Slashed generalized Rayleigh distribution

We introduce a new family of distributions suitable for fitting positive data sets with high kurtosis which is called the Slashed Generalized Rayleigh Distribution. This distribution arises as the quotient of two independent random variables, one being a generalized Rayleigh distribution in the numerator and the other a power of the uniform distribution in the denominator. We present properties and carry out estimation of the model parameters by moment and maximum likelihood (ML) methods. Finally, we conduct a small simulation study to evaluate the performance of ML estimators and analyze real data sets to illustrate the usefulness of the new model.     

Theory and methods for partitioned Gini coefficients computed on post-stratified data

The Gini coefficient is used to measure inequality in populations. However, shifts in the population distribution may affect subgroups differently. Consequently, it can be informative to examine inequality separately for these segments. Consider an independently and identically distributed sample split based on ranking and compute the Gini coefficient for each partition. These coefficients, calculated from post-stratified data, are not functions of U-statistics. Therefore, previous theoretical and methodological results cannot be applied. In this article, the asymptotic joint distribution is derived for the partitioned coefficients and bootstrap methods for inference are developed. Finally, an application to per capita income across census tracts is examined.     

The exponentiated reduced Kies distribution: Properties and applications

Here we consider an exponentiated version of the reduced Kies distribution and discuss some of its properties. The parameters of the distribution are estimated by the method of maximum likelihood and illustrated with the help of certain real-life data sets. Asymptotic behavior of the maximum likelihood estimators of the parameters of the distribution is also studied by using certain simulated data sets.     

Extended scaled prediction variance optimality for modified central composite design

Robust parameter designs (RPDs) enable the experimenter to discover how to modify the design of the product to minimize the effect due to variation from noise sources. The aim of this article is to show how this amount of work can be reduced under modified central composite design (MCCD). We propose a measure of extended scaled prediction variance (ESPV) for evaluation of RPDs on MCCD. Using these measures, we show that we can check the error or bias associated with estimating the model parameters and suggest the values of α recommended for MCCS under minimum ESPV.     

The performance of model averaged tail area confidence intervals

We investigate the exact coverage and expected length properties of the model averaged tail area (MATA) confidence interval proposed by Turek and Fletcher, CSDA, 2012, in the context of two nested, normal linear regression models. The simpler model is obtained by applying a single linear constraint on the regression parameter vector of the full model. For given length of response vector and nominal coverage of the MATA confidence interval, we consider all possible models of this type and all possible true parameter values, together with a wide class of design matrices and parameters of interest. Our results show that, while not ideal, MATA confidence intervals perform surprisingly well in our regression scenario, provided that we use the minimum weight within the class of weights that we consider on the simpler model.     

Prior specification of neighbourhood and interaction structure in binary Markov random fields

We formulate a prior distribution for the energy function of stationary binary Markov random fields (MRFs) defined on a rectangular lattice. In the prior we assign distributions to all parts of the energy function. In particular we define priors for the neighbourhood structure of the MRF, what interactions to include in the model, and for potential values. We define a reversible jump Markov chain Monte Carlo (RJMCMC) procedure to simulate from the corresponding posterior distribution when conditioned to an observed scene. Thereby we are able to learn both the neighbourhood structure and the parametric form of the MRF from the observed scene. We circumvent evaluations of the intractable normalising constant of the MRF when running the RJMCMC algorithm by adopting a previously defined approximate auxiliary variable algorithm. We demonstrate the usefulness of our prior in two simulation examples and one real data example.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

Creativity in social work education and practice: reflections on a Survivor Arts Project

This article, written by survivor artists, practitioners and academics (each moving between these different identities and associated voices), will explore a creative arts approach to social work (SW) education within the context of service user and carer involvement. In addition to building upon a developing literature base, the writers will draw upon their own experience and the experience of other service user and carer contributors as well as student and practitioner feedback. In doing so, this article will seek to analyse some of the ways the creative arts can be harnessed as a tool for a more radical, creative and critical approach to professional education and how this in turn can help develop more empathic, critically self-reflective and creative (in the broadest sense of the word) practitioners. In critiquing the more traditional service user and carer involvement approach, this article will attempt to show how the more innovative approach of the Survivor Arts Project can move us beyond an arguably more limiting ‘expertise through experience’ model. In outlining an emancipatory model which recognises the relevance of lived experience and places this within the context of survivor movements, focusing also on the skills and insights survivors bring to SW education, this article will detail the ways we can work towards developing a more vibrant and dynamic learning environment which values our life stories and recognises our strengths as well as the many different and overlapping identities which place us along a continuum that connects our lives and informs our practice.     

Foster Parents’ Nutritional Strategies and Children’s Well-Being

Among their many responsibilities, foster parents are tasked with providing healthy food and proper nutrition to children with a variety of physical, psychological, and emotional needs. Using the lens of symbolic interactionism, this exploratory mixed methods study examined how foster parents attend to the nutritional needs of abused and neglected children. Methods used included both quantitative surveys and qualitative follow-up interviews. Surveys were completed by 23 foster parents in a large, Midwest metropolitan area. Parents reported they provided their children balanced meals, although one-third of households reported instances of food insecurity. During qualitative interviews, foster parents (N = 9) described how children arrived at their home with a variety of unhealthy eating habits, some severe in nature. Parents discussed strategies used to modify and improve eating patterns, including offering healthy food choices, having children plan meals, and gathering as a family for daily dinners. In general, parents understood the link between unhealthy eating and past childhood trauma, but also reported being unprepared for more serious eating problems like hoarding. Findings indicate that foster parents need food assistance to increase food security as well as training in assessing and responding a variety of unhealthy child eating behaviors.