Forecast Combination and Model Averaging Using Predictive Measures

We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting when uninformative priors on the model parameters are used and improves forecast performance. For the predictive likelihood we argue that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and in an application to forecasts of the Swedish inflation rate, where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.     

From Alphabetized to Classified: Reorganizing a Large Periodicals Collection

The bound periodicals at Founders Memorial Library at Northern Illinois University have always been shelved by main entry. This arrangement, however, has confused patrons and has created problems with coordinating the records in the Cataloging and Acquisitions Departments. Staff in those two departments had long lobbied to shelve the bound periodicals by call numbers. In September 2008, library staff were granted permission to begin the process of rearranging them. Over the course of eight months, library staff labeled approximately 159,000 volumes of bound periodicals. This article describes how it was done with a digital camera and a Photobucket account (www.photobucket.com).     

A Systematic Review of Interventions for Elder Abuse

The purpose of this study was to use rigorous systematic review methods to summarize the effectiveness of interventions for elder abuse. Only eight studies met our inclusion criteria. Evidence regarding the recurrence of abuse following intervention was limited, but the interventions for which this outcome was reported failed to reduce, and may have even increased, the likelihood of recurrence. Elder abuse interventions had no significant effect on case resolution and at-risk caregiver outcomes, and had mixed results regarding professional knowledge and behavior related to elder abuse. The included studies had important methodological limitations that limit our ability to draw conclusions about the effectiveness of these interventions.     

High dimensional extension of the growth curve model and its application in genetics

Recent advances in technology have allowed researchers to collect large scale complex biological data, simultaneously, often in matrix format. In genomic studies, for instance, measurements from tens to hundreds of thousands of genes are taken from individuals across several experimental groups. In time course microarray experiments, gene expression is measured at several time points for each individual across the whole genome resulting in a high-dimensional matrix for each gene. In such experiments, researchers are faced with high-dimensional longitudinal data. Unfortunately, traditional methods for longitudinal data are not appropriate for high-dimensional situations. In this paper, we use the growth curve model and introduce test useful for high-dimensional longitudinal data and evaluate its performance using simulations. We also show how our approach can be used to filter genes in time course genomic experiments. We illustrate this using publicly available genomic data, involving experiments comparing normal human lung tissue with vanadium pentoxide treated human lung tissue, designed with the aim of understanding the susceptibility of individuals working in petro-chemical factories to airway re-modelling. Using our method, we were able to filter out 1053 (about 5 %) genes as non-noise genes from a pool of  22,277. Although our focus is on hypothesis testing, we also provided modified maximum likelihood estimator for the mean parameter of the growth curve model and assessed its performance through bias and mean squared error.     

Assessing potential hybridization between a hypothetical gene drive-modified Drosophila suzukii and nontarget Drosophila species

Genetically engineered gene drives (geGD) are potentially powerful tools for suppressing or even eradicating populations of pest insects. Before living geGD insects can be released into the environment, they must pass an environmental risk assessment to ensure that their release will not cause unacceptable harm to non-targeted entities of the environment. A key research question concerns the likelihood that nontarget species will acquire the functional GD elements; such acquisition could lead to reduced abundance or loss of those species and to a disruption of the ecosystem services they provide. The main route for gene flow is through hybridization between the geGD insect strain and closely related species that co-occur in the area of release and its expected dispersal. Using the invasive spotted-wing drosophila, Drosophila suzukii, as a case study, we provide a generally applicable strategy on how a combination of interspecific hybridization experiments, behavioral observations, and molecular genetic analyses can be used to assess the potential for hybridization.     

Forthcoming in Sociological Forum

Table of Contents

Forthcoming in Sociological Forum     

Rate of consistency of one sample tests of location

Let X₁,…,X_n be a sample from a population with continuous distribution function F(x?θ) such that F(x)+F(-x)=1 and 0<F(x)<1, x?R¹. It is shown that the power- function of a monotone test of H: θ=θ₀ against K: θ>θ₀ cannot tend to 1 as θ?θ₀ → ∞ more than n times faster than the tails of F tend to 0. Some standard as well as robust tests are considered with respect to this rate of convergence.     

Second order asymptotic relations of M-estimators and R-estimators in two-sample location model

Let T_M be an M-estimator (maximum likelihood type estimator) and T_R be an R-estimator (Hodges-Lehmann's estimator) of the shift parameter Δ in the two-sample location model. The asymptotic representation of √N(T_M-T_R) up to a term of the order O_p(N^{-$\text{1}\text{4}$}) is derived which is valid if the functions Ψ and ? generating T_M and T_R, respectively, decompose into an absolutely continuous and a step-function components; the order O_p(N^{-$\text{1}\text{4}$}) cannot be improved unless the discontinuous components vanish. As a consequence, the conditions under which √N(T_M-T_R)=O_p(N^{-$\text{1}\text{4}$}) are obtained. The main tool for obtaining the results is the second order asymptotic linearity of the pertaining linear rank statistics which is proved here under the assumption that the score-generating function ? has some jump-discontinuities.