ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers: 211 (Lecture Notes in Statistics, 211) - Image 1

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Springer ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers: 211 (Lecture Notes in Statistics, 211)

Name: ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers: 211 (Lecture Notes in Statistics, 211)
Brand: Springer
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Public Health

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This proceedings volume contains nine selected papers that were presented in the International Symposium in Statistics, 2012 held at Memorial University from July 16 to 18. These nine papers cover three different areas for longitudinal data analysis, four dealing with longitudinal data subject to measurement errors, four on incomplete longitudinal data analysis, and the last one for inferences for longitudinal data subject to outliers. Unlike in the independence setup, the inferences in measurement errors, missing values, and/or outlier models, are not adequately discussed in the longitudinal setup. The papers in the present volume provide details on successes and further challenges in these three areas for longitudinal data analysis. This volume is the first outlet with current research in three important areas in the longitudinal setup. The nine papers presented in three parts clearly reveal the similarities and differences in inference techniques used for three different longitudinal setups. Because the research problems considered in this volume are encountered in many real life studies in biomedical, clinical, epidemiology, socioeconomic, econometrics, and engineering fields, the volume should be useful to the researchers including graduate students in these areas. From the Back Cover This proceedings volume contains nine selected papers that were presented in the International Symposium in Statistics, 2012 held at Memorial University from July 16 to 18. These nine papers cover three different areas for longitudinal data analysis, four dealing with longitudinal data subject to measurement errors, four on incomplete longitudinal data analysis, and the last one for inferences for longitudinal data subject to outliers. Unlike in the independence setup, the inferences in measurement errors, missing values, and/or outlier models, are not adequately discussed in the longitudinal setup. The papers in the present volume provide details on successes and further challenges in these three areas for longitudinal data analysis. This volume is the first outlet with current research in three important areas in the longitudinal setup. The nine papers presented in three parts clearly reveal the similarities and differences in inference techniques used for three different longitudinal setups. Because the research problems considered in this volume are encountered in many real life studies in biomedical, clinical, epidemiology, socioeconomic, econometrics, and engineering fields, the volume should be useful to the researchers including graduate students in these areas. About the Author Brajendra C. Sutradhar is a University Research Professor at Memorial University in St. John's, Canada. He organized the ISS-2009 as well, with its main theme on inferences in generalized linear longitudinal mixed models (GLLMMs), and edited the special issue of the Canadian Journal of Statistics (2010, Vol. 38, June issue, John Wiley) with seven selected papers from the symposium. He is author of the book Dynamic Mixed Models for Familial Longitudinal Data, published in 2011 by Springer, New York.

Product Specifications

Brand: Springer
Format: Paperback
ASIN: 1489998209
Category: Books > Subjects > Health, Family & Lifestyle > Medical & Healthcare Practitioners > General Medical Issues > Public Health
Domain: Amazon UK
Release Date: 23 August 2015
Listed Since: 24 August 2015

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Springer ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers: 211 (Lecture Notes in Statistics, 211)

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