Quasi-variances in Xlisp-Stat and on the web

The most common summary of a fitted statistical model, a list of parameter estimates and standard errors, does not give the precision of estimated combinations of the parameters, such as differences or ratios. For this, covariances also are needed; but space constraints typically mean that the full covariance matrix cannot routinely be reported. In the important case of parameters associated with the discrete levels of an experimental factor or with a categorical classifying variable, the identifiable parameter combinations are linear contrasts. The QV Calculator computes "quasi-variances" which may be used as an alternative summary of the precision of the estimated parameters. The summary based on quasi-variances is simple and permits good approximation of the standard error of any desired contrast. The idea of such a summary has been suggested by Ridout (1989) and, under the name "floating absolute risk", by Easton, Peto & Babiker (1991). It applies to a wide variety of statistical models, including linear and nonlinear regressions, generalized-linear and GEE models, Cox proportional-hazard models for survival data, generalized additive models, etc. The QV Calculator is written in Xlisp-Stat (Tierney,'90) and can be used either directly by users who have access to Xlisp-Stat or through a web interface by those who do not. The user either supplies the covariance matrix for the effect parameters of interest, or, if using Xlisp-Stat directly, can generate that matrix by interaction with a model object.

CGIwithR: Facilities for processing web forms using R

CGIwithR is a package for use with the R statistical computing environment, to facilitate processing of information from web-based forms, and reporting of results in the Hypertext Markup Language (HTML), through the Common Gateway Interface (CGI). CGIwithR permits the straightforward use of R as a CGI scripting language. This paper serves as an extended user manual for CGIwithR, supplementary to the R help pages installed with the package.

Bradley-Terry models in R : the BradleyTerry2 package

This is a short overview of the R add-on package BradleyTerry2, which facilitates the specification and fitting of Bradley-Terry logit, probit or cauchit models to pair-comparison data. Included are the standard 'unstructured' Bradley-Terry model, structured versions in which the parameters are related through a linear predictor to explanatory variables, and the possibility of an order or 'home advantage' effect or other 'contest-specific' effects. Model fitting is either by maximum likelihood, by penalized quasi-likelihood (for models which involve a random effect), or by bias-reduced maximum likelihood in which the first-order asymptotic bias of parameter estimates is eliminated. Also provided are a simple and efficient approach to handling missing covariate data, and suitably-defined residuals for diagnostic checking of the linear predictor

A generic algorithm for reducing bias in parametric estimation

A general iterative algorithm is developed for the computation of reduced-bias parameter estimates in regular statistical models through adjustments to the score function. The algorithm unifies and provides appealing new interpretation for iterative methods that have been published previously for some specific model classes. The new algorithm can usefully be viewed as a series of iterative bias corrections, thus facilitating the adjusted score approach to bias reduction in any model for which the first- order bias of the maximum likelihood estimator has already been derived. The method is tested by application to a logit-linear multiple regression model with beta-distributed responses; the results confirm the effectiveness of the new algorithm, and also reveal some important errors in the existing literature on beta regression

Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models

Penalization of the likelihood by Jeffreys' invariant prior, or by a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial generalized linear models. The class of models includes logistic regression, where the Jeffreys-prior penalty is known additionally to reduce the asymptotic bias of the maximum likelihood estimator; and also models with other commonly used link functions such as probit and log-log. Shrinkage towards equiprobability across observations, relative to the maximum likelihood estimator, is established theoretically and is studied through illustrative examples. Some implications of finiteness and shrinkage for inference are discussed, particularly when inference is based on Wald-type procedures. A widely applicable procedure is developed for computation of maximum penalized likelihood estimates, by using repeated maximum likelihood fits with iteratively adjusted binomial responses and totals. These theoretical results and methods underpin the increasingly widespread use of reduced-bias and similarly penalized binomial regression models in many applied fields

Statistical modelling of citation exchange among statistics journals

Scholarly journal rankings based on citation data are often met with skepticism by the scientific community. Part of the skepticism is due to the discrepancy between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of authors. This paper focuses on analysis of the table of cross-citations among a selection of Statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care in order to avoid potential over-interpretation of insignificant differences between journal ratings

Is Free Information Really Free? Information Supply into an IT-Based Organizational Memory System

Information sharing is a critical issue facing businesses today. In the United States some 90 percent of large private sector and 40 percent of public sector enterprises are reported to have at least one initiative in place to assist in the sharing of information. In contrast, the realities of not sharing information are great with estimates of up to $12 billion wasted each year as employees duplicate one another’s work. Information sharing is often facilitated by an IT-based organizational memory system, and this paper examines one such system at a large U.S.-based IT consulting firm. Our study examines what impacts information supply into the system. Using a wide-scale survey deployed to over 1,200 professionals with over a 30 percent response rate, we use structural equation modeling to show that information supply by an individual is a result of weighing up the personal costs and benefits of such supply. While the costs of information supply have been covered in depth in the literature, the benefits side of the equation has received little attention. This paper addresses that gap, and shows that the ability to influence is a critical component of the benefits the information supplier expects to receive to offset the costs of supplying information. We conclude by noting how this research may impact managers, suppliers and users of information sharing systems, and present ideas for future research

Quasi-variances in Xlisp-Stat and on the web

The most common summary of a fitted statistical model, a list of parameter estimates and standard errors, does not give the precision of estimated combinations of the parameters, such as differences or ratios. For this, covariances also are needed; but space constraints typically mean that the full covariance matrix cannot routinely be reported. In the important case of parameters associated with the discrete levels of an experimental factor or with a categorical classifying variable, the identifiable parameter combinations are linear contrasts. The QV Calculator computes "quasi-variances" which may be used as an alternative summary of the precision of the estimated parameters. The summary based on quasi-variances is simple and permits good approximation of the standard error of any desired contrast. The idea of such a summary has been suggested by Ridout (1989) and, under the name "floating absolute risk", by Easton, Peto & Babiker (1991). It applies to a wide variety of statistical models, including linear and nonlinear regressions, generalized-linear and GEE models, Cox proportional-hazard models for survival data, generalized additive models, etc. The QV Calculator is written in Xlisp-Stat (Tierney, 1990) and can be used either directly by users who have access to Xlisp-Stat or through a web interface by those who do not. The user either supplies the covariance matrix for the effect parameters of interest, or, if using Xlisp-Stat directly, can generate that matrix by interaction with a model object