Usability and Feasibility Study of a Remote Cognitive Behavioral Therapy System with Long-Term Unemployed Women

We present the results of the use of a cognitive behavioral therapeutic intervention tool to improve the mental, physical, and social health of a group of long-term unemployed women in Spain. Method: We sent automated text messages (SMS) to the mobile phones of long-term unemployed women selected at random from public social services. During a 28-day intervention period, women received four daily automated text messages on her mobile phone on a predetermined hourly schedule. We measured depression symptoms at the start and end of the intervention and we analyzed qualitative data to determine the acceptability of a remote SMS program. Results: Depression symptoms using the Personal Health Questionnaire-9 (PHQ-9), went from an average of 13.8 at baseline to 4.9 at the end of 28 days (p?=?0.89). One hundred percent of the women reported that they liked receiving the text messages and most found them helpful.     

Approximate confidence intervals for the weighted kappa coefficient of a binary diagnostic test subject to a case–control design

Case–control design to assess the accuracy of a binary diagnostic test (BDT) is very frequent in clinical practice. This design consists of applying the diagnostic test to all of the individuals in a sample of those who have the disease and in another sample of those who do not have the disease. The sensitivity of the diagnostic test is estimated from the case sample and the specificity is estimated from the control sample. Another parameter which is used to assess the performance of a BDT is the weighted kappa coefficient. The weighted kappa coefficient depends on the sensitivity and specificity of the diagnostic test, on the disease prevalence and on the weighting index. In this article, confidence intervals are studied for the weighted kappa coefficient subject to a case–control design and a method is proposed to calculate the sample sizes to estimate this parameter. The results obtained were applied to a real example.     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.     

Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

Tests for Structural Changes in Time Series of Counts

We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.     

A comparison of L-, LQ-, TL-moment and maximum likelihood high quantile estimates of the GPD and GEV distribution

In this article, L-moments, LQ-moments and TL-moments of the generalized Pareto and generalized extreme-value distributions are derived up to the fourth order. The first three L-, LQ- and TL-moments are used to obtain estimators of their parameters. Performing a simulation study, high-quantile estimates based on L-, LQ-, and TL-moments are compared to the maximum likelihood estimate with respect to their sample mean squared error. This consists of identifying an optimal combination of parameters α and p both considered in the range [0, 0.5] for estimating quantiles by LQ-moments. The results show L-moment and maximum likelihood methods outperform other methods.     

On fiducial generators

Fiducial inference has been gaining presence recently and it is the intention of the present article to look at the notion of fiducial generators; meaning procedures to simulate parameter values that in some sense correspond to simulations from some implicit fiducial distribution. It is well known that when the distribution has group structure, stemming from the natural pivotal associated, a fiducial may be obtained. It is in the non group distributions that there appears to be still room for finding a fiducial distribution. Recently some general procedures have been proposed for dealing with generalized fiducials, but these depend on certain choices for a structural equation or a fiducial equation, as in Hannig (2009 Hannig, J. (2009). On generalized fiducial inference. Stat. Sin. 19:491–544.[Web of Science ®] , [Google Scholar]) or Taraldsen and Lindqvist (2013 Taraldsen, G., Lindqvist, B.H. (2013). Fiducial theory and optimal inference. Ann. Stat. 41(1):323–341.[Crossref], [Web of Science ®] , [Google Scholar]), respectively. A brief presentation is made of an earlier approach to fiducial inference for multivariate parameters, as in Brillinger (1962 Brillinger, D.R. (1962). Examples bearing on the definition of fiducial probability with a bibliography. Ann. Math. Stat. 33(4):1349–1355.[Crossref] , [Google Scholar]), and the implied fiducial generator introduced in Engen and Lillegård (1997 Engen, S., Lillegård, M. (1997). Stochastic simulation conditioned on sufficient statistics. Biometrika 84(1):235–240.[Crossref], [Web of Science ®] , [Google Scholar]), trying to connect them. Three interesting non group distributions are seen; two of them, the truncated exponential and the two-parameter gamma, already reported in literature. A third non group distribution is analyzed; the inverse Gaussian, connecting the fiducial that results following Brillinger (1962 Brillinger, D.R. (1962). Examples bearing on the definition of fiducial probability with a bibliography. Ann. Math. Stat. 33(4):1349–1355.[Crossref] , [Google Scholar]), with a result pertaining confidence limits for the shape parameter in Hsieh (1990 Hsieh, H.K. (1990). Inferences on the coefficient of variation of an inverse-Gaussian distribution. Commun. Stat. - Theory Methods 19(5):1589–1605.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). In the three cases, comparisons are made with the Bayesian posteriors that have been known to be close numerically. Some discussion is made on the issue of singularities of the fiducial density and its connection with densities that do not integrate to unity. As to the case of discrete observables, some comments are made for the Bernoulli distribution, only.     

Discussing Global Issues Through Contemporary Art

Contemporary global issues can be examined through the lens of modern photographic art. In an effort to prepare global-ready graduates, this article explores the pressing problems of environmental degradation, urbanization, and homelessness through the work of three contemporary artists. Illustrative works, suggested approaches, and curriculum resources are provided.