Computational approach test for inference about several correlation coefficients: Equality and common

In this article, we consider inference about the correlation coefficients of several bivariate normal distributions. We first propose computational approach tests for testing the equality of the correlation coefficients. In fact, these approaches are parametric bootstrap tests, and simulation studies show that they perform very satisfactory, and the actual sizes of these tests are better than other existing approaches. We also present a computational approach test and a parametric bootstrap confidence interval for inference about the parameter of common correlation coefficient. At the end, all the approaches are illustrated using two real examples.     

The McDonald Gompertz distribution: Properties and applications

This article introduces a five-parameter lifetime model called the McDonald Gompertz (McG) distribution to extend the Gompertz, generalized Gompertz, generalized exponential, beta Gompertz, and Kumaraswamy Gompertz distributions among several other models. The hazard function of new distribution can be increasing, decreasing, upside-down bathtub, and bathtub shaped. We obtain several properties of the McG distribution including moments, entropies, quantile, and generating functions. We provide the density function of the order statistics and their moments. The parameter estimation is based on the usual maximum likelihood approach. We also provide the observed information matrix and discuss inferences issues. The flexibility and usefulness of the new distribution are illustrated by means of application to two real datasets.     

Optimal acceptance sampling policy considering Bayesian risks

In this paper, we propose a sampling policy considering Bayesian risks. Various definitions of producer's risk and consumer's risk have been made. Bayesian risks for both producer and consumer are proven to give better information to decision-makers than classical definitions of the risks. So considering the Bayesian risk constraints, we seek to find optimal acceptance sampling policy by minimizing total cost, including the cost of rejecting the batch, the cost of inspection, and the cost of defective items detected during the operation. Proper distributions to construct the objective function of the model are specified. In order to demonstrate the application of the proposed model, we illustrate a numerical example. Furthermore, the results of the sensitivity analysis show that lot size, the cost of inspection, and the cost of one defective item are key factors in sampling policies. The acceptable quality level, the lot tolerance proportion defective, and Bayesian risks also affect the sampling policy, but variations of acceptable quality level and producer Bayesian risks, for values more than a specified value, cause no changes in sampling policy.     

Determinants of Infant Mortality in Older ASEAN Economies

Infant mortality in the Association of Southeast Asian Nations (ASEAN) has been declining, yet disparities remain between the nations. This paper therefore explores the determinants of infant mortality in the older ASEAN-4 economies, Malaysia, Thailand, Indonesia and the Philippines using an Autoregressive Distributed Lag (ARDL) Error Correction Model framework. The key findings of the study are: First, there is evidence of long-run relationships among infant mortality, education, female fertility, income and access to healthcare. Second, the determinants of infant mortality vary between countries. Female fertility emerged as the main determinant of infant mortality in Malaysia, while access to healthcare matter for infant mortality in Indonesia, and to a lesser extent for the Philippines. The income effect is significant for reducing infant mortality in Malaysia, while female education is important for Indonesia and Thailand. Third, the speed of adjustment of infant mortality rate is comparatively low in ASEAN-4.     

Empirical Bayes spatial prediction using a Monte Carlo EM algorithm

This paper deals with an empirical Bayes approach for spatial prediction of a Gaussian random field. In fact, we estimate the hyperparameters of the prior distribution by using the maximum likelihood method. In order to maximize the marginal distribution of the data, the EM algorithm is used. Since this algorithm requires the evaluation of analytically intractable and high dimensionally integrals, a Monte Carlo method based on discretizing parameter space, is proposed to estimate the relevant integrals. Then, the approach is illustrated by its application to a spatial data set. Finally, we compare the predictive performance of this approach with the reference prior method.     

Deconstruction,postmodernism and philosophy of science: Some Epistemo‐critical bearings

Abstract

In the recent critique of ‘Western metaphysics’ by post‐structuralist and postmodern theorists, there has emerged a distinctive line of thought which seeks to apply such critique to the domain of political theory. This paper approaches Hannah Arendt's conceptualisation of the political as a proto‐type of such a theorisation, deploying as it does key elements of the Heideggerian (and more broadly, phenomenological) position so as to rethink the nature of the political. By delineating the specifically ‘post‐metaphysical’ moments of Arendt's theory and its corresponding critique of political modernity, I endeavour to illuminate both the advantages and pitfalls of contemporary efforts at developing a philosophical conception of the political on the basis of a neo‐Heideggerian position.     

Improved attribute acceptance sampling plans based on maxima nomination sampling

This paper demonstrates the use of maxima nomination sampling (MNS) technique in design and evaluation of single AQL, LTPD, and EQL acceptance sampling plans for attributes. We exploit the effect of sample size and acceptance number on the performance of our proposed MNS plans using operating characteristic (OC) curve. Among other results, we show that MNS acceptance sampling plans with smaller sample size and bigger acceptance number perform better than commonly used acceptance sampling plans for attributes based on simple random sampling (SRS) technique. Indeed, MNS acceptance sampling plans result in OC curves which, compared to their SRS counterparts, are much closer to the ideal OC curve. A computer program is designed which can be used to specify the optimum MNS acceptance sampling plan and to show, visually, how the shape of the OC curve changes when parameters of the acceptance sampling plan vary. Theoretical results and numerical evaluations are given.     

Development and Consideration of Global Policies for Managing the Future Risks of Poliovirus Outbreaks: Insights and Lessons Learned Through Modeling

The success of the Global Polio Eradication Initiative promises to bring large benefits, including sustained improvements in quality of life (i.e., cases of paralytic disease and deaths avoided) and costs saved from cessation of vaccination. Obtaining and maintaining these benefits requires that policymakers manage the transition from the current massive use of oral poliovirus vaccine (OPV) to a world without OPV and free of the risks of potential future reintroductions of live polioviruses. This article describes the analytical journey that began in 2001 with a retrospective case study on polio risk management and led to development of dynamic integrated risk, economic, and decision analysis tools to inform global policies for managing the risks of polio. This analytical journey has provided several key insights and lessons learned that will be useful to future analysts involved in similar complex decision-making processes.     

Some results on bootstrap prediction intervals

We investigate the construction of a BC_a-type bootstrap procedure for setting approximate prediction intervals for an efficient estimator θ_m of a scalar parameter θ, based on a future sample of size m. The results are also extended to nonparametric situations, which can be used to form bootstrap prediction intervals for a large class of statistics. These intervals are transformation-respecting and range-preserving. The asymptotic performance of our procedure is assessed by allowing both the past and future sample sizes to tend to infinity. The resulting intervals are then shown to be second-order correct and second-order accurate. These second-order properties are established in terms of min(m, n), and not the past sample size n alone.