Kniha Statistical Analysis with Measurement Error or Misclassification Grace Y. Yi

Statistical Analysis with Measurement Error or Misclassification

Autor: Grace Y. Yi
Jazyk: Angličtina
Vazba: Pevná
Dostupnost: Skladem u dodavatele
Odesíláme za 10-13 dnů
3 426
This monograph on measurement error and misclassification emphasizes unique features in modeling and...

Informace o knize

Autor
Jazyk
Angličtina
Vazba
Kniha - Pevná
Vydáno
2017
Stránek
479
EAN
9781493966387
ISBN
1493966383
Enbook ID
13640592
Hmotnost
940
Rozměry
243 x 164 x 35

Kompletní popis

This monograph on measurement error and misclassification 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 brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. 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 methods-such as likelihood and estimating function theory-or modeling schemes in varying settings-such as survival analysis and longitudinal data analysis-can result in a full appreciation of the material, but it is not essential since many chapters provide basic inference frameworks and background information.
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 biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data.

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