A Mandate for Honesty: Jeff Young's No Bullshit Therapy

Jeff Young talks about having been trained to be formal with clients, and his gradual realisation that he could return to the more direct ways of speech he learned as a child, and combine that with warmth. He found that clients and fellow professionals welcomed a therapist who did not ‘beat around the bush’. Together, Jeff and Ron talk about when directness is appropriate, and how directness needs to be modified for particular clients.     

Contemporary Political Communications: Audiences, Politicians and the Media in International Research

Research into political communication has grown rapidly in the past three decades, shifting from specific, quantitative and utilitarian studies to include a broad variety of research questions, methods and theoretical frameworks. Interdisciplinary work in political communication takes audiences, political actors (such as governments and politicians), and media content and media institutions as central foci. Research has traditionally focused on election campaign-specific studies within national contexts. Emerging research, however, is now addressing broader questions about media texts and organisations and also about representations and power. This research is working to trace technological changes and developments in an era of globalised media and political communication and developing new, complex conceptions of audience agency and reception.     

Robust multivariate mixture regression models with incomplete data

Multivariate mixture regression models can be used to investigate the relationships between two or more response variables and a set of predictor variables by taking into consideration unobserved population heterogeneity. It is common to take multivariate normal distributions as mixing components, but this mixing model is sensitive to heavy-tailed errors and outliers. Although normal mixture models can approximate any distribution in principle, the number of components needed to account for heavy-tailed distributions can be very large. Mixture regression models based on the multivariate t distributions can be considered as a robust alternative approach. Missing data are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this paper, we propose a multivariate t mixture regression model with missing information to model heterogeneity in regression function in the presence of outliers and missing values. Along with the robust parameter estimation, our proposed method can be used for (i) visualization of the partial correlation between response variables across latent classes and heterogeneous regressions, and (ii) outlier detection and robust clustering even under the presence of missing values. We also propose a multivariate t mixture regression model using MM-estimation with missing information that is robust to high-leverage outliers. The proposed methodologies are illustrated through simulation studies and real data analysis.     

Bias of maximum likelihood estimators of the response standard deviation in regression

The bias of maximum likelihood estimators of the standard deviation of the response in location/scale regression models is considered. Results are obtained for a very wide family of densities for the response variable. These are used to propose point estimators with improved mean square error properties and to demonstrate the importance of bias correction in statistical inference when samples are moderately small.     

Migration Policy and Development in Chile

Current and prospective migration law and policy in Chile does not adequately incorporate the causes, content, and consequences of international migration to and from Chile. We describe and examine migration in‐flows, out‐flows, migration‐related policies, and how those policies drive, and are driven by, notions of development in Chile. We explore contradictions in Chilean nascent migration policy currently under legislative review. We argue that it is imperative that migration, migration policy, and their relationship to development be discussed inclusively and transparently and be explicitly incorporated into the Chilean government's nascent migration and development legal policies and frameworks.     

A monte carlo study of variable selection and inferences in a two stage random coefficient linear regression model with unbalanced data

A growth curve analysis is often applied to estimate patterns of changes in a given characteristic of different individuals. It is also used to find out if the variations in the growth rates among individuals are due to effects of certain covariates. In this paper, a random coefficient linear regression model, as a special case of the growth curve analysis, is generalized to accommodate the situation where the set of influential covariates is not known a priori. Two different approaches for seleaing influential covariates (a weighted stepwise selection procedure and a modified version of Rao and Wu’s selection criterion) for the random slope coefficient of a linear regression model with unbalanced data are proposed. Performances of these methods are evaluated by means of Monte-Carlo simulation. In addition, several methods (Maximum Likelihood, Restricted Maximum Likelihood, Pseudo Maximum Likelihood and Method of Moments) for estimating the parameters of the selected model are compared Proposed variable selection schemes and estimators are appliedtotheactualindustrial problem which motivated this investigation.