### Complex Datasets and Inverse Problems: Tomography, Networks and Beyond

Editor: Regina Liu
Editor: William Strawderman
Editor: Cun-Hui Zhang

Lecture Notes--Monograph Series, Volume 54
Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007.
273 pp.

#### Abstract:

This book is a collection of papers dedicated to the memory of Yehuda Vardi. Yehuda was the chair of the Department of Statistics of Rutgers University when he passed away unexpectedly on January 13, 2005. On October 21--22, 2005, some 150 leading scholars from many different fields, including statistics, telecommunications, biomedical engineering, bioinformatics, biostatistics and epidemiology, gathered at Rutgers in a conference in his honor. This conference was on Complex Datasets and Inverse Problems: Tomography, Networks, and Beyond,'' and was organized by the editors. The present collection includes research work presented at the conference, as well as contributions from Yehuda's colleagues.

The theme of the conference was networks and other important and emerging areas of research involving incomplete data and statistical inverse problems. Networks are abundant around us: communication, computer, traffic, social and energy are just a few examples. As enormous amounts of network data are collected in this information age, the field has attracted a great amount of attention from researchers in statistics and computer engineering as well as telecommunication providers and various government agencies. However, few statistical tools have been developed for analyzing network data as they are typically governed by time-varying and mutually dependent communication protocols sitting on complicated graph-structured network topologies. Many prototypical applications in these and other important technologies can be viewed as statistical inverse problems with complex, massive, high-dimensional and possibly biased/incomplete data. This unifying theme of inverse problems is particularly appropriate for a conference and volume dedicated to the memory of Yehuda. Indeed he made influential contributions to these fields, especially in medical tomography, biased data, statistical inverse problems, and network tomography.

The conference was supported by the NSF Grant DMS 05-34181, and by the Faculty of Arts and Sciences and the Department of Statistics of Rutgers University. We would like to thank the participants of the conference, the contributors to the volume, and the anonymous reviewers. Thanks are also due to DIMACS for providing conference facilities, and to the members of the staff and the many graduate students from the Department of Statistics for their tireless efforts to ensure the success of the conference. Last but not least, we would like to thank Ms. Pat Wolf for her patience and meticulous attention to all details in handling the papers in this volume.

Permanent link to this monograph: http://projecteuclid.org/euclid.lnms/1196794933
ISBN:978-0-940600-70-6
ISBN:0-940600-70-6

iii-iv

Preface

Regina Liu, William Strawderman, and Cun-Hui Zhang; v

Dedication

vi

Contributor's List

vii-viii

Deconvolution by simulation

Colin Mallows; 1-11

An iterative tomogravity algorithm for the estimation of network traffic

Jiangang Fang, Yehuda Vardi, and Cun-Hui Zhang; 12-23

Statistical inverse problems in active network tomography

Earl Lawrence, George Michailidis, and Vijayan N. Nair; 24-44

Network tomography based on 1-D projections

Aiyou Chen, and Jin Cao; 45-61

Using data network metrics, graphics, and topology to explore network characteristics

A. Adhikari, L. Denby, J. M. Landwehr, and J. Meloche; 62-75

A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization

Susana Eyheramendy, and David Madigan; 76-91

Estimating the proportion of differentially expressed genes in comparative DNA microarray experiments

Javier Cabrera, and Ching-Ray Yu; 92-102

Functional analysis via extensions of the band depth

Rebecka Jornsten, and Sara López-Pintado; 103-120

A representative sampling plan for auditing health insurance claims

Arthur Cohen, and Joseph Naus; 121-131

Confidence distribution (CD) -- distribution estimator of a parameter

Kesar Singh, Minge Xie, and William E. Strawderman; 132-150

Empirical Bayes methods for controlling the false discovery rate with dependent data

Weihua Tang, and Cun-Hui Zhang; 151-160

A smoothing model for sample disclosure risk estimation

Yosef Rinott, and Natalie Shlomo; 161-171

A note on the $U, V$ method of estimation

Arthur Cohen, and Harold Sackrowitz; 172-176

Local polynomial regression on unknown manifolds

Peter J. Bickel, and Bo Li; 177-186

Shape restricted regression with random Bernstein polynomials

I-Shou Chang, Li-Chu Chien, Chao A. Hsiung, Chi-Chung Wen, and Yuh-Jenn Wu; 187-202

Non- and semi-parametric analysis of failure time data with missing failure indicators

Irene Gijbels, Danyu Lin, and Zhiliang Ying; 203-223

Nonparametric estimation of a distribution function under biased sampling and censoring

Micha Mandel; 224-238

Estimating a Polya frequency function$_2$

Jayanta Kumar Pal, Michael Woodroofe, and Mary Meyer; 239-249

A comparison of the accuracy of saddlepoint conditional cumulative distribution function approximations

Juan Zhang, and John E. Kolassa; 250-259

Multivariate medians and measure-symmetrization

Richard A. Vitale; 260-267

Statistical thinking: From Tukey to Vardi and beyond

Larry Shepp; 268-273