Bayesian inference using product of spacings function for Progressive hybrid Type-I censoring scheme

This article is devoted to the development of product of spacings estimator for a Progressive hybrid Type-I censoring scheme with binomial removals. The experimental units are assumed to follow inverse Lindley distribution. We propose a Bayes estimator of associated scale parameter based on the product of spacings function and simultaneously compare it with that obtained under a usual Bayesian estimation procedure. The estimators are obtained under the squared error loss function along with corresponding HP intervals evaluated by using the Markov chain Monte-Carlo technique. The classical product of spacings estimator has also been derived and compared with the maximum likelihood estimator in addition to 95% average asymptotic confidence intervals. The applicability of the proposed methods is demonstrated by analysing a real data of guinea pigs affected with tuberculosis for the considered censoring scheme.     

CONSEQUENCES OF OFFSHORING TO DEVELOPING NATIONS: LABOR-MARKET OUTCOMES,WELFARE, AND CORRECTIVE INTERVENTIONS

Our parsimonious two-country (developed country and developing country) model of offshoring provides nuanced results. These include cases where wages monotonically improve, as well as where wages exhibit an inverted-U relationship with offshoring cost reductions. We identify conditions under which these relationships hold. Since global welfare always rises with improvements in offshoring technology, we find that there is a role for a minimum wage (alternatively, wage tax) in the developing country. We derive such a policy's optimal level. There is also the possibility of a developed country optimal offshoring tax for extracting terms-of-trade benefits. We, finally, analyze the two-country Nash equilibrium in policies. (JEL F11, F13, F16, F66, O19, O24)     

Minimum disparity estimation based on combined disparities: Asymptotic results

The maximum likelihood estimator (MLE) is asymptotically efficient for most parametric models under standard regularity conditions, but it has very poor robustness properties. On the other hand some of the minimum disparity estimators like the minimum Hellinger distance estimator (MHDE) have strong robustness features but their small sample efficiency at the model turns out to be very poor compared to the MLE. Methods based on the minimization of some combined disparities can substantially improve their small sample performances without affecting their robustness properties (Park et al., 1995). All studies involving the combined disparity have so far been empirical, and there are no results on the asymptotic properties of these estimators. In view of the usefulness of these procedures this is a major gap in theory, which we try to fill through the present work. Some illustrations of the performance of the estimators and the corresponding tests are also provided.     

Robust and efficient parameter estimation based on censored data with stochastic covariates

Analysis of random censored life-time data along with some related stochastic covariables is of great importance in many applied sciences. The parametric estimation technique commonly used under this set-up is based on the efficient but non-robust likelihood approach. In this paper, we propose a robust parametric estimator for censored data with stochastic covariates based on the minimum density power divergence approach. The resulting estimator also has competitive efficiency with respect to the maximum likelihood estimator under pure data. The strong robustness property of the proposed estimator with respect to the presence of outliers is examined and illustrated through an appropriate real data example and simulation studies. Further, the theoretical asymptotic properties of the proposed estimator are also derived in terms of a general class of M-estimators based on the estimating equation.     

Aggregating Infinite Utility Streams with InterGenerational Equity: The Impossibility of Being Paretian

It has been known that, in aggregating infinite utility streams, there does not exist any social welfare function, which satisfies the axioms of Pareto, intergenerational equity, and continuity. We show that the impossibility result persists even without imposing the continuity axiom, and in frameworks allowing for more general domains of utilities than those used in the existing literature.     

Who Gets to ‘Work Hard,Play Hard’? Gendering the Work–Life Balance Rhetoric in Canadian Tech Companies

This article is based on an exploratory study of the implicit gender norms in work–life balance (WLB) rhetoric in ten Canadian information and communication technologies (ICT) organizations. Interviews with human resources (HR) managers and preliminary company website analysis revealed a masculinist and heterosexist bias in the implementation of WLB practices, legitimized by the gender composition of the workforce and the demanding yet inherently rewarding nature of the ICT sector. Participants deliberately separated care (read: childcare) from WLB (read: flexible hours and working from home), reproducing the assumption that an ‘ordinary’ worker is a man with a female partner who assumes primary responsibility for the reproductive realm. The study concludes with: (i) recommendations to increase HR's role in providing functional support for WLB practices and (ii) three future directions for research. This article contributes to a general call in feminist scholarship to apply a gendered lens to WLB practices.     

ROBUST INFERENCE IN PARAMETRIC MODELS USING THE FAMILY OF GENERALIZED NEGATIVE EXPONENTIAL DISPARITIES

We examine robust estimators and tests using the family of generalized negative exponential disparities, which contains the Pearson's chi‐square and the ordinary negative exponential disparity as special cases. The influence function and α‐influence function of the proposed estimators are discussed and their breakdown points derived. Under the model, the estimators are asymptotically efficient, and are shown to have an asymptotic breakdown point of 50%. The proposed tests are shown to be equivalent to the likelihood ratio test under the null hypothesis, and their breakdown points are obtained. The competitive performance of the proposed estimators and tests relative to those based on the Hellinger distance is illustrated through examples and simulation results. Unlike the Hellinger distance, several members of this family of generalized negative exponential disparities generate estimators which also possess excellent inlier‐controlling capability. The corresponding tests of hypothesis are shown to have better power breakdown than the Hellinger deviance test in the cases examined.     

Generalized Wald-type tests based on minimum density power divergence estimators

In testing of hypothesis, the robustness of the tests is an important concern. Generally, the maximum likelihood-based tests are most efficient under standard regularity conditions, but they are highly non-robust even under small deviations from the assumed conditions. In this paper, we have proposed generalized Wald-type tests based on minimum density power divergence estimators for parametric hypotheses. This method avoids the use of nonparametric density estimation and the bandwidth selection. The trade-off between efficiency and robustness is controlled by a tuning parameter β. The asymptotic distributions of the test statistics are chi-square with appropriate degrees of freedom. The performance of the proposed tests is explored through simulations and real data analysis.