A new class of matrix variate elliptically contoured distributions

Summary A new class of matrix variate elliptically contoured distributions is defined. Properties of this class of distributions are studied. Examples of distributions which belong to this class are also presented. Rose-Hulman Institute of Technology Research supported by the FRC Major Grant, Bowling Grant, Bowling Green State University.     

Tabulating weighted sup-norm functionals used in change-point analysis

We introduce a number of weighted partial sum processes of which certain sup-norm functionals may be used, for example, to detect changes in the mean of independent observations. Their limiting distributions are derived mathematically, simulated and then tabulated. With this information, a detailed numerical investigation of the power of these functionals is carried out.* ^*Research mainly done while at Carleton University, partially supported by an NSERC Canada Grant of Miklós Csörg? at Carleton University, Ottawa. Also supported by the Austrian Science Foundation (FWF) under grant SFB#010 (‘Adaptive Information Systems and Modelling in Economics and Management Science’) while at Technische Universität Wien. ? ^?Research partially supported by an NSERC Canada Grant of Miklós Csörg? at Carleton University, Ottawa.      

On calculating the fisher information in order statistics

This article gives a simple result for the expression of the Fisher information in order statistics. This result enables us to calculate easily the Fisher information in any set of order statistics whose details have been known to be messy and complicated. We consider here its application in the optimal spacing problem where the exact Fisher information in order statistics has been approximated with the asymptotic information or the reciprocal of the variance of a suitable estimator. This work was supported by Korea Research Foundation Grant(KRF-2000-015-DP0056)     

Noninformative priors for the normal variance ratio

In this paper, we consider noninformative priors for the ratio of variances in two normal populations. We develop first and second order matching priors. We find that the second order matching prior matches alternative coverage probabilities up to the second order and is also a HPD matching prior. It turns out that among the reference priors, only one-at-a-time reference prior satisfies a second order matching criterion. Our simulation study indicates that the one-at-a-time reference prior performs better than other reference priors in terms of matching the target coverage probabilities in a frequentist sense. This work is supported by Korea Research Foundation Grant (KRF-2004-002-C00041).     

Dodge-roming AOQL plans for inspection by variables from numerical point of view

In this paper we shall deal with the AOQL single sampling plans for inspection by variables when the remainder of rejected lots is inspected. We shall report on an algorithm allowing calculation¹ of these plans. For the calculation we shall derive a new theorem (see Theorem 4) and we shall use an original method. Prepared with support from the Grant Agency of the Czech Republic. The calculation is considerably difficult, we must use sequently three numerical methods.     

The application of robust Bayesian analysis to hypothesis testing and Occam's Razor

Summary Robust Bayesian analysis deals simultaneously with a class of possible prior distributions, instead of a single distribution. This paper concentrates on the surprising results that can be obtained when applying the theory to problems of testing precise hypotheses when the “objective” class of prior distributions is assumed. First, an example is given demonstrating the serious inadequacy of P-values for this problem. Next, it is shown how the approach can provide statistical quantification of Occam's Razor, the famous principle of science that advocates choice of the simpler of two hypothetical explanations of data. Finally, the theory is applied to multinomial testing. Research supported by the National Science Foundation, Grant DMS-8923071, and by NASA Contract NAS5-29285 for the hubble Space Telescope.     

Bayesian decision criteria in the presence of noises under quadratic and absolute value loss functions

Summary Misclassifications, or noises, in the sampling stage of a Bayesian scheme can seriously affect the values of decision criteria such as the Bayes Risk and the Expected Value of Sample Information. This problem does not seem to be much addressed in the existing literature. In this article, using an approach based on hypergeometric functions and numerical computation, we study the effects of these noises under the two most important loss functions: the quadratic and the absolute value. A numerical example illustrates these effects in a representative case, using both loss functions, and provides additional insights into the general problem. Research partially supported by NSERC grant A 9249 (Canada) and FICU Grant 2000/pas/13. The authors wish to thank colleagues at the University of Alberta in Edmonton, Canada, for very stimulating discussions, and an anonymous referee for drawing their attention to three relevant references that have enriched the content of this final version.     

