Network design for reverse logistics

Collection and recycling of product returns is gaining interest in business and research worldwide. Growing green concerns and advancement of green supply chain management (GrSCM) concepts and practices make it all the more relevant. Inputs from literature and informal interviews with 84 stakeholders are used to develop a conceptual model for simultaneous location–allocation of facilities for a cost effective and efficient reverse logistics (RL) network. We cover costs and operations across a wide domain and our proposed RL network consists of collection centers and two types of rework facilities set up by original equipment manufacturers (OEMs) or their consortia for a few categories of product returns under various strategic, operational and customer service constraints in the Indian context.     

Profile analysis of several groups

In this paper, profile analysis of several groups is considered. The maximum likelihood estimate, along with its covariance matrix, is given for the “level differences” between the grops. Likelihood ratio test procedures, with their distributions, are given for the three hypothesis known in the literature as “parallelism”, “level hypothesis” and “ no condition variation”. In the literature, even for the two groups case, some procedures are not likelihood procedures.     

Economical process adjustment with measurement error

Srivastava and Wu (1997) considered a random walk model with sampling interval and measurement error which was assumed to be white noise. In this paper, we consider the situation in which the measurement error is also a random walk. It is assumed that there is a sampling cost and an adjustment cost. The cost of deviating from the target value is assumed to be proportional to the square of the deviations. The long-run average cost rate is evaluated exactly in terms of the first four moments of a randomly stopped random walk. Using approximations of those moments, optimum, values of the control parameters are given.     

Searching effective rotation patterns for population median using exponential type estimators in two-occasion rotation sampling

ABSTRACT

The present article is an attempt to explore the rotation patterns using exponential ratio type estimators for the estimation of finite population median at current occasion in two occasion rotation sampling. Properties of the proposed estimators including the optimum replacement strategies have been elaborated. The proposed estimators have been compared with sample median estimator when there is no matching from previous occasion as well with the ratio type estimator proposed by Singh et al. (2007 Singh, H.P., Tailor, R., Singh, S., Kim, Jong-Min. (2007). Quintile estimation in successive sampling. J. Kor. Stat. Soc. 36(4):543–556. [Google Scholar]) for second quantile. The behaviors of the proposed estimators are justified by empirical interpretations and validated by means of simulation study with the help of some natural populations.     

Optimal linear projections for enhancing desired data statistics

Problems involving high-dimensional data, such as pattern recognition, image analysis, and gene clustering, often require a preliminary step of dimension reduction before or during statistical analysis. If one restricts to a linear technique for dimension reduction, the remaining issue is the choice of the projection. This choice can be dictated by desire to maximize certain statistical criteria, including variance, kurtosis, sparseness, and entropy, of the projected data. Motivations for such criteria comes from past empirical studies of statistics of natural and urban images. We present a geometric framework for finding projections that are optimal for obtaining certain desired statistical properties. Our approach is to define an objective function on spaces of orthogonal linear projections—Stiefel and Grassmann manifolds, and to use gradient techniques to optimize that function. This construction uses the geometries of these manifolds to perform the optimization. Experimental results are presented to demonstrate these ideas for natural and facial images.     

Ordinary least squares and Stein-rule predictions in regression models under inclusion of some superfluous variables

This article considers a misspecified linear regression model in which misspecification relates to the inclusion of some explanatory variables. Assuming the distribution of disturbances to be not necessarily normal, this paper investigates the efficiency properties of predictions arising from ordinary least squares and Stein-rule when the aim is to predict either the actual value or the mean value of the study variable.