Language: English
Published by Penguin Random House, 2015
ISBN 10: 1580896537 ISBN 13: 9781580896535
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Language: English
Published by Penguin Random House, 2015
ISBN 10: 1580896537 ISBN 13: 9781580896535
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Language: English
Published by Penguin Random House, 2014
ISBN 10: 1580896529 ISBN 13: 9781580896528
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Language: English
Published by Singapore, Springer., 2016
ISBN 10: 9811025932 ISBN 13: 9789811025938
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Condition: New. 1st edition NO-PA16APR2015-KAP.
Language: English
Published by CRC Press, Boca Raton, Florida, 2021
ISBN 10: 1138106402 ISBN 13: 9781138106406
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First Edition
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Language: English
Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
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Condition: Hervorragend. Zustand: Hervorragend | Seiten: 508 | Sprache: Englisch | Produktart: Bücher | This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methodsżsuch as likelihood and estimating function theoryżor modeling schemes in varying settingsżsuch as survival analysis and longitudinal data analysisżcan result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.
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Condition: New. pp. 238 Softcover reprint of the original 1st ed. 2016 edition NO-PA16APR2015-KAP.
Language: English
Published by Chapman and Hall/CRC, 2021
ISBN 10: 1138106402 ISBN 13: 9781138106406
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Language: English
Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032070080 ISBN 13: 9781032070087
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Handbook of Measurement Error Models | Grace Y. Yi (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | Chapman and Hall/CRC | EAN 9781032070087 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Advanced Statistical Methods in Data Science | Ding-Geng Chen (u. a.) | Taschenbuch | xvi | Englisch | 2018 | Springer | EAN 9789811096624 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2018
ISBN 10: 9811096627 ISBN 13: 9789811096624
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invitedthe presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.
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