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Models for Repeated Measurements

Author

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  • Lindsey, J. K.

    (Limburgs Universitair Centrum)

Abstract

This second edition of Models for Repeated Measurements has been comprehensively revised and updated, taking into account the huge amount of research that has been carried out in the subject in recent years. A wide variety of useful new models is now available, models that have revolutionized the analysis of such data. The second edition contains three new chapters on models for continuous non-normal data, on various design issues specific to repeated measurements, and on missing data and dropouts. Exercises have been added at the ends of most chapters, and the programming functions needed to carry out the analyses in the book are publicly available. Models for Repeated Measurements is an essential reference for research statisticians in agriculture, medicine, economics, and psychology, and for the many consulting statisticians who want an up-to-date expository account of this important topic. The book is organized into four parts. In the first part, the general context of repeated measurements is presented. The three basic types of response variables, continuous (normal), categorical and count, and duration, are introduced. There is a discussion of the ways in which such repeated observations are interdependent. The book also develops a framework for constructing suitable models, with the introduction of the necessary concepts of multivariate distributions and stochastic processes. In the following three parts, a large number of specific examples, including data tables, is presented to illustrate the models available. Each of these parts corresponds to one of the types of responses mentioned above. FROM REVIEWS OF THE FIRST EDITION . . . a timely book . . . useful both as a graduate text and as a consulting source. Statistical Methods in Medical Research

Suggested Citation

  • Lindsey, J. K., 1999. "Models for Repeated Measurements," OUP Catalogue, Oxford University Press, edition 2, number 9780198505594.
  • Handle: RePEc:oxp:obooks:9780198505594
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    Citations

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    Cited by:

    1. Devin S. Johnson & Jennifer A. Hoeting, 2003. "Autoregressive Models for Capture-Recapture Data: A Bayesian Approach," Biometrics, The International Biometric Society, vol. 59(2), pages 341-350, June.
    2. Marta Nai Ruscone & Daniel Fernández, 2021. "Dynamics of HDI Index: Temporal Dependence Based on D-vine Copulas Model for Three-Way Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 158(2), pages 563-593, December.
    3. Gianni Betti & Antonella D’Agostino & Laura Neri, 2002. "Panel regression models for measuring multidimensional poverty dynamics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(3), pages 359-369, October.
    4. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    5. Courgeau, Daniel, 2007. "Multilevel synthesis. From the group to the individual," MPRA Paper 43189, University Library of Munich, Germany.
    6. A. Azarbar & Y. Zhang & S. Nadarajah, 2019. "An investigation of effective factors on children’s growth failure in Iran using multilevel models," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 553-560, March.
    7. Nadia Solaro & Pier Ferrari, 2007. "Robustness of Parameter Estimation Procedures in Multilevel Models When Random Effects are MEP Distributed," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(1), pages 51-67, June.
    8. Ulf Böckenholt, 2003. "Analysing state dependences in emotional experiences by dynamic count data models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(2), pages 213-226, May.
    9. Lindsey, J.K. & Lindsey, P.J., 2006. "Multivariate distributions with correlation matrices for nonlinear repeated measurements," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 720-732, February.
    10. D. M. Farewell & C. Huang & V. Didelez, 2017. "Ignorability for general longitudinal data," Biometrika, Biometrika Trust, vol. 104(2), pages 317-326.
    11. M. Pourahmadi & M. J. Daniels, 2002. "Dynamic Conditionally Linear Mixed Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 58(1), pages 225-231, March.
    12. P. J. Lindsey & J. Kaufmann, 2004. "Analysis of a longitudinal ordinal response clinical trial using dynamic models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(3), pages 523-537, August.
    13. Mohsen Pourahmadi, 2002. "Graphical Diagnostics for Modeling Unstructured Covariance Matrices," International Statistical Review, International Statistical Institute, vol. 70(3), pages 395-417, December.
    14. Filomena Maggino & Carolina Facioni, 2017. "Measuring Stability and Change: Methodological Issues in Quality of Life studies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(1), pages 161-187, January.
    15. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    16. Luca Merlo & Lea Petrella & Nikos Tzavidis, 2022. "Quantile mixed hidden Markov models for multivariate longitudinal data: An application to children's Strengths and Difficulties Questionnaire scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 417-448, March.
    17. Susanne May & Victor DeGruttola, 2007. "Nonparametric Tests for Two-Group Comparisons of Dependent Observations Obtained at Varying Time Points," Biometrics, The International Biometric Society, vol. 63(1), pages 194-200, March.

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