Weighted simulated integrated conditional moment tests for parametric conditional distributions of stationary time series processes

In this article, we propose a weighted simulated integrated conditional moment (WSICM) test of the validity of parametric specifications of conditional distribution models for stationary time series data, by combining the weighted integrated conditional moment (ICM) test of Bierens (1984 Bierens, H. J. (1984). Model specification testing of time series regressions. Journal of Econometrics 26:323–353.[Crossref], [Web of Science ®] , [Google Scholar]) for time series regression models with the simulated ICM test of Bierens and Wang (2012 Bierens, H. J., Wang, L. (2012). Integrated conditional moment tests for parametric conditional distributions. Econometric Theory 28:328–362.[Crossref], [Web of Science ®] , [Google Scholar]) of conditional distribution models for cross-section data. To the best of our knowledge, no other consistent test for parametric conditional time series distributions has been proposed yet in the literature, despite consistency claims made by some authors.     

Layered adaptive importance sampling

Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.     

Varying kernel marginal density estimator for a positive time series

This paper analyses the large sample behaviour of a varying kernel density estimator of the marginal density of a non-negative stationary and ergodic time series that is also strongly mixing. In particular we obtain an approximation for bias, mean square error and establish asymptotic normality of this density estimator. We also derive an almost sure uniform consistency rate over bounded intervals of this estimator. A finite sample simulation shows some superiority of the proposed density estimator over the one based on a symmetric kernel.     

Multiphase experiments in practice: A look back

Multiphase experiments are introduced and an overview of their design and analysis as it is currently practised is given via an account of their development since 1955 and a literature survey. Methods that are available for designing and analysing them are outlined, with an emphasis on making explicit the role of the model in their design. The availability of software and its use is described in detail. Overall, while multiphase designs have been applied in areas such as plant breeding, plant pathology, greenhouse experimentation, product storage, gene expression studies, and sensory evaluation, their deployment has been limited.     

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.     

Illuminating the Phenomenological Challenges of Cross-Cultural Supervision

ABSTRACT

The authors discuss transference enactments that occur during cross-cultural supervision. Previous research has shown that few mental health professionals willingly talk about race during supervision and that White supervisees experience significant difficulty when directly engaged in discussions about race. They introduce a new phenomenon, “stereotypical” transference enactments, as a means for understanding the countertransference Black female clinical supervisors experience during clinical supervision of White supervisees. The findings indicated that effective cross-cultural supervision should address the intersectionality of race and gender to enhance the development of multicultural clinical skills. The clinical and educational challenges are discussed by using two case vignettes.     

Variations in the Life Histories of Incarcerated Parents by Race and Ethnicity: Implications for Service Provision

ABSTRACT

Incarcerated parents have complex life histories that often remain unresolved during incarceration, can continue to create barriers to prosocial success on release, and present similar intergenerational challenges for their children. This study examines the life histories of incarcerated fathers and mothers from the Pacific Northwest and how their experiences vary based on race and ethnicity. Five areas examined were exposure to trauma, child welfare involvement, mental health and substance abuse problems, juvenile justice and adult criminal justice involvement, and intergenerational criminal justice involvement. The sample comprised 359 incarcerated parents, and their racial/ethnic composition was 59% White, 14% African American, 11% multiracial, 8% Native American, and 7% Latino. Few differences were found across racial and ethnic groups. Mothers appeared more similar to each other across groups than fathers. Results illustrated similarities yet some surprising differences with national trends on key study variables. Implications for future research and intervention and prevention are discussed.     

The Moderating Effect of Living with a Child Before Incarceration on Postrelease Outcomes Related to a Prison-Based Parent Management Training Program

ABSTRACT

With the rapid growth in incarceration in the United States over the past few decades came dramatic growth in the number of the incarcerated parents with at least one minor child. Parental incarceration places extra stresses and strains on families and children. Almost all of those incarcerated will eventually be released. However, the majority of those released from prison will be rearrested within a year. Finding interventions that can decrease the likelihood of returning to crime and to incarceration are of utmost importance. Using a social bond theory framework, the authors examine the moderating effect of living with a child before incarceration on program outcomes related to a prison-based parent management training program. A significant effect was found. Implications for these findings are discussed for future research and practice.