Robust estimation of multivariate regression model

This paper studies robust estimation of multivariate regression model using kernel weighted local linear regression. A robust estimation procedure is proposed for estimating the regression function and its partial derivatives. The proposed estimators are jointly asymptotically normal and attain nonparametric optimal convergence rate. One-step approximations to the robust estimators are introduced to reduce computational burden. The one-step local M-estimators are shown to achieve the same efficiency as the fully iterative local M-estimators as long as the initial estimators are good enough. The proposed estimators inherit the excellent edge-effect behavior of the local polynomial methods in the univariate case and at the same time overcome the disadvantages of the local least-squares based smoothers. Simulations are conducted to demonstrate the performance of the proposed estimators. Real data sets are analyzed to illustrate the practical utility of the proposed methodology. This work was supported by the National Natural Science Foundation of China (Grant No. 10471006).     

Estimations of a threshold parameter in cox regression

Therneau et al (1990) used martingale residual plots to study the threshold effect of some covariates in a proportional hazard regression model for survival data subject to right censoring. We show that the maximum partial likelihood estimate provides an asymptotically consistent estimator for the unknown threshold. This procedure is illustrated by applying it to a data set from a cohort of patients with B-lineage leukemia treated at St. Jude Children's Research Hospital.     

On random variate generation for the generalized hyperbolic secant distributions

We give random variate generators for the generalized hyperbolic secant distribution and related families such as Morris's skewed generalized hyperbolic secant family and a family introduced by Laha and Lukacs. The rejection method generators are uniformly fast over the parameter space and are based upon a complex function representation of the distributions due to Harkness and HarknessThe author's research was sponsored by NSERC Grant A3456 and FCAR Grant 90-ER-0291.     

A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials

Summary Meta-analyses of sets of clinical trials often combine risk differences from several 2×2 tables according to a random-effects model. The DerSimonian-Laird random-effects procedure, widely used for estimating the populaton mean risk difference, weights the risk difference from each primary study inversely proportional to an estimate of its variance (the sum of the between-study variance and the conditional within-study variance). Because those weights are not independent of the risk differences, however, the procedure sometimes exhibits bias and unnatural behavior. The present paper proposes a modified weighting scheme that uses the unconditional within-study variance to avoid this source of bias. The modified procedure has variance closer to that available from weighting by ideal weights when such weights are known. We studied the modified procedure in extensive simulation experiments using situations whose parameters resemble those of actual studies in medical research. For comparison we also included two unbiased procedures, the unweighted mean and a sample-size-weighted mean; their relative variability depends on the extent of heterogeneity among the primary studies. An example illustrates the application of the procedures to actual data and the differences among the results. This research was supported by Grant HS 05936 from the Agency for Health Care Policy and Research to Harvard University.     

D-optimal designs for combined polynomial and trigonometric regression on a partial circle

Consider the D-optimal designs for a combined polynomial and trigonometric regression on a partial circle. It is shown that the optimal design is equally supported and the structure of the optimal design depends only on the length of the design interval and the support points are analytic functions of this parameter. Moreover, the Taylor expansion of the optimal support points can be determined efficiently by a recursive procedure. Examples are presented to illustrate the procedures for computing the optimal designs.     

Serials for Distance Education Research Projects

Abstract

Public service librarians, eager to comply with Association of College and Research Libraries guidelines, face challenges in the rush to support distance education programs in higher education. Distance education students doing serials research encounter significant problems. In this column the author identifies and discusses issues related to the instruction, reference, and access needs of such students. Serials Review 2002; 28:206–212.     

Statistical Inference for Transformation Inhomogeneous Point Processes

Abstract.  Statistical inference for exponential inhomogeneous Markov point processes by transformation is discussed. It is argued that the inhomogeneity parameter can be estimated, using a partial likelihood based on an inhomogeneous Poisson point process. The inhomogeneity parameter can thereby be estimated without taking the interaction into account, which simplifies the statistical analysis considerably. Data analysis and simulation experiments support the results.     

