The moral basis of sustainable society: the Gandhian concept of ecological citizenship

Dominant approaches to sustainability have focused on environmental governance with efficient mechanisms and technical quick-fixes for regulatory changes and policy reforms within the growth-centred economic model. However, they fail to develop an authentic ‘ecological citizenship’ for a more fundamental change in the framework of moral values guiding individuals' behaviour and attitude towards the environment and their choices to live lightly on earth. This article argues that the transformation to a sustainable society necessitates deeper moral changes and the development of an ecological morality at the individual level as the core of sustainability. The article examines the distinctiveness of the Gandhian approach to ‘ecological citizenship’ within his paradigm of non-violence and ethical holism as an alternative to the dominant thinking. Within his broader moral-philosophical framework, the paper focuses on Gandhi's theories of eco-localism, unity of life, economics of well-being, and the moral praxis of subordinating the material to moral development realized by the human self through an ‘inner revolution’ with a goal to improve the ‘quality of man’, moving beyond the conventional ‘fear–greed’ dichotomy as motivators of behaviour to bring about a societal transformation towards a sustainable society based on freedom, equity, justice, and peace.     

Nonparametric estimator for the survival function of quality adjusted lifetime (QAL) in a three-state illness–death model

In this work, we consider nonparametric estimation of QAL distribution in a three-state illness–death model. In our approach, we first write down the expression for the distribution of QAL in terms of the joint distribution of the sojourn times in the three states. The estimate of the QAL distribution is obtained by substituting the estimates of sojourn time distributions in the expression of the QAL distribution. The proposed nonparametric estimate, assuming independence between time to illness and sojourn time in the state of illness, is uniformly consistent. Asymptotic normality has also been established. An estimate of asymptotic variance has been obtained. The performance of the proposed estimator is investigated by simulation. A data set of the Stanford Heart Transplant program has been analyzed for illustration.     

Selection of Prior for the Variance Component and Approximations for Posterior Moments in the Fay-Herriot Model

In the Fay-Herriot model, a prior distribution for the variance component allows posterior moments to be approximated with the Laplace method, avoiding computer intensive Monte Carlo Markov chains. The extremely skewed posterior distribution of the variance component results from the asymmetry of the parameter space with variance parameters constrained to be positive. The prior avoids the extreme skewness of the posterior in contrast to the commonly used uniform prior. With this prior, the mean squared error and coverage in the approximate hierarchical Bayes method are satisfactory when used to estimate small area means. Computation time is shorter than with Monte Carlo Markov chains. The approximations give easy interpretations of Bayesian methods and highlight frequentist properties of the parameters.     

Optimum release time of a software under periodic debugging schedule

In this paper, we study the problem of finding the optimal release time of a software meant to be marketed over a finite time horizon. The optimal design scheme considers reduction of the whole cost, achievement of a specified reliability with some high probability, or a combination of the both. We assume that the software is debugged periodically and the bug detection rates in the pre-release and post-release periods are different. We derive the optimal solution in each case. We also demonstrate the utility of the results through simulations, leading to different types of graphs showing the relationship between the target reliability, specified budget and the optimal number of additional debugging periods.     

Effect of reporting bias in the analysis of spontaneous reporting data

It is well‐known that a spontaneous reporting system suffers from significant under‐reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under‐reporting for the calculation of measures of association between a drug and the adverse drug reaction under study. Often there is direct and/or indirect information on the reporting probabilities. This work incorporates the reporting probabilities into existing methodologies, specifically to Bayesian confidence propagation neural network and DuMouchel's empirical Bayesian methods, and shows how the two methods lead to biased results in the presence of under‐reporting. Considering all the cases to be reported, the association measure for the source population can be estimated by using only exposure information through a reference sample from the source population. Copyright © 2014 John Wiley & Sons, Ltd.     

MultiOrd: An R Package for Generating Correlated Ordinal Data

In this article, operational details of an R package MultiOrd that is designed for the generation of correlated ordinal data are described, and examples of some important functions are given. The package provides a valuable and needed tool that has been lacking for generating multivariate ordinal data.     

PoisNor: An R package for generation of multivariate data with Poisson and normal marginals

In this article, the operational details of the R package PoisNor that is designed for simulating multivariate data with count and continuous variables with a prespecified correlation matrix are described, and examples of some important functions are given. The data-generation mechanism is a combination of the “NORmal To Anything” principle and a recently established connection between Poisson and normal correlations. The package provides a unique and useful tool that has been lacking for generating multivariate mixed data with Poisson and normal components.     

A RANDOMIZED LONGITUDINAL PLAY-THE-WINNER DESIGN FOR REPEATED BINARY DATA

In some clinical trials with two treatment arms, the patients enter the study at different times and are then allocated to one of two treatment groups. It is important for ethical reasons that there is greater probability of allocating a patient to the group that has displayed more favourable responses up to the patient's entry time. There are many adaptive designs in the literature to meet this ethical constraint, but most have a single binary response. Often the binary response is longitudinal in nature, being observed repeatedly over different monitoring times. This paper develops a randomized longitudinal play‐the‐winner design for such binary responses which meets the ethical constraint. Some performance characteristics of this design have been studied. It has been implemented in a trial of pulsed electro‐magnetic field therapy with rheumatoid arthritis patients.