The general class of chain estimators for the ratio of two means using double sampling

A wider class of chain based estimators for the ratio of two means of a finite population has been proposed by using two auxiliary variables. Singh et al.(1994) is a particular case of this class.     

CAN JUSTICE AND SHAME DETER AN ESCALATION IN TAX EVASION?

This study examines the roles that justice and shame management play in deterring escalation in tax evasion. Data were taken from the Graduates' Hopes, Visions and Actions Survey based on a sample of 447 Australian graduates who recently received their tertiary degrees. Path analysis reveals that both justice variables (societal and personal) indirectly affect tax non‐compliance through shame (or lack thereof). Injustice brings about a significant increase in unacknowledged shame which, in turn, leads to increased tax evasion. The path to tax evasion is also mediated by an attitude condoning non‐compliance, and dissociation. Of considerable interest is the finding that personal injustice, in contrast to societal injustice, has a direct link to tax evasion. The results of the study demonstrate that felt injustice triggers both emotional and cognitive responsiveness, and works to undermine moral obligation in tax compliance.     

Multiple imputation compared with restricted pseudo‐likelihood and generalized estimating equations for analysis of binary repeated measures in clinical studies

Non‐likelihood‐based methods for repeated measures analysis of binary data in clinical trials can result in biased estimates of treatment effects and associated standard errors when the dropout process is not completely at random. We tested the utility of a multiple imputation approach in reducing these biases. Simulations were used to compare performance of multiple imputation with generalized estimating equations and restricted pseudo‐likelihood in five representative clinical trial profiles for estimating (a) overall treatment effects and (b) treatment differences at the last scheduled visit. In clinical trials with moderate to high (40–60%) dropout rates with dropouts missing at random, multiple imputation led to less biased and more precise estimates of treatment differences for binary outcomes based on underlying continuous scores. Copyright © 2005 John Wiley & Sons, Ltd.     

People as partners: Facilitating people's participation in public–private partnerships for solid waste management

Cities in developing countries are facing a double dilemma. On one hand, the urban population is growing rapidly, causing a huge increase in demand for waste management services. On the other hand, the traditional public sector is failing to respond to the increased demand for service. The public sector is constrained by resource and institutional limitations. It is often proposed that the solution lies in private sector participation. It is expected that the private sector, with its dynamism and flexibility, may fill in the service delivery gaps by forming partnership with the public sector. However, a third tier—the people—is often overlooked in the service delivery framework. Citizens can contribute significantly to service delivery. They can support the private sector with payment of service charges. But more importantly, they can play an active role in improving accountability and service quality of both public and private sector. This radical shift in people's role, from passive service receivers to active service partners, however, may not occur endogenously. External help from facilitating agencies may enable the public and private sector to form partnership with people for better service delivery. This article examines the role of facilitating agencies in developing tripartite partnership for solid waste management service in Bangladesh. The key lessons learned are: a number of obstacles prevented spontaneous partnership among the public sector, private sector and people; facilitating agencies were able to overcome the obstacles to form partnership of the three; and, the tripartite arrangement resulted in higher accountability and better service delivery.     

Estimators for the Finite Mixture of Rayleigh Model Based on Progressively Censored Data

In this article, based on progressively Type-II censored samples from a heterogeneous population that can be represented by a finite mixture of two-component Rayleigh lifetime model, the problem of estimating the parameters and some lifetime parameters (reliability and hazard functions) are considered. Both Bayesian and maximum likelihood estimators are of interest. A class of natural conjugate prior densities is considered in the Bayesian setting. The Bayes estimators are obtained using both the symmetric (squared error) loss function, and the asymmetric (LINEX and General Entropy) loss functions. It has been seen that the estimators obtained can be easily evaluated for this type of censoring by using suitable numerical methods. Finally, the performance of the estimates have been compared on the basis of their simulated maximum square error via a Monte Carlo simulation study.     

Approximate MLEs of the parameters of location-scale models under type II censoring

Log-location-scale distributions are widely used parametric models that have fundamental importance in both parametric and semiparametric frameworks. The likelihood equations based on a Type II censored sample from location-scale distributions do not provide explicit solutions for the para-meters. Statistical software is widely available and is based on iterative methods (such as, Newton Raphson Algorithm, EM algorithm etc.), which require starting values near the global maximum. There are also many situations that the specialized software does not handle. This paper provides a method for determining explicit estimators for the location and scale parameters by approximating the likelihood function, where the method does not require any starting values. The performance of the proposed approximate method for the Weibull distribution and Log-Logistic distributions is compared with those based on iterative methods through the use of simulation studies for a wide range of sample size and Type II censoring schemes. Here we also examine the probability coverages of the pivotal quantities based on asymptotic normality. In addition, two examples are given.     

Reliability estimation in stress–strength models: an MCMC approach

In this paper, we consider the estimation of the stress–strength parameter R=P(Y<X) when X and Y are independent and both are modified Weibull distributions with the common two shape parameters but different scale parameters. The Markov Chain Monte Carlo sampling method is used for posterior inference of the reliability of the stress–strength model. The maximum-likelihood estimator of R and its asymptotic distribution are obtained. Based on the asymptotic distribution, the confidence interval of R can be obtained using the delta method. We also propose a bootstrap confidence interval of R. The Bayesian estimators with balanced loss function, using informative and non-informative priors, are derived. Different methods and the corresponding confidence intervals are compared using Monte Carlo simulations.     

Social Protection Spending in Context of Structural and Institutional Performance: A Global Empirical Analysis

Social Indicators Research - An understanding about the dynamics of social protection spending is made in the context of structural and institutional performances. A dataset of 134 countries for...