIA. Personality characteristics of FDIA mother-perpetrators were examined through a comparative analysis of MMPI-2 profiles. It was predicted that mother-perpetrators would have significantly higher overall profile elevations on the MMPI-2 and elevations on specific clinical scales than a comparative control group. Results indicated that mother-perpetrators displayed significantly higher, elevations on the Psychasthenia (Pt) and Schizophrenia (Sc) scales and a trend toward significantly higher elevation on the Psychopathic Deviate (Pd) scale than the control group. These findings, along with the demographic information obtained, establish a preliminary foundation for understanding an overall profile of mother-perpetrators.