Domains of convergence for the EM algorithm: a cautionary tale in a location estimation problem

The EM algorithm is a popular method for maximizing a likelihood in the presence of incomplete data. When the likelihood has multiple local maxima, the parameter space can be partitioned into domains of convergence, one for each local maximum. In this paper we investigate these domains for the location family generated by the t-distribution. We show that, perhaps somewhat surprisingly, these domains need not be connected sets. As an extreme case we give an example of a domain which consists of an infinite union of disjoint open intervals. Thus the convergence behaviour of the EM algorithm can be quite sensitive to the starting point.     

Estimation of critical points in the mixture inverse Gaussian model

The maximum likelihood estimation for the critical points of the failure rate and the mean residual life function are presented in the case of mixture inverse Gaussian model. Several important data sets are analyzed from this point of view. For each of the data sets, Bootstrapping is used to construct confidence intervals of the critical points.     

Stochastic analysis of covariance when the error distribution is long-tailed symmetric

In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.     

Variable selection in elliptical linear mixed model

Variable selection in elliptical Linear Mixed Models (LMMs) with a shrinkage penalty function (SPF) is the main scope of this study. SPFs are applied for parameter estimation and variable selection simultaneously. The smoothly clipped absolute deviation penalty (SCAD) is one of the SPFs and it is adapted into the elliptical LMM in this study. The proposed idea is highly applicable to a variety of models which are set up with different distributions such as normal, student-t, Pearson VII, power exponential and so on. Simulation studies and real data example with one of the elliptical distributions show that if the variable selection is also a concern, it is worthwhile to carry on the variable selection and the parameter estimation simultaneously in the elliptical LMM.     

Robust Liu estimator for regression based on an M-estimator

Consider the regression model y = beta 0 1 + Xbeta + epsilon. Recently, the Liu estimator, which is an alternative biased estimator beta L (d) = (X'X + I) -1 (X'X + dI)beta OLS , where 0<d<1 is a parameter, has been proposed to overcome multicollinearity . The advantage of beta L (d) over the ridge estimator beta R (k) is that beta L (d) is a linear function of d. Therefore, it is easier to choose d than to choose k in the ridge estimator. However, beta L (d) is obtained by shrinking the ordinary least squares (OLS) estimator using the matrix (X'X + I) -1 (X'X + dI) so that the presence of outliers in the y direction may affect the beta L (d) estimator. To cope with this combined problem of multicollinearity and outliers, we propose an alternative class of Liu-type M-estimators (LM-estimators) obtained by shrinking an M-estimator beta M , instead of the OLS estimator using the matrix (X'X + I) -1 (X'X + dI).     

The Determinants of Retail Productivity: A Critical Review of the Evidence

This paper discusses the literature on the established determinants of productivity in the retail sector. It also draws attention to some neglected strands of research which provide useful insights into strategies that could allow productivity enhancements in this area of the economy. To date, very few attempts have been made to integrate different specialisms in order to explain what drives productivity in retail. Here this paper rectifies this omission by putting together studies from economics, geography, knowledge management and employment studies. It is the authors’ view that quantitative studies of retail productivity should focus on total factor productivity in retailing as the result of competition/composition effects, planning regulations, information and communications technology, the multinational operation element and workforce skills. Further, the fact that retail firms possess advantages that are transferable between locations suggests that investment in strategies enhancing the transfer of explicit and tacit knowledge between and within businesses are crucial to achieve productivity gains.     

Mixtures of Farlie-Gumbel-Morgenstern Copulas

Some modifications of bivariate Farlie-Gumbel-Morgenstern (FGM) copulas are going to be explained in this article. These modifications are generated by using mixtures of bivariate FGM copula functions. The main goal of this study is to determine both the ranges of association parameter and the rate of correlation, and also observe the changes in local dependence function. An application, which is related with simulated data, is conducted and results are illustrated.     

Foreign Employers as Relief Routes: Women,Multinational Corporations and Managerial Careers in Japan

This article argues that multinational corporations may provide critical relief routes for women workers' progress in managerial careers in national contexts where their career paths with domestic employers remain blocked by traditional and institutional practices. It illustrates this possibility through a study of two women managers at the local head office of a foreign‐owned multinational retailer in Japan and their career trajectories. The alternative career paths through foreign employers are not without their contingencies and constraints, and the article identifies the limitations of the transformative potential foreign employers could have in the larger realm of women's managerial employment in a restrictive context such as Japan. Noting that globalization incorporates different groups of workers into the global economy with different costs and rewards, the article concludes by calling for a more nuanced understanding of women's employment with multinationals and for further research that remains cognizant of the multiplicity of experiences in different contexts.