Dilemma of a Native: On Location,Authenticity, and Reflexivity

With the focus on native/halfie anthropology, I address in this paper three issues of location, authenticity, and reflexivity of the anthropologist in close connection to the reality of uneven distribution of power inside academe among scholars and institutions, and, above all, anthropologists, including native anthropologists. I emphasise that interaction between the field and the ethnographer never closes even after the fieldwork is over; the issue of intersubjectivity is not confined in writing one book, but permeates an ethnographer's lifelong career; the power relation is no longer divided only between the field and the home institution, but also divided within so-called Western academe. Through these discussions, I try to draw our attention to multiple axes of tension in the production of anthropological knowledge.     

Fixed or variable demand? Does it matter when locating a facility?

In most competitive location models available in the literature, it is assumed that the demand is fixed independently of market conditions. However, demand may vary depending on prices, distances to the facilities, etc., especially when the goods are not essential. Taking variable demand into consideration increases the complexity of the problem and, therefore, the computational effort needed to solve it, but it may make the model more realistic. In this paper, a new planar competitive location and design problem with variable demand is presented. By using it, it is shown numerically for the first time in the literature that the assumption of fixed demand influences the location decision very much, and therefore the selection of the type of demand (fixed or variable) must be made with care when modeling location problems. Finally, two methods are presented to cope with the new model, an exact interval branch-and-bound method and an evolutionary algorithm called UEGO (Universal Evolutionary Global Optimizer).     

A comparison of robust estimators based on two types of trimming

The least trimmed squares (LTS) estimator and the trimmed mean (TM) are two well-known trimming-based estimators of the location parameter. Both estimates are used in practice, and they are implemented in standard statistical software (e.g., S-PLUS, R, Matlab, SAS). The breakdown point of each of these estimators increases as the trimming proportion increases, while the efficiency decreases. Here we have shown that for a wide range of distributions with exponential and polynomial tails, TM is asymptotically more efficient than LTS as an estimator of the location parameter, when they have equal breakdown points.     

An efficient nonparametric test for bivariate two-sample location problem

We propose a new test for the two-sample bivariate location problem. The proposed test statistic has a U-statistic representation with a degenerate kernel. The limiting distribution is found for the proposed test statistic. The power of the test is compared using Monte Carlo simulation to the tests of Blumen [I. Blumen, A new bivariate sign-test for location, Journal of the American Statistical Association 53 (1958) 448–456], Mardia [K.V. Mardia, A non-parametric test for the bivariate two-sample location problem, Journal of the Royal Statistical Society, Series B 29 (1967) 320–342], Peters and Randles [D. Peters, R.H. Randles, A bivariate signed-rank test for the two-sample location problem, Journal of the Royal Statistical Society, Series B 53 (1991) 493–504], LaRocque, Tardif and van Eeden [D. LaRocque, S. Tardif, C. van Eeden, An affine-invariant generalization of the Wilcoxon signed-rank test for the bivariate location problem, Australian and New Zealand Journal of Statistics 45 (2003) 153–165], and Baringhaus and Franz [L. Baringhaus, C. Franz, On a new multivariate two-sample test, Journal of Multivariate Analysis 88 (2004) 190–206]. Under the bivariate normal and bivariate t distributions the proposed test was more powerful than the competitors for almost every change in location. Under the other distributions the proposed test reached the desired power of one at a faster rate than the other tests in the simulation study. Application of the test is presented using bivariate data from a synthetic and a real-life data set.     

Combining decision tree and MAUT for selecting a country for a global manufacturing facility

The paper presents a multi-phase approach for selecting a country in which to locate a global manufacturing facility. An influence diagram is used to frame the decision. A decision tree then analyzes uncertainties regarding cost and generates a risk profile. The risk profile becomes one of the measures in an MAUT model that incorporate a wide range of factors. This sequential approach of using the output from a decision tree as input to MAUT is demonstrated with an example involving an auto supplier locating a new plant in one of five countries. Three decision makers were interviewed to determine the weights and the shape of the individual utility curves. The paper identifies, clearly defines, and incorporates a variety of measures for which national data are readily available. This list is broader and less subjective when compared to other examples reported in the literature.     

A Note on the Hodges–Lehmann Estimator and the Logistic Distribution

ABSTRACT

It is well known that the Hodges–Lehmann estimator is asymptotically efficient for the location parameter of the logistic distribution. In this article we give a simple and direct proof that this property also characterizes the logistic between all the symmetric location distributions under mild conditions. Using pseudolikelihood, we also show how to find from the Hodges–Lehmann estimator an asymptotically efficient estimator of the scale parameter of the logistic distribution.     

Sustainable supply chain network design: An optimization-oriented review

Supply chain network design (SCND) models and methods have been the subject of several recent literature review surveys, but none of them explicitly includes sustainable development as a main characteristic of the problem considered. The aim of this review is to bridge this gap. The paper analyzes 87 papers in the field of supply chain network design, covering mathematical models that include economic factors as well as environmental and/or social dimensions. The review is organized along four research questions asking (i) which environmental and social objectives are included, (ii) how are they integrated into the models, (iii) which methods and tool are used and finally (iv) which industrial applications and contexts are covered in these models. The review finds that there are a number of limitations to the current research in sustainable SCND. The narrow scope of environmental and social measures in current models should go beyond limited greenhouse gas indicators to broader life-cycle approaches including new social metrics. The more effective inclusion of uncertainty and risk in models with improved multi-objective approaches is also needed. There are also significant gaps in the sectors used to test models limiting more general applicability. The paper concludes with promising new avenues of research to more effectively include sustainability into SCND models.     

Sequential estimation of a linear function of location parameters of two negative exponential distributions

The paper considers the problem of bounded risk point estimation for a linear function of location parameters of two negative exponential distributions, including the difference in a special case, when two scale parameters are unknown. Purely sequential procedures are proposed and second order expansions of the average sample sizes and risk are given. Furthermore some simulation results are provided.     

SMALL SAMPLE INFERENCE IN LOCATION SCALE MODELS WITH STUDENT(λ) ERRORS

ABSTRACT

A third order accurate approximation to the p value in testing either the location or scale parameter in a location scale model with Student(λ) errors is introduced. The third order approximation is developed via an asymptotic method, based on exponential models and the saddlepoint approximation. Techniques are presented for the numerical computation of all quantities required for the third order approximation. To compare the accuracy of various asymptotic methods a numerical example and simulation study are included. The numerical example and simulation study illustrate that the third order method presented leads to a more accurate p value approximation compared to first order methods in Student(λ) models with small samples.     

Location and Scale Parameter Family of Distributions Attaining the Bhattacharyya Bound

It is well known that, under appropriate regularity conditions, the variance of an unbiased estimator of a real-valued function of an unknown parameter can coincide with the Cramér–Rao lower bound only if the family of distributions is a one-parameter exponential family. But it seems that the necessary conditions about the probability distribution for which there exists an unbiased estimator whose variance coincides with the Bhattacharyya lower bound are not completely known. The purpose of this paper is to specify the location, scale, and location-scale parameter family of distributions attaining the general order Bhattacharyya bound in certain class.