Implementing New Bibliometric Services at EPA-RTP Library

Abstract

The EPA-RTP Library is a federal research library established on the campus of the United States Environmental Protection Agency in Research Triangle Park, North Carolina. Historically, one of the ways the library has supported the information needs of researchers on campus is through the provision of basic bibliometric-related reference support. As these requests evolved in complexity due to the nature of organizational and scientific research impact demands, library staff recognized an opportunity to expand the library’s offerings to form two distinct services with product deliverables. The resulting services analyze and package metrics at the author, article, and journal levels with graphical data visualizations to provide researchers with a portfolio-style product they can use to demonstrate their individual scholarly output and research value. Development of Research Impact Reports and Article Impact Reports is explained in detail along with reflections on implementation processes and prospects for future expansion in bibliometric analysis services.     

Robust regression using iteratively reweighted least-squares

The rapid development of the theory of robust estimation (Huber, 1973) has created a need for computational procedures to produce robust estimates. We will review a number of different computational approaches for robust linear regression but focus on one—iteratively reweighted least-squares (IRLS). The weight functions that we discuss are a part of a semi-portable subroutine library called ROSEPACK (RObust Statistical Estimation PACKage) that has been developed by the authors and Virginia Klema at the Computer Research Center of the National Bureau of Economic Research, Inc. in Cambridge, Mass. with the support of the National Science Foundation. This library (Klema, 1976) makes it relatively simple to implement an IRLS regression package.     

Network reliability for multipath TCP networks with a retransmission mechanism under the time constraint

It is essential to reduce data latency and guarantee quality of service for modern computer networks. The emerging networking protocol, Multipath Transmission Control Protocol, can reduce data latency by transmitting data through multiple minimal paths (MPs) and ensure data integrity by the packets retransmission mechanism. The bandwidth of each edge can be considered as multi-state in computer networks because different situations, such as failures, partial failures and maintenance, exist. We evaluate network reliability for a multi-state retransmission flow network through which the data can be successfully transmitted by means of multiple MPs under the time constraint. By generating all minimal bandwidth patterns, the proposed algorithm can satisfy these requirements to calculate network reliability. An example and a practical case of the Pan-European Research and Education Network are applied to demonstrate the proposed algorithm.     

ON TWO-STAGE SUCCESSIVE SAMPLING

In repeated sampling enquiries application of method of successive sampling with partial replacement of units is advantageous. In this paper use of successive sampling technique when the sampling design is multi-stage has been studied. Estimates of population mean and its variance have been obtained (a) on the second occasion, and (b) on the h-th occasion under two systems of replacement, namely (i) by retaining only a fraction p of psu's along with their sample of ssu's, and (ii) by retaining all the psu's on the second occasion, but from each psu only a fraction p of the ssu's within them is retained. A cost function suitable to the design has been illustrated, with the help of data collected from a sample survey conducted by the Indian Council of Agricultural Research.     

Choosing the link function and accounting for link uncertainty in generalized linear models using Bayes factors

One important component of model selection using generalized linear models (GLM) is the choice of a link function. We propose using approximate Bayes factors to assess the improvement in fit over a GLM with canonical link when a parametric link family is used. The approximate Bayes factors are calculated using the Laplace approximations given in [32], together with a reference set of prior distributions. This methodology can be used to differentiate between different parametric link families, as well as allowing one to jointly select the link family and the independent variables. This involves comparing nonnested models and so standard significance tests cannot be used. The approach also accounts explicitly for uncertainty about the link function. The methods are illustrated using parametric link families studied in [12] for two data sets involving binomial responses. The first author was supported by Sonderforschungsbereich 386 Statistische Analyse Diskreter Strukturen, and the second author by NIH Grant 1R01CA094212-01 and ONR Grant N00014-01-10745.