On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

A comparison of recent nonparametric methods for testing effects in two-by-two factorial designs

The two-way two-levels crossed factorial design is a commonly used design by practitioners at the exploratory phase of industrial experiments. The F-test in the usual linear model for analysis of variance (ANOVA) is a key instrument to assess the impact of each factor and of their interactions on the response variable. However, if assumptions such as normal distribution and homoscedasticity of errors are violated, the conventional wisdom is to resort to nonparametric tests. Nonparametric methods, rank-based as well as permutation, have been a subject of recent investigations to make them effective in testing the hypotheses of interest and to improve their performance in small sample situations. In this study, we assess the performances of some nonparametric methods and, more importantly, we compare their powers. Specifically, we examine three permutation methods (Constrained Synchronized Permutations, Unconstrained Synchronized Permutations and Wald-Type Permutation Test), a rank-based method (Aligned Rank Transform) and a parametric method (ANOVA-Type Test). In the simulations, we generate datasets with different configurations of distribution of errors, variance, factor's effect and number of replicates. The objective is to elicit practical advice and guides to practitioners regarding the sensitivity of the tests in the various configurations, the conditions under which some tests cannot be used, the tradeoff between power and type I error, and the bias of the power on one main factor analysis due to the presence of effect of the other factor. A dataset from an industrial engineering experiment for thermoformed packaging production is used to illustrate the application of the various methods of analysis, taking into account the power of the test suggested by the objective of the experiment.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.     

Reflecting on the Past: A Content Analysis of Family Therapy Research from 2000‐2015

In this content analysis, researchers examine articles published from 2000 to 2015 in three family therapy journals, yielding a total of 948 empirical articles. The purpose is to provide an overview of the research being published, assess who is publishing, and investigate the current state of clinical effectiveness research in marriage and family therapy (MFT). Most first authors were affiliated with MFT programs and primarily included diversity and couples in their research. There was a significant increase of research on clinical process—though the number of clinical outcome studies held steady. There were no significant changes with regard to research funding. Implications support the use of innovative research methods to provide evidence of clinical effectiveness.     

Building a health communication brand for University of Texas System tobacco control

Objective: Despite declining cigarette smoking rates in the US, there is a continued need for tobacco prevention education campaigns to reach young adults. Recognizing the need for improved tobacco control messaging, the University of Texas (UT) System engaged The University of Texas at Austin Center for Health Communication to develop a brand and message that would strengthen tobacco control efforts at its 14 institutions. Methods: This article describes the iterative process involved in creating a brand for tobacco control, including an environmental scan, identifying potential message themes, and creating and refining logos. Results: This article highlights the process of developing a system-wide tobacco control brand. Specifically, the process included coordinating an interdisciplinary team with content and design experts, and presenting ideas to stakeholders for serial feedback and refinement, among others. Conclusions: Ultimately, this project offers a model for other systems of higher education interested in pursuing similar initiatives.     

Graphical representation of survival curves in the presence of time-dependent categorical covariates with application to liver transplantation

Graphical representation of survival curves is often used to illustrate associations between exposures and time-to-event outcomes. However, when exposures are time-dependent, calculation of survival probabilities is not straightforward. Our aim was to develop a method to estimate time-dependent survival probabilities and represent them graphically. Cox models with time-dependent indicators to represent state changes were fitted, and survival probabilities were plotted using pre-specified times of state changes. Time-varying hazard ratios for the state change were also explored. The method was applied to data from the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL). Survival curves showing a ‘split’ at a pre-specified time t allow for the qualitative comparison of survival probabilities between patients with similar baseline covariates who do and do not experience a state change at time t. Time since state change interactions can be visually represented to reflect changing hazard ratios over time. A2ALL study results showed differences in survival probabilities among those who did not receive a transplant, received a living donor transplant, and received a deceased donor transplant. These graphical representations of survival curves with time-dependent indicators improve upon previous methods and allow for clinically meaningful interpretation.     

Child Obesity and the Interaction of Family and Neighborhood Socioeconomic Context

Population Research and Policy Review - The literature on neighborhoods and child obesity links contextual conditions to risk, assuming that if place matters, it matters in a similar way for...     

Periodic review of foster care: a brief national assessment.

A survey of statewide public agencies shows that a majority of states have some form of judicial or administrative periodic review, although few have both. The organizational level of authority and degree of foster care specialization of agencies are positively correlated with administrative review and negatively correlated with judicial review.