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.     

Posterity and population growth: fertility intention among a cohort of Nigerian adolescents

Population growth is probably the greatest global challenge of the twenty-first century and fertility is a central element of this growth. Fertility is a human attribute which depends almost entirely on social, economic, political, cultural and psychological frameworks, making fertility intention an element of what individuals learn from a very young age as part of their socialisation into society. The fundamental significance of socio-psychological, environmental and cultural factors in what adolescents are assimilating on fertility cannot be exaggerated, yet, relevant information is limited. Eight factors deduced from ecological model and theory of planned behaviour were used to predict fertility intention among a cohort of Nigerian adolescents, using cross-sectional design. Mean fertility intention was 4.06 ± 1.34. Age and religion had no effect, but gender did. Self esteem, perceived parental expectation of fertility, attitude towards fertility and peer-related subjective norm are significant predictors. Media and ethnic attitude are insignificant predictors of, but are significantly related to fertility intention. Attitude towards a four-child family and perceived behavioural control yielded insignificant relationships with, and also failed to predict fertility intention. Perceived parental expectation of fertility, an interpersonal factor of the ecological model is the single most important predictor (β = 0.707, R² = 0.506, r = 0.711, and partial r = 0.710). Fertility intention points towards fertility decline, though sluggish and diminutive, thereby failing to reflect the need of Nigeria’s population pyramid to thin out from the base.

     

Emphasizing the experiences of researchers after RCR instructions: Introduction to Responsible Conduct of Research (RCR) in Malaysia

The purpose of this study is to highlight the experiences of individuals who participate in the Responsible Conduct of Research (RCR) training program held at various universities in Malaysia. In response to a mailing request sent to 40 individuals who had undertaken a RCR training program, 15 participants agreed to be interviewed. The results of the study showed that the three main reasons for participating in the training were as follows: anticipation for knowledge gained; personal experience with research misconduct; and establishing a new network of researchers. In terms of the positive effects gained from undertaking the training, the participants highlighted an increased awareness of the issues and problems related to research misconduct; the need to promote integrity in research conduct; a change in the way they conduct their research; and a change in the way they confront and address misconduct. The findings of this study should be valuable for policy makers and those involved in the management of research programs and ethics, as it demonstrated the importance of RCR training in equipping researchers with the necessary knowledge to conduct research responsibly, and to avoid research misconduct.