Estimation of P(Y < X) for Weibull Distribution Under Progressive Type-II Censoring

Based on progressively Type II censored samples, we consider the estimation of R = P(Y < X) when X and Y are two independent Weibull distributions with different shape parameters, but having the same scale parameter. The maximum likelihood estimator, approximate maximum likelihood estimator, and Bayes estimator of R are obtained. Based on the asymptotic distribution of R, the confidence interval of R are obtained. Two bootstrap confidence intervals are also proposed. Analysis of a real data set is given for illustrative purposes. Monte Carlo simulations are also performed to compare the different proposed methods.     

On the moments of record values

In this paper some general relations for expectations of functions of record values are established. It is seen that these relations may be used to obtain recurrence relations for moments of record values. Bounds on expectations of record values with numerical computations are presented. Applications to the characterizations of the generalizeed exponential distribution are also given.     

On the Optimal Parameters for Reconstruction and Forecasting in Singular Spectrum Analysis

There are two main parameters in Singular Spectrum Analysis (SSA). The aim of this study is to determine whether the optimal values of these parameters are different for reconstruction and forecasting stages, and if those are worth the extra computational effort and time which they require. Here, we evaluate these issues using simulation study.     

On risk-averse maximum weighted subgraph problems

In this work, we consider a class of risk-averse maximum weighted subgraph problems (R-MWSP). Namely, assuming that each vertex of the graph is associated with a stochastic weight, such that the joint distribution is known, the goal is to obtain a subgraph of minimum risk satisfying a given hereditary property. We employ a stochastic programming framework that is based on the formalism of modern theory of risk measures in order to find minimum-risk hereditary structures in graphs with stochastic vertex weights. The introduced form of risk function for measuring the risk of subgraphs ensures that optimal solutions of R-MWS problems represent maximal subgraphs. A graph-based branch-and-bound (BnB) algorithm for solving the proposed problems is developed and illustrated on a special case of risk-averse maximum weighted clique problem. Numerical experiments on randomly generated Erdös-Rényi graphs demonstrate the computational performance of the developed BnB.     

Efficiency of the generalized difference-based Liu estimators in semiparametric regression models with correlated errors

In this paper, a generalized difference-based estimator is introduced for the vector parameter β in the semiparametric regression model when the errors are correlated. A generalized difference-based Liu estimator is defined for the vector parameter β in the semiparametric regression model. Under the linear nonstochastic constraint Rβ=r, the generalized restricted difference-based Liu estimator is given. The risk function for the β?_GRD(η) associated with weighted balanced loss function is presented. The performance of the proposed estimators is evaluated by a simulated data set.     

Identification of outlying height and weight data in the Iranian National Health Survey 1990-92

Data on the weights and heights of children 2-18 yeas old in Iran were obtained in a National Health Survey of 10 660 families in 1990-92. Data were 'cleaned' in 1 year age groups. After excluding gross outliers by inspection of bivariate scatter plots, Box-Cox power transformations were used to normalize the distributions of height and weight. If a multivariate Box-Cox power transformation to normality exists, then it is equivalent to normalizing the data variable by variable. After excluding gross outliers, exclusions based on the Mahalanobis distance were almost identical to those identified by Hadi's iterative procedure, because the percentages of outliers were small. In all, 1% of the observations were gross outliers and a further 0.4% were identified by multivariate analysis. Review of records showed that the outliers identified by multivariate analysis resulted from data-processing errors. After transformation and 'cleaning', the data quality was excellent and suitable for the construction of growth charts.     

Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives

In several cases, count data often have excessive number of zero outcomes. This zero-inflated phenomenon is a specific cause of overdispersion, and zero-inflated Poisson regression model (ZIP) has been proposed for accommodating zero-inflated data. However, if the data continue to suggest additional overdispersion, zero-inflated negative binomial (ZINB) and zero-inflated generalized Poisson (ZIGP) regression models have been considered as alternatives. This study proposes the score test for testing ZIP regression model against ZIGP alternatives and proves that it is equal to the score test for testing ZIP regression model against ZINB alternatives. The advantage of using the score test over other alternative tests such as likelihood ratio and Wald is that the score test can be used to determine whether a more complex model is appropriate without fitting the more complex model. Applications of the proposed score test on several datasets are also illustrated.     

A bayesian analysis of the mixed linear model

The mixed model is defined. The exact posterior distribution for the fixed effect vector is obtained. The exact posterior distribution for the error variance is obtained. The exact posterior mean and variance of a Bayesian estimator for the variances of random effects is also derived. All computations are non-iterative and avoid numerical integrations.     

The impact of in-vehicle cell-phone use on accidents or near-accidents among college students

With in-vehicle use of cell phones rapidly increasing, the safety of young drivers, who represent 14% of licensed drivers but 26% of drivers involved in fatal crashes, may be disproportionately threatened. The authors used a questionnaire to examine the association between in-vehicle cell-phone use and accidents or near-accidents among 1,291 conveniently recruited college students in 4 states. Of the 1,185 respondents who were drivers, 87% had a cell phone, and 86% of the cell-phone owners reported talking while driving at least occasionally. Of the 762 reported accidents or near-accidents, 21% (n = 159) involved at least 1 of the drivers talking while driving. Chi-square tests and logistic regression analyses showed that the frequency, not the duration, of drivers talking while driving was related to experiencing accidents or near-accidents. Differences between drivers who used cell phones and nonusers in unsafe driving behaviors and attitudes were also examined, and target groups for intervention efforts against talking on a cell phone while driving are suggested.