Communities and academics: A developing dialogue

Intentional communities have been described as laboratories for testing and demonstrating new ideologies and social structures. This paper focuses on the part played by academics in disseminating the results of these ‘social experiments’ to a wider audience. The relationship between intentional communities and academics has often been difficult, with mistrust on both sides. By looking at the underlying needs of both, this paper intends to throw light on the source of some of the difficulties, so that both may better fulfil their important social functions. A case study is reported to illustrate the main points. The paper concludes with advice to both intentional communities and academics for building better relationships and developing co-operative projects.     

Adaptation over parametric families of symmetric linear estimators

This paper treats an abstract parametric family of symmetric linear estimators for the mean vector of a standard linear model. The estimator in this family that has smallest estimated quadratic risk is shown to attain, asymptotically, the smallest risk achievable over all candidate estimators in the family. The asymptotic analysis is carried out under a strong Gauss–Markov form of the linear model in which the dimension of the regression space tends to infinity. Leading examples to which the results apply include: (a) penalized least squares fits constrained by multiple, weighted, quadratic penalties; and (b) running, symmetrically weighted, means. In both instances, the weights define a parameter vector whose natural domain is a continuum.     

Arbeit und Leben in Organisationen 2008

Mitteilungen

Arbeit und Leben in Organisationen 2008 Anlage und Ergebnisse der Studie     

Weighted Median of the Data in Solving Least Absolute Deviations Problems

We consider the weighted median problem for a given set of data and analyze its main properties. As an illustration, an efficient method for searching for a weighted Least Absolute Deviations (LAD)-line is given, which is used as the basis for solving various linear and nonlinear LAD-problems occurring in applications. Our method is illustrated by an example of hourly natural gas consumption forecast.     

Least squares and shrinkage estimation under bimonotonicity constraints

In this paper we describe active set type algorithms for minimization of a smooth function under general order constraints, an important case being functions on the set of bimonotone r×s matrices. These algorithms can be used, for instance, to estimate a bimonotone regression function via least squares or (a smooth approximation of) least absolute deviations. Another application is shrinkage estimation in image denoising or, more generally, regression problems with two ordinal factors after representing the data in a suitable basis which is indexed by pairs (i,j)∈{1,…,r}×{1,…,s}. Various numerical examples illustrate our methods.