Mixed ranked set sampling design

The main focus of agricultural, ecological and environmental studies is to develop well designed, cost-effective and efficient sampling designs. Ranked set sampling (RSS) is one method that leads to accomplish such objectives by incorporating expert knowledge to its advantage. In this paper, we propose an efficient sampling scheme, named mixed RSS (MxRSS), for estimation of the population mean and median. The MxRSS scheme is a suitable mixture of both simple random sampling (SRS) and RSS schemes. The MxRSS scheme provides an unbiased estimator of the population mean, and its variance is always less than the variance of sample mean based on SRS. For both symmetric and asymmetric populations, the mean and median estimators based on SRS, partial RSS (PRSS) and MxRSS schemes are compared. It turns out that the mean and median estimates under MxRSS scheme are more precise than those based on SRS scheme. Moreover, when estimating the mean of symmetric and some asymmetric populations, the mean estimates under MxRSS scheme are found to be more efficient than the mean estimates with PRSS scheme. An application to real data is also provided to illustrate the implementation of the proposed sampling scheme.     

Bayesian Transformation Models for Multivariate Survival Data

In this paper, we propose a general class of Gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported.     

Acknowledgement

We gratefully acknowledge the partial support of the National Institute of Environmental Health Sciences through Training Grant #T32 ESO 7018 and Research Grant #ROI ESO 2286, also the partial support of Special Projects funded by COllncil for Tobacco Research - U.S.A., Inc.     

Fitting a multiple regression function

Consider the p-dimensional unit cube [0,1]^p, p≥1. Partition [0, 1]^p into n regions, R_1,n,…,R_n,n such that the volume Δ(R_j,n) is of order n^?1,j=1,…,n. Select and fix a point in each of these regions so that we have x⁽ⁿ⁾₁,…,x⁽ⁿ⁾_n. Suppose that associated with the j-th predictor vector x⁽ⁿ⁾_j there is an observable variable Y⁽ⁿ⁾_j, j=1,…,n, satisfying the multiple regression model $\text{Y}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{=g(}\text{x}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{)+e}{}^{\mathrm{(n)}}{}_{\mathrm{j}}$, where g is an unknown function defined on [0, 1]^pand {e⁽ⁿ⁾_j} are independent identically distributed random variables with Ee⁽ⁿ⁾₁=0 and Var e⁽ⁿ⁾₁=σ²<∞. This paper proposes as an estimator of g(x), where k(u) is a known p-dimensional bounded density and {a_n} is a sequence of reals converging to 0 asn→∞. Weak and strong consistency of g_n(x) and rates of convergence are obtained. Asymptoticnormality of the estimator is established. Also proposed is $\text{σ}{}^2{}_{\mathrm{n}}\text{=n}{}^{\mathrm{?1}}\text{Σ}{}^{\mathrm{n}}{}_{\mathrm{j=1}}\text{(Y}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{?g}{}_{\mathrm{n}}\text{(}\text{x}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{))}{}^2$ as a consistent estimate of σ².     

On the normal approximation of an estimate of the mean residual life of a multicomponent system

An estimate of the mean residual life function of a complex system of k independent identically distributed components is proposed and studied with emphasis being on the order of normal approximation.     

An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems

The main characteristic of today's manufacturing environments is volatility. Under a volatile environment, demand is not stable. It changes from one production period to another. To operate efficiently under such environments, the facilities must be adaptive to changing production requirements. From a layout point of view, this situation requires the solution of the dynamic layout problem (DLP). DLP is a computationally complex combinatorial optimization problem for which optimal solutions can only be found for small size problems. It is known that classical optimization procedures are not adequate for this problem. Therefore, several heuristics including taboo search, simulated annealing and genetic algorithm are applied to this problem to find a good solution. This work makes use of the ant colony optimization (ACO) algorithm to solve the DLP by considering the budget constraints. The paper makes the first attempt to show how the ACO can be applied to DLP with the budget constraints. In the paper, example applications are presented and computational experiments are performed to present suitability of the ACO to solve the DLP problems. Promising results are obtained from the solution of several test problems.     

On Bayesian Inference for Proportional Hazards Models Using Noninformative Priors

In this article, we investigate the propertiesof the posterior distribution under the uniform improper priorfor two commonly used proportional hazards models; the Weibullregression model and the extreme value regression model. We allowthe observations to be right censored. We obtain sufficient conditionsfor the existence of the posterior moment generating functionof the regression coefficients. A dataset involving a lung cancerclinical trial and a simulation are presented to illustrate ourresults.     

On the identification of extreme outliers and dragon-kings mechanisms in the upper tail of income distribution

The presence of extreme outliers in the upper tail data of income distribution affects the Pareto tail modeling. A simulation study is carried out to compare the performance of three types of boxplot in the detection of extreme outliers for Pareto data, including standard boxplot, adjusted boxplot and generalized boxplot. It is found that the generalized boxplot is the best method for determining extreme outliers for Pareto distributed data. For the application, the generalized boxplot is utilized for determining the exreme outliers in the upper tail of Malaysian income distribution. In addition, for this data set, the confidence interval method is applied for examining the presence of dragon-kings, extreme outliers which are beyond the Pareto or power-laws distribution.     

Antecedents and Consequences of Marital Satisfaction in an Emirati Sample: A Structural Equation Model Analysis

This study surveyed 1,000 married Emirati men and women to explore a model of marital satisfaction in relation to self-esteem, satisfaction with life, gender-role attitudes, and general health (physical and psychological). The Emirati Marital Satisfaction Scale (EMSS), State Self-Esteem Scale (SSES), Satisfaction With Life Scale (SWLS), the Arabic General Health Questionnaire, and the Sex-Role Egalitarianism Scale were used. Confirmatory factor analysis (CFA) on the EMSS indicated a moderate fit of the data to one factor structure. CFA on SSES and SWLS indicated an adequate fit of the data to the four-factor solution and one factor, respectively. The structural equation model for the EMSS showed that both general health and gender-role attitudes predict marital satisfaction and that marital satisfaction seems to predict the individual’s well-being as measured by life satisfaction and self-esteem. These results are discussed within the context of the United Arab Emirates culture and directions for future research are provided.     

Contamination,deception and ‘othering’: the media framing of the horsemeat scandal

Food and consumption practices are cultural symbols of communities, nations, identity and a collective imaginary which bind people in complex ways. The media framed the 2013 horsemeat scandal by fusing discourses beyond the politics of food. Three recurrent media frames and dominant discourses converged with wider political debates and cultural stereotypes in circulation in the media around immigration and intertextual discourse on historical food scandals. What this reveals is how food consumption and food-related scandals give rise to affective media debates and frames which invoke fear of the other and the transgression of a sacred British identity, often juxtaposing ‘Britishness’ with a constructed ‘Otherness’.