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Probability Approximations and Beyond

Lecture Notes in Statistics Band 205

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In June 2010, a conference, Probability Approximations and Beyond, was held at the National University of Singapore (NUS), in honor of pioneering mathematician Louis Chen. Chen made the first of several seminal contributions to the theory and application of Stein’s method. One of his most important contributions has been to turn Stein’s concentration inequality idea into an effective tool for providing error bounds for the normal approximation in many settings, and in particular for sums of random variables exhibiting only local dependence.  This conference attracted a large audience that came to pay homage to Chen and to hear presentations by colleagues who have worked with him in special ways over the past 40+ years. 

The papers in this volume attest to how Louis Chen’s cutting-edge ideas influenced and continue to influence such areas as molecular biology and computer science. He has developed applications of his work on Poisson approximation to problems of signal detection in computational biology. The original papers contained in this book provide historical context for Chen’s work alongside commentary on some of his major contributions by noteworthy statisticians and mathematicians working today.


Einband Taschenbuch
Herausgeber Andrew Barbour, Hock Peng Chan, David Siegmund
Seitenzahl 159
Erscheinungsdatum 07.12.2011
Sprache Englisch
ISBN 978-1-4614-1965-5
Verlag Springer US
Maße (L/B/H) 23,5/15,5/0,9 cm
Gewicht 277 g
Abbildungen 20 schwarz-weiße und 23 farbige Abbildungen, 3 schwarz-weiße Tabellen
Auflage 2012

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  • Couplings for Irregular Combinatorial Assemblies.- Berry-Esseen Inequality for Unbounded Exchangeable Pairs.- Clubbed Binomial Approximation for the Lightbulb Process.- Coverage of Random Discs Driven by a Posson Point Process.- On the Optimality of Stein Factors.- Basic Estimates of Stability Rate for One-dimensional Diffusions.- Trend Analysis of Extreme Values.- Renormalizations in White Noise Analysis.- M-dependence Approximation for Dependent Random Variables.- Variable Selection for Classification and Regression in Large p, Small n Problems.