Johnson Wesley O (42 results)

Hardcover. Condition: Used: Good. Book is in very good condition with only few underlined pages. Minor shelf/edge wear. Un-creased spine, binding tight, name written on first page.

Hardcover. Condition: Fine. xvi, 448 p. 24 cm. Numerous diagrams and tables. Purple hardcover.

Condition: very good. Berlin & New York: Springer-Verlag, 1996. Hardcover. xvi. 448 pp. Index. - Contents : After Dinner Remarks: On the Occasion of Seymour Geisser's 65th Birthday, Hsinchu, Taiwan, December 13, 1994 / Marvin Zelen -- Story about Cosimo di Medici / David Lane -- Some Statistical Issues in Forensic DNA Profiling / Seymour Geisser -- Statistical Practice as Argumentation: A Sketch of a Theory of Applied Statistics / James S. Hodges -- Decision Rules and Market Share: Aggregation in an Information Contagion Model / David Lane and Roberta Vescovini -- Bayesian Method of Moments (BMOM) Analysis of Mean and Regression Models / Arnold Zellner -- On the Prediction of Growth Curves / Jack C. Lee and Seymour Geisser -- Predictive Influence in the Log Normal Survival Model / Wesley O. Johnson -- Selecting the Form of Combining Regressions Based on Recursive Prediction Criteria / Kuo-yuan Liang and Keunkwan Ryu -- Multiperiod-Ahead Predictive Densities and Model Comparison in Dynamic Models / Chung-ki Min -- Bayesian Prediction Under Asymmetric Linear Loss: Forecasting State Tax Revenues in Iowa / Charles H. Whiteman -- On Small Sample Bayesian Inference and Sequential Design for Quantal Response Curves / Tom Leonard and John S. J. Hsu -- Some Simulation Results for the Performance of DNA Classification of Noisy Image Data / S. James Press -- Case-Control Studies and Bayesian Inference / M. Zelen and R. A. Parker -- Orthogonalizations and Prior Distributions for Orthogonalized Model Mixing / Merlise Clyde and Giovanni Parmigiani -- Improved Quasi-Maximum Likelihood Estimation for Stochastic Volatility Models / F. Jay Breidt and Alicia L. Carriquiry -- Bayesian Inference for Linear Models Subject to Inequality Constraints / John F. Geweke -- The Asymptotic Distribution of the Maximum Likelihood and Mantel-Haenszel Estimators of the Common Odds Ratio in k 2x2 Tables / Samuel W. Greenhouse and Joseph L. Gastwirth -- On the Justification of Default and Intrinsic Bayes Factors / James O. Berger and Luis R. Pericchi -- Tests on Independence of VNTR Alleles in the Chinese Population in Hong Kong / Wing K. Fung -- Bayes Factors for Testing the Equality of Covariance Matrix Eigenvalues / Robert E. McCulloch and Peter E. Rossi -- Model Selection in Parapsychology: Psychokinesis, Precognition or Chance / Jessica Utts -- An Empirical Bayesian Approach to Cointegrating Rank Selection and Test of the Present Value Model for Stock Prices / John C. Chao and Peter C. B. Phillips -- Prediction of Individual Bond Prices via the TDM Model / Takeaki Kariya and Hiroshi Tsuda -- On the Use of Canonical Correlation Analysis in Testing Common Trends / N. H. Chan and Ruey S. Tsay -- Conjugate Processes / Clive W. J. Granger and Jin-Lung Lin -- Nonlinear Time Series-Bifurcation, Chaos and Stationarity / Wolfgang Kliemann -- The VAR-VARCH Model: A Bayesian Approach / Wolfgang Polasek and Hideo Kozumi -- Statistical Assessment of Atmospheric Ozone Data for Depletion / Gregory C. Reinsel and G. C. Tiao. Condition : very good copy. ISBN 9780387948089. Keywords : MATHEMATICS, statistics.…

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

PF. Condition: New.

Condition: New. In English.

Condition: New. pp. 468.

Condition: Used. pp. 448.

Paperback. Condition: Brand New. reprint edition. 464 pages. 9.25x6.10x1.34 inches. In Stock.

Condition: Used. pp. 448.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Modelling and Prediction Honoring Seymour Geisser contains the refereed proceedings of the Conference on Forecasting, Prediction, and Modelling held at National Chiao Tung University, Taiwan in 1994. The papers discuss general methodological issues; prediction; design of experiments and classification; prior distributions and estimation; posterior odds, testing, and model selection; modelling and prediction in finance; and time series modelling and applications. Specific topics include very interesting and topical statistical issues related to DNA fingerprinting and spatial image reconstruction, foundational issues for applied statistics and testing hypotheses, forecasting tax revenues and bond prices, and assessing oxone depletion.…

Condition: Used. pp. 448.

Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. PP. 516 Index.

Hardback. Condition: New. A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses. The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development. The author's writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.…

Condition: New. pp. xv + 192 Illus.

Condition: New. PP. 516.

Condition: New. pp. xv + 192 Index.

Hardcover. Condition: Brand New. 1st edition. 192 pages. 9.50x6.25x0.75 inches. In Stock.

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Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Condition: Brand New. 1st edition. 498 pages. 10.75x7.50x1.25 inches. In Stock.

Condition: New. In English.

Hardback. Condition: New. A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses. The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development. The author's writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.…

Hardback. Condition: New. Praise for Bayesian Thinking in Biostatistics:"This thoroughly modern Bayesian book .is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are activated by excellent coverage of computing methods and provision of code. Their content on model assessment, robustness, data-analytic approaches and predictive assessments.are essential to valid practice. The numerous exercises and professional advice make the book ideal as a text for an intermediate-level course." -Thomas Louis, Johns Hopkins University"The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes it a complete self- contained introduction to Bayesian inference for biomedical problems.Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems."- Peter Mueller, University of TexasWith a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently use, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests.Key Features Applies a Bayesian perspective to applications in biomedical science Highlights advances in clinical trial design Goes beyond standard statistical models in the book by introducing Bayesian nonparametric methods and illustrating their uses in data analysis Emphasizes estimation of biomedically relevant quantities and assessment of the uncertainty in this estimation Provides programs in the BUGS language, with variants for JAGS and Stan, that one can use or adapt for one's own researchThe intended audience includes graduate students in biostatistics, epidemiology, and biomedical researchers, in generalAuthorsGary L. Rosner is the Eli Kennerly Marshall, Jr., Professor of Oncology at the Johns Hopkins School of Medicine and Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Purushottam (Prakash) W. Laud is Professor in the Division of Biostatistics, and Director of the Biostatistics Shared Resource for the Cancer Center, at the Medical College of Wisconsin. Wesley O. Johnson is professor Emeritus in the Department of Statistics as the Univer.…