SimSel: a new simulation method for variable selection

We propose a new simulation method, SimSel, for variable selection in linear and nonlinear modelling problems. SimSel works by disturbing the input data with pseudo-errors. We then study how this disturbance affects the quality of an approximative model fitted to the data. The main idea is that disturbing unimportant variables does not affect the quality of the model fit. The use of an approximative model has the advantage that the true underlying function does not need to be known and that the method becomes insensitive to model misspecifications. We demonstrate SimSel on simulated data from linear and nonlinear models and on two real data sets. The simulation studies suggest that SimSel works well in complicated situations, such as nonlinear errors-in-variable models.     

LASSO order selection for sparse autoregression: a bootstrap approach

Autoregressive models are widely employed for predictions and other inferences in many scientific fields. While the determination of their order is in general a difficult and critical step, this task becomes more complicated and crucial when the time series under investigation is realization of a stochastic process characterized by sparsity. In this paper we present a method for order determination of a stationary AR model with a sparse structure, given a set of observations, based upon a bootstrapped version of MAICE procedure [Akaike H. Prediction and entropy. Springer; 1998], in conjunction with a LASSO-type constraining procedure for lag suppression of insignificant lags. Empirical results will be obtained via Monte Carlo simulations. The quality of our method is assessed by comparison with the commonly adopted cross-validation approach and the non bootstrap counterpart of the presented procedure.     

Finding Known Journal Articles: Comparing a Citation Linker to an A-to-Z List

OpenURL links provide access to full-text articles from citation databases; however, end users who have found citations outside of library databases must find the full text of their journal articles another way. Many OpenURL link resolvers offer a citation finder service which allows the end user to search for specific known items at the article level. This paper studies the usability of the “citation linker” search from Ex Libris' SFX®. Twenty-one volunteers tested finding known journal articles using both a standard A-to-Z list and SFX' citation linker search. The researchers compare results from the two search interfaces, document the problems found, recommend which interface makes the best default search interface, and suggest improvements to the citation linker interface.     

SNP selection for predicting a quantitative trait

Molecular markers combined with powerful statistical tools have made it possible to detect and analyze multiple loci on the genome that are responsible for the phenotypic variation in quantitative traits. The objectives of the study presented in this paper are to identify a subset of single nucleotide polymorphism (SNP) markers that are associated with a particular trait and to construct a model that can best predict the value of the trait given the genotypic information of the SNPs using a three-step strategy. In the first step, a genome-wide association test is performed to screen SNPs that are associated with the quantitative trait of interest. SNPs with p-values of less than 5% are then analyzed in the second step. In the second step, a large number of randomly selected models, each consisting of a fixed number of randomly selected SNPs, are analyzed using the least angle regression method. This step will further remove redundant SNPs due to the complicated association among SNPs. A subset of SNPs that are shown to have a significant effect on the response trait more often than by chance are considered for the third step. In the third step, two alternative methods are considered: the least angle shrinkage and selection operation and sparse partial least squares regression. For both methods, the predictive ability of the fitted model is evaluated by an independent test set. The performance of the proposed method is illustrated by the analysis of a real data set on Canadian Holstein cattle.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

The two ANSI standards for serial holdings statements: A comparison

The ANSI Standard on Serial Holdings Statements at the Summary Level is being withdrawn and a new holdings standard developed by NISO, entitled Serial Holdings Statements, is being approved in its place. This article compares the two standards, and describes the changes users will need to make when adopting the new standard.     

An alternative approach to variable selection for prediction

A procedure for selecting a subset of predictor variables in regression analysis is suggested. The procedure is so designed that it leads to the selection of a subset of variables having an adequate degree of informativeness with a directly specified confidence coefficient. Some examples are considered to illustrate the application of the procedure.     

Using a Standard License for Individual Electronic Journal Purchases

Abstract

This article describes a pilot project in the Massachusetts Institute of Technology Libraries in which a standard library license (based directly on the CLIR/NERL/NELINET license) was sent to publishers of individual electronic journals as the initial step in arranging for e-journal access, rather than working first with the publisher's own license. Serials Review 2003; 29:302–304.     

Journal Finder, a Second Look: Implications for Serials Access in Today''s Library

Since The University of North Carolina at Greensboro's Jackson Library introduced Journal Finder in August 2001, the service has grown and evolved. Its features and capabilities have been expanded, enhanced, and refined, and a partnership program has been developed. Journal Finder enjoys significant initial success and appears to meet a real need in the research life of faculty and students. Additionally, Journal Finder has revealed several user trends with implications for the library, including the use of print serials, photocopying, interlibrary loan, and document delivery/pay-per-view, and it continues to provide insights and data for serials collection management decisions. Serials Review 2003; 29:287–294.     

Bandwidth selection for kernel density estimation: a review of fully automatic selectors

On the one hand, kernel density estimation has become a common tool for empirical studies in any research area. This goes hand in hand with the fact that this kind of estimator is now provided by many software packages. On the other hand, since about three decades the discussion on bandwidth selection has been going on. Although a good part of the discussion is about nonparametric regression, this parameter choice is by no means less problematic for density estimation. This becomes obvious when reading empirical studies in which practitioners have made use of kernel densities. New contributions typically provide simulations only to show that the own selector outperforms some of the existing methods. We review existing methods and compare them on a set of designs that exhibit few bumps and exponentially falling tails. We concentrate on small and moderate sample sizes because for large ones the differences between consistent methods are often negligible, at least for practitioners. As a byproduct we find that a mixture of simple plug-in and cross-validation methods produces bandwidths with a quite stable performance.