Bayesian analysis of a general growth curve model with predictions using power transformations and AR(1) autoregressive dependence

In this paper, we consider a Bayesian analysis of the unbalanced (general) growth curve model with AR(1) autoregressive dependence, while applying the Box-Cox power transformations. We propose exact, simple and Markov chain Monte Carlo approximate parameter estimation and prediction of future values. Numerical results are illustrated with real and simulated data.     

Design of generalized CSP-C continuous sampling plan

In this paper, the concept acceptance number has been incorporated to the single level continuous sampling plan CSP-1. The advantage of the proposed plan, designated as the CSP-C plan, is to achieve a reduction in the average fraction inspected at good quality levels. Nomographs for the design of the proposed plan are presented. The expressions of the performance measures for this new plan such as OC, AOQ and AFI are also provided.     

New Environmental Theories: Toward a Coherent Theory of Environmentally Significant Behavior

This article develops a conceptual framework for advancing theories of environmentally significant individual behavior and reports on the attempts of the author's research group and others to develop such a theory. It discusses definitions of environmentally significant behavior; classifies the behaviors and their causes; assesses theories of environmentalism, focusingespecially on value-belief-norm theory; evaluates the relationship between environmental concern and behavior; and summarizes evidence on the factors that determine environmentally significant behaviors and that can effectively alter them. The article concludes by presenting some major propositions supported by available research and some principles for guiding future research and informing the design of behavioral programs for environmental protection.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

Interplay Between Sensation Seeking and Parental Rules in the Emergence of Heavy Episodic Drinking

This study examined the parallel mediational processes between sensation seeking and parental rules on alcohol, in the emergence of heavy episodic drinking (HED) in adolescents. Data were drawn from a U.K. clustered randomized control trial (control arm only, N ≈ 6,300, M_age at baseline = 12.5). Using parallel process latent growth curve analysis, stricter parental rules at baseline were found to be associated with greater declines in sensation seeking over time and a lower risk of HED at follow‐up (+33 months). Higher initial levels of sensation seeking predicted a faster relaxation of parental rules and a greater risk of HED. By maintaining strict rules about alcohol, parents may promote a positive reduction in sensation seeking and a lower risk of HED.     

Is Sexual Abuse a Unique Predictor of Sexual Risk Behaviors,Pregnancy, and Motherhood in Adolescence?

This study tested sexual abuse as a unique predictor of subsequent adolescent sexual behaviors, pregnancy, and motherhood when in company with other types of maltreatment (physical abuse, neglect) and alternative behavioral, family, and contextual risk factors in a prospective, longitudinal study of maltreated (n = 275) and comparison (n = 239) nulliparous females aged 14–19 years old assessed annually through 19 years old. Hierarchical regression was used to disentangle risk factors that account for the associations of maltreatment type on risky sexual behaviors at 19 years old, adolescent pregnancy, and adolescent motherhood. Findings indicate that sexual and physical abuse remain significant predictors of risky sexual behaviors, and that sexual abuse remains a significant predictor of adolescent motherhood when alternative explanatory variables are controlled.