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.     

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.     

Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation

Children represent a large underserved population of “therapeutic orphans,” as an estimated 80% of children are treated off‐label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or “borrowing”) of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure‐response information for antiepileptic drugs to pediatrics.     

Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

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.     

Allais for the poor: Relations to ability,information processing,and risk attitudes

This paper complements evidence on the Allais paradox from advanced countries and educated people by a novel investigation in a poor rural area. The share of Allais-type behavior is indeed high and related to indicators of “lacking ability,” such as poor education, unemployment, and little financial sophistication. Based on prospective reference theory, we extend these characteristics by biased processing of probabilistic information. Finally, we reveal that Allais-type behavior is linked to risk-related characteristics, such as risk tolerance and optimism. This indicates a potential problem as exactly the more dynamic among the poor tend to make inconsistent decisions under uncertainty.     

Constructing prevention programmes with a Māori health service provider view

Mainstream approaches to chronic condition management and prevention inadequately address the needs of Māori, the Indigenous people of New Zealand. Māori health service providers (MHSPs) are uniquely placed to address the critical gap in the prevention of chronic conditions. In this paper, we report qualitative research findings investigating how prevention was being modelled, practiced and measured in selected MHSP settings. Results indicate barriers to achieving wellbeing through health service delivery. The dominant individualistic, medical conditions-focused discourse, along with responding to acute need, is a driver of service delivery norms.There are examples of shifts in organisational structure and delivery configurations that demonstrate that these norms are being challenged and reframed, in some form, by MHSPs. Consolidation of these approaches requires significant work and increased resources as well as a broader systems-level response that prioritises prevention.     

The Diffusion of Tolerance: Birth Cohort Changes in the Effects of Education and Income on Political Tolerance

Political tolerance—the willingness to extend civil liberties to traditionally stigmatized groups—is pivotal to the functioning of democracy and the well‐being of members of stigmatized groups. Although political tolerance has traditionally been more common among American elites, we argue that as tolerance has increased, it has also diffused to less educated and less affluent segments of the population. The relative stability of political attitudes over the life course and the socialization of more recent birth cohorts in contexts of increased tolerance suggest that this diffusion of tolerance occurs across birth cohorts rather than time periods. Using age‐period‐cohort models and more than three and a half decades of repeated cross‐sectional survey data, we find persistent and robust across‐cohort declines in the importance of both income and higher education in determining levels of political tolerance. Declines in the effects of socioeconomic status are evident with tolerance toward all five out‐groups in the analysis—anti‐religionists, gays and lesbians, communists, militarists, and racists—but to varying degrees. These findings fit with a model of changes in public opinion, particularly views of civil and political rights, through processes of cultural diffusion and cohort replacement.     

Developing an undue influence screening tool for Adult Protective Services

The study purpose was to develop and pilot an undue influence screening tool for California’s Adult Protective Services (APS) personnel based on the definition of undue influence enacted into California law January 1, 2014. Methods included four focus groups with APS providers (n = 33), piloting the preliminary tool by APS personnel (n = 15), and interviews with four elder abuse experts and two APS administrators. Social service literature—including existing undue influence models—was reviewed, as were existing screening and assessment tools. Using the information from these various sources, the California Undue Influence Screening Tool (CUIST) was developed. It can be applied to APS cases and potentially adapted for use by other professionals and for use in other states. Implementation of the tool into APS practice, policy, procedures, and training of personnel will depend on the initiative of APS management. Future work will need to address the reliability and validity of CUIST.