Bayesian estimation for the Pareto income distribution

The Bayes estimators of the Gini index, the mean income and the proportion of the population living below a prescribed income level are obtained in this paper on the basis of censored income data from a pareto income distribution. The said estimators are obtained under the assumptions of a two-parameter exponential prior distribution and the usual squared error loss function. This work is also extended to the case when the income data are grouped and the exact incomes for the individuals in the population are not available. The method for the assessment of the hyperparameters is also outlined. Finally, the results are generalized for the doubly truncated gamma prior distribution. Now deceased.     

Sexual Harassment of Women Working as Room Attendants within 5‐Star Hotels

Given the gendered power relations and the isolated nature of women hotel room attendants' working environments, guest‐initiated sexual harassment experienced by room attendants is a significant, under‐investigated problem. This study of women attendants' experiences of sexual harassment was conducted in 5‐star hotels located on the Gold Coast — a notable tourism destination — of Queensland, Australia. Adopting a socialist–feminist critical theory epistemological perspective, the study used a qualitative constructivist grounded theory methodology. The research reveals the pervasiveness of sexual harassment experienced by women hotel room attendants. In particular, this study illuminates the varied forms, meanings and consequences of sexual harassment in a particular organizational context. In focusing on the interacting effects of the gendered nature of the hotel workplace and the hotel workplace culture, the near‐complete ‘normalization’ of sexual harassment within the hotels is revealed. This outcome is a source of considerable concern, with implications for the industry, for employment relations institutions and for public policy.     

Supplier‐Switching Inertia and Competitive Asymmetry: A Demand‐Side Perspective*

Building on strategic management, operations strategy, and supplier management literatures, this article presents a framework for supplier selection from the demand‐side perspective. We highlight the role of a purchasing firm's switching inertia in the supplier‐selection process and demonstrate the usefulness of our framework for the industrial automation industry. Empirical data for this study was collected from 171 corporate and plant‐level executives in pharmaceutical, chemical, and paper‐and‐pulp manufacturing industries in the United States. A series of Web‐based individually customized discrete choice experiments asked the respondents to either switch to the new supplier or stay with the existing supplier. Based on the results of these experiments, we demonstrate the existence of switching inertia in the supplier‐selection process and discuss the managerial implications for incumbent and challenger supplier firms.     

Bayesian Estimation of Regression Coefficients Under Extended Balanced Loss Function

Appreciating the desirability of simultaneously using both the criteria of goodness of fitted model and clustering of estimates around true parameter values, an extended version of the balanced loss function is presented and the Bayesian estimation of regression coefficients is discussed. The thus obtained optimal estimator is then compared with the least squares estimator and posterior mean vector with respect to the criteria like posterior expected loss, Bayes risk, bias vector, mean squared error matrix and risk function.     

Bayesian analysis of the linear regression model with an edgeworth series prior distribution

The purpose of the present investigation 1s to observe the effect of departure from normahty of the prior distribution of regresslon parameters on the Bayman analysis of a h e a r regresslon model Assuming an Edgeworth serles prior distribution for the regresslon coefficients and gamma prior for the disturbances precision, the expressions for the posterlor distribution, posterlor mean and Bayes risk under a quadratic loss function are obtalned The results of a numerical evaluation are also analyzed     

Bayesian predictive analysis of the linear regression model with an edgeworth series prior distribution

In this paper we obtain the Bayes forecasts for the future observations on the dependent variable in the linear regression model when the regression coefficients have an Edgeworth series prior distribution. Furthermore, we consider the effect of departure from normality of the prior distribution of regression coefficients on the Bayes forecasts.     

BAYESIAN ANALYSIS OF THE LINEAR REGRESSION MODEL WITH NON-NORMAL DISTURBANCES

This paper considers the Bayesian analysis of a linear regression model with identically independently distributed non-normal disturbances. The distribution of disturbances is approximated by an Edgeworth series distribution with cumulants, of order higher than fourth, negligible. The posterior distribution of the regression coefficients vector is obtained under the assumption of a g-prior distribution for the parameters of the model. The Bayes estimator and its Bayes risk of the estimator are derived under a quadratic loss structure.     

Stein rule prediction of the composite target function in a general linear regression model

This paper considers the problem of simultaneous prediction of the actual and average values of the dependent variable in a general linear regression model. Utilizing the philosophy of Stein rule procedure, a family of improved predictors for a linear function of the actual and expected value of the dependent variable for the forecast period has been proposed. An unbiased estimator for the mean squared error (MSE) matrix of the proposed family of predictors has been obtained and dominance of the family of Stein rule predictors over the best linear unbiased predictor (BLUP) has been established under a quadratic loss function.     

Bayesian analysis of a linear model involving structural changes in either regression parameters or disturbances precision

ABSTRACT

The present paper considers the Bayesian analysis of a linear regression model involving structural change, which may occur either due to shift in disturbances precision or due to shift in regression parameters. The posterior density for the regression parameter has been derived and posterior odds ratio for testing the hypothesis that structural change is due to shift in disturbances precision against the alternative that the change is due to shift in regression parameters has been obtained. The findings of a numerical simulation have been presented. The proposed model has been applied to RBI data set on corporate sector.