Proceedings of the Second Seattle Symposium in Biostatistics (eBook, PDF)

Analysis of Correlated Data
Redaktion: Lin, Danyu; Heagerty, Patrick J.

• Format: PDF

Produktbeschreibung

This volume contains a selection of papers presented at the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data. The symposium was held in 2000 to celebrate the 30^th anniversary of the University of Washington School of Public Health and Community Medicine. It featured keynote lectures by Norman Breslow, David Cox and Ross Prentice and 16 invited presentations by other prominent researchers. The papers contained in this volume encompass recent methodological advances in several important areas, such as longitudinal data, multivariate failure time data and genetic data, as well as innovative applications of the existing theory and methods. This volume is a valuable reference for researchers and practitioners in the field of correlated data analysis.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 331
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781441990761
• Artikelnr.: 44175725

Autorenporträt

PATRICK J. HEAGERTY is an associate professor in the Department of Biostatistics at the University of Washington in Seattle and an associate member at the Fred Hutchinson Cancer Research Center.

Inhaltsangabe

Whither PQL?.- Correlation and marginal longitudinal kernel nonparametric regression.- Analysis of multivariate monotone missing data by a pseudolikelihood method.- Quantile regression for correlated observations.- Small sample inference for clustered data.- Some appications of indirect inference to longitudinal and repeated events data.- On characterizing joint survivor functions by minima.- Nonparametric estimation of the bivariate survivor function.- A semiparametric regression model for panel count data: When do pseudo-likelihood estimators become badly inefficient?.- Some biases that may affect kin-cohort studies for estimating the risks from indentified disease genes.- Optimal structural nested models for optimal sequential decisions.- Addresses for Contact Authors.- List of Referees.