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Applied Linear Regression for Longitudinal Data: With an Emphasis on Missing Observations [Kietas viršelis]

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  • Formatas: Hardback, 226 pages, aukštis x plotis: 234x156 mm, weight: 453 g, 73 Tables, black and white; 1 Line drawings, color; 46 Line drawings, black and white; 1 Illustrations, color; 46 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 09-Dec-2022
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 0367634317
  • ISBN-13: 9780367634315
Kitos knygos pagal šią temą:
Applied Linear Regression for Longitudinal Data: With an Emphasis on Missing ObservationsDidesnis paveikslėlis
  • Formatas: Hardback, 226 pages, aukštis x plotis: 234x156 mm, weight: 453 g, 73 Tables, black and white; 1 Line drawings, color; 46 Line drawings, black and white; 1 Illustrations, color; 46 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 09-Dec-2022
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 0367634317
  • ISBN-13: 9780367634315
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780367634315.html
Raktažodžiai:
This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following:





Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies

It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.

Recenzijos

"Overall, the book is well written. It is clear and allows the reader understanding the main concepts behind models for longitudinal data analysis, with few effort from a technical viewpoint. The examples used to illustrate the methods covered in the textbook are numerous and also rather easy to follow. This helps the reader learn how to proceed with a full longitudinal data analysis."

Maria Francesca Marino, University of Florence, Italy, The American Statistician, February 2024.

"Overall, this book is a very comprehensive coverage of the methods for analysing longitudinal data with missing observations. For those of us who teach or supervise students and researchers in the application of linear regression models, this is a useful resource. One of the most notable aspects of the book is the wealth of exercises. The book's companion website provides the data files for working through the exercises. If you are looking for a textbook that explains the material through worked examples and exercises, then you have found a real gem. Similarly, if you are a practising biostatistician with a particularly developed understanding of the nuances of longitudinal data analysis, you will find it of academic interest."

Pentti Nieminen, Finland, ISCB News, May 2024.

1. Scientific Framework of Data Analysis
2. Revisiting and Shortcomings
of Standard Linear Regression Models
3. An Introduction to the Analysis of
Longitudinal Data
4. Model Building for Longitudinal Data Analysis
5.
Analysis of a Pre/Post Measurement Design
6. Analysis of Longitudinal
Life-Event Studies
7. Analysis of Longitudinal Experimental Studies
Frans E.S. Tan is an associate professor (retired) of methodology and statistics at Maastricht University, The Netherlands.

Shahab Jolani is an assistant professor of methodology and statistics at Maastricht University, The Netherlands.