Bayesian quantile regression for skew-normal linear mixed models

Linear mixed models have been widely used to analyze repeated measures data which arise in many studies. In most applications, it is assumed that both the random effects and the within-subjects errors are normally distributed. This can be extremely restrictive, obscuring important features of within-and among-subject variations. Here, quantile regression in the Bayesian framework for the linear mixed models is described to carry out the robust inferences. We also relax the normality assumption for the random effects by using a multivariate skew-normal distribution, which includes the normal ones as a special case and provides robust estimation in the linear mixed models. For posterior inference, we propose a Gibbs sampling algorithm based on a mixture representation of the asymmetric Laplace distribution and multivariate skew-normal distribution. The procedures are demonstrated by both simulated and real data examples.     

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.     

Parallel and interacting stochastic approximation annealing algorithms for global optimisation

We present the parallel and interacting stochastic approximation annealing (PISAA) algorithm, a stochastic simulation procedure for global optimisation, that extends and improves the stochastic approximation annealing (SAA) by using population Monte Carlo ideas. The efficiency of standard SAA algorithm crucially depends on its self-adjusting mechanism which presents stability issues in high dimensional or rugged optimisation problems. The proposed algorithm involves simulating a population of SAA chains that interact each other in a manner that significantly improves the stability of the self-adjusting mechanism and the search for the global optimum in the sampling space, as well as it inherits SAA desired convergence properties when a square-root cooling schedule is used. It can be implemented in parallel computing environments in order to mitigate the computational overhead. As a result, PISAA can address complex optimisation problems that it would be difficult for SAA to satisfactory address. We demonstrate the good performance of the proposed algorithm on challenging applications including Bayesian network learning and protein folding. Our numerical comparisons suggest that PISAA outperforms the simulated annealing, stochastic approximation annealing, and annealing evolutionary stochastic approximation Monte Carlo.     

The Aging Semantic Differential in Mandarin Chinese: Measuring Attitudes toward Older Adults in China

ABSTRACT

The Aging Semantic Differential (ASD) is the most widely used instrument to measure young people’s attitudes towards older adults. This study translated the ASD to Mandarin and examined its psychometric properties. The Mandarin-ASD contains three latent factors (Personality and Mental Health, Societal Participation, and Physical) that have high internal reliability and reasonable discriminate validity. Social work researchers, practitioners and allied professionals may utilize the ASD-Mandarin instrument to measure young people’s attitudes towards older adults in China. We issue a call for a universal-ASD that can be applied across different cultural contexts.     

Increasing Competency,Self-Confidence,and Connectedness Among Foster Care Alumni Entering a 4-Year University: Findings from an Early-Start Program

Research shows that few foster care alumni enroll and complete post-secondary education. For those who do enroll, many experience challenges associated with academic and social adjustment and are at risk of dropping out. Many college-based programs are being developed and are available to foster care alumni to support them during their post-secondary education. This study used pre-tests, post-tests, and journal entries of seventeen participants to evaluate a strengths-based, resilience-oriented early-start program for newly enrolled students at a large public university that provides knowledge about the institution, resources (e.g., tutoring), and social support necessary for foster care alumni to make a smooth transition into college life and to increase short and long-term student success. Quantitative and qualitative findings indicate that students who participated in the early-start program showed increased confidence and competency in academic and social adjustment. Findings also showed personal growth (e.g., building resiliency) and an increased connectedness among students who participated in the program. Study findings can inform the development of new on-campus support programs and help enhance existing programming. Future research should address short and long-term outcomes and experiences of students who are former foster youth who have participated in on-campus support programs.     

Gerontological Social Work Student-Delivered Respite: A Community-University Partnership Pilot Program

ABSTRACT

This study describes a community-university partnership to support a gerontological social work student-delivered respite program, the Houseguest Program (Houseguest). Houseguest was designed using a community-engaged scholarship model of integrating research, teaching, and service. Houseguest was piloted with a small group of community-dwelling, coresiding dementia caregivers and care recipients. We examined caregivers’ experiences with student-delivered respite using qualitative data analysis. Thematic analysis produced 8 themes: (a) respite from full time caregiving role, (b) information on caregiving strategies, (c) no-cost supportive services, (d) opportunity for care recipients to socialize, (e) tailored activities for care recipients, (f) rapport-building between students and family dyad, (g) reciprocity between students and family dyad, and (h) program continuation. We conclude with a proposed community-engaged scholarship model for dementia caregiving. Through a community-university partnership, Houseguest reduced the impact of caregiver burden and created an opportunity for students to serve families affected by dementia through respite and tailored activities.