Exact Likelihood Ratio Testing for Homogeneity of the Exponential Distribution

The aim of this article is to discuss homogeneity testing of the exponential distribution. We introduce the exact likelihood ratio test of homogeneity in the subpopulation model, ELR, and the exact likelihood ratio test of homogeneity against the two-components subpopulation alternative, ELR2. The ELR test is asymptotically optimal in the Bahadur sense when the alternative consists of sampling from a fixed number of components. Thus, in some setups the ELR is superior to frequently used tests for exponential homogeneity which are based on the EM algorithm (like the MLRT, ADDS, and D-tests). One important example of superiority of ELR and ELR2 tests is the case of lower contamination. We demonstrate this fact by both theoretical comparisons and simulations.     

Connecting Knowledge and Policy: The Promise of Community Indicators in the United States

There are over 200 identified community based indicator projects in the United States. This paper reviews the factors that contribute to the success and failure of community indicator projects and discusses the types of desired outcomes that communities are trying to achieve. Both organizational and political factors contributing to the success of community indicators projects are discussed. Appendices include information on many of the community indicator projects in the United States. Advice for communities seeking to launch an indicator project are outlined.     

On the inadequacy of the regression paradigm used in the study of human judgment

Although traditional instruments of research into human judgment - correlational statistics, the lens model, the ANOVA approach, etc. - are analytical, logical, and explicit tools of study, they might be inadequate, irrational and incorrect in their ultimate impact. In this short note a few examples are introduced to show that linear (in parameters) regression models could represent only a quasi-rational paradigm at best. Simple graphical diagrams are used to clarify three main difficulties with the linear model.