A monte carlo study of robust estimators of location

Andrews et al (1972) carried out an extensive Monte Carlo study of robust estimators of location. Their conclusions were that the hampel and the skipped estimates, as classes, seemed to be preferable to some of the other currently fashionable estimators. The present study extends this work to include estimators not previously examined. The estimators are compared over short-tailed as well as long-tailed alternatives and also over some dependent data generated by first-order autoregressive schemes. The conclusions of the present study are threefold. First, from our limited study, none of the so-called robust estimators are very efficient over short-tailed situations. More work seems to be necessary in this situation. Second, none of the estimators perform very well in dependent data situations, particularly when the correlation is large and positive. This seems to be a rather pressing problem. Finally, for long-tailed alternatives, the hampel estimators and Hogg-type adaptive versions of the hampels are the strongest classes. The adaptive hampels neither uniformly outperform nor are they outperformed by the hampels. However, the superiority in terms of maximum relative efficiency goes to the adaptive hampels. That is, the adaptive hampels, under their worst performance.     

Low order approximations in deconvolution and regression with errors in variables

Summary.  We suggest two new methods, which are applicable to both deconvolution and regression with errors in explanatory variables, for nonparametric inference. The two approaches involve kernel or orthogonal series methods. They are based on defining a low order approximation to the problem at hand, and proceed by constructing relatively accurate estimators of that quantity rather than attempting to estimate the true target functions consistently. Of course, both techniques could be employed to construct consistent estimators, but in many contexts of importance (e.g. those where the errors are Gaussian) consistency is, from a practical viewpoint, an unattainable goal. We rephrase the problem in a form where an explicit, interpretable, low order approximation is available. The information that we require about the error distribution (the error-in-variables distribution, in the case of regression) is only in the form of low order moments and so is readily obtainable by a rudimentary analysis of indirect measurements of errors, e.g. through repeated measurements. In particular, we do not need to estimate a function, such as a characteristic function, which expresses detailed properties of the error distribution. This feature of our methods, coupled with the fact that all our estimators are explicitly defined in terms of readily computable averages, means that the methods are particularly economical in computing time.     

INDUSTRIAL STRUCTURE AND MONOPOLY POWER IN THE FEDERAL BUREAUCRACY: AN EMPIRICAL ANALYSIS

Predictions of various models of public sector supply rely on the assumption of bureau monopoly power. This assumption is tested first by measuring industrial structure of the federal public sector using the concentration ratio and Herfindahl index, and second by examining the relationship between the resulting estimates and bureau monopoly power. The findings indicate that monopoly structure of the federal bureaucracy is more limited than is generally assumed. Interpretations on the basis of traditional industrial organization theory and contestable market theory suggest that bureau monopoly power is questionable and may not be the source of observed inefficiencies in bureau supply.     

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.     

Children's Engagement as Urban Researchers and Consultants in Aotearoa/New Zealand: Can it Increase Children's Effective Participation in Urban Planning?

Children conducting research and engaging in ‘adult’ domains such as urban planning challenge perceptions of children as merely ‘adult becomings’ — perceptions which, along with lack of pathways for participation, have largely excluded children from effective participation in the public realm. This paper considers two urban participatory projects in Aotearoa/New Zealand: in the first, children (10–13 years) researched peer perceptions of Auckland city life; in the second, they were consulted by Auckland Council on the redevelopment of a city square. We reflect on processes and outcomes, and the extent to which facilitating such projects may increase children's meaningful participation in urban planning.     

Two Stochastic Multidimensional Choice Models for Marketing Research

Two recently developed probabilistic multidimensional models for analyzing pairwise choice data are introduced, discussed in terms of their differential properties, and extended in several ways. The first one, the wandering vector model, was originally suggested by Carroll [12] and extended by De Soete and Carroll [30]. The second model, called the wandering ideal point model, is a more recently proposed [32] unfolding analog of the wandering vector model. A general maximum likelihood estimation method for fitting the various models described is mentioned, as well as a statistical test for assessing the goodness of fit. Finally, an application of the models is provided concerning consumer choice for some 14 brands of over-the-counter analgesics to illustrate how such models can be gainfully utilized for marketing decision making concerning product positioning.