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A Bayesian covariance structure model (BCSM) is proposed for interval-censored multi-way nested survival data. This flexible modeling framework generalizes mixed effects survival models by allowing positive and negative associations among clustered observations. Conjugate shifted-inverse gamma priors are proposed for the covariance parameters, implying inverse gamma priors for the eigenvalues of the covariance matrix, which ensures a positive definite covariance matrix under posterior analysis. A numerically efficient Gibbs sampling procedure is defined for balanced nested designs. This requires sampling latent variables from their marginal full conditional distributions, which are derived through a recursive formula. This makes the estimation procedure suitable for interval-censored data with large cluster sizes. For unbalanced nested designs, a novel (balancing) data augmentation procedure is introduced to improve the efficiency of the Gibbs sampler. The Gibbs sampling procedure is validated in two simulation studies. The linear transformation BCSM (LT-BCSM) was applied to two-way nested interval-censored event times to analyze differences in adverse events between three groups of patients, who were randomly allocated to treatment with different stents (BIO-RESORT). The parameters of the structured covariance matrix represented unobserved heterogeneity in treatment effects and were examined to detect differential treatment effects. A comparison was made with inference results under a random effects linear transformation model. It was concluded that the LT-BCSM led to inferences with higher posterior credibility, a more profound way of quantifying evidence for risk equivalence of the three treatments, and it was more robust to prior specifications. [ABSTRACT FROM AUTHOR]

The present study aims at understanding the quantities and types of litter in and along the Nagase River, and analyzes littering behavior using a questionnaire. Plastic bags, including shopping bags and confectionery wrappers, were the most frequently observed items in the Nagase River. Cigarette-related litter (e.g. cigarette butts and packaging) and drink bottles (e.g. PET bottles, beverage cans) were the most frequently observed types of litter on the streets along the Nagase River. The questionnaires completed by passers-by and students in the schools and a university near the Nagase River were analyzed (N=731). The rate of those who actually littered in the Nagase River and the streets along the river was 14%, and the main kinds of litters were confectionary wrappers, cigarette-related, PET and glass bottles, cans, and plastic bags. The questionnaire results were subjected to covariance structure analysis to determine factors contributing to littering behavior. The results show that, social norms are the most important factor influencing the act of littering, and awareness of the anti-social nature of littering in daily life is strongly related. [ABSTRACT FROM AUTHOR]

In this paper, we study the finite sample behavior of an over-identifying restriction test, the J test, in generalized method of moments(GMM). We consider two variants of the J test, one with centered weighting matrix and the other with uncentered weighting matrix. We demonstrate that the finite sample distribution of J test with centered weighting matrix is close to F distribution whereas that with uncentered weighting matrix is close to beta distribution and that both are far different from chi-square distribution. Using this, we demonstrate why competing simulation results relating to the size property is reported in the context of dynamic panel data and covariance structure models. [ABSTRACT FROM AUTHOR]

Lead time (LT) is a core parameter that varies and affects all supply chain (SC) partners. LT uncertainty is known as a kind of supply uncertainty in the SC literature which attracts attention of many researchers. In this paper, impact of LT variability in a serially connected supply chain with 4 levels is investigated. The study includes two main stages: development of a structural model and testing the structural relations by using simulated data. Simulation makes it possible to remove effects of intervener variables in order to measure pure impact of LT uncertainty on SC. Regarding this matter, a structural relationship model with three hypotheses about direct and indirect impact of LT uncertainty on supply chain inventories have been developed. To test the proposed structural relations, simulated data were used. The results of the study show that by increasing the lead time variance, order variances increase while no impact on the bullwhip is observed, i.e. the order variances increase uniformly throughout the entire SC. Furthermore, results show that the increase in the lead time variance will lead to inventory fluctuations. One practical recommendation of this study is to apply an investment strategy to reduce the lead time uncertainty. [ABSTRACT FROM AUTHOR]

Theory of variable selection for structural models that do not have clear dependent variables is developed. Theory is derived within the framework of the curved exponential family of distributions for observed variables. The idea of Rao''s score test was taken to construct a test statistic for variable selection, and its statistical properties are examined. In particular, the test statistic is shown to have asymptotic central chi-square distribution under a kind of alternative hypothesis. This fact will provide an evidence for excellent performance of the score statistic for real data sets. [Copyright &y& Elsevier]

Agnew's (1985) revised strain theory argues that delinquency results from the inability to escape legally from painful or aversive situations. There is much indirect support for the theory, with experimental and survey data indicating that delinquency is associated with a wide variety of aversive situations. The experimental data, however, are of questionable generalizability and the survey data are primarily cross sectional in nature, leaving unresolved the issue of causal direction. This study examines the relationship between environmental adversity and delinquency using longitudinal data from a nationally representative sample of adolescent boys. A distribution-free method (arbitrary generalized least squares) is used to estimate a covariance structure model depicting a reciprocal relationship between adversity and delinquency. The results support the revised strain theory: environmental adversity has a causal effect on delinquency, but delinquency does not have an effect on adversity. [ABSTRACT FROM AUTHOR]