Radiation Risk Estimation - Based on Measurement Error Models

This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies. Contents:

Part I - Estimation in regression models with errors in covariates
Measurement error models
Linear models with classical error
Polynomial regression with known variance of classical error
Nonlinear and generalized linear models

Part II Radiation risk estimation under uncertainty in exposure doses
Overview of risk models realized in program package EPICURE
Estimation of radiation risk under classical or Berkson multiplicative error in exposure doses
Radiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accident
Elements of estimating equations theory
Consistency of efficient methods
Efficient SIMEX method as a combination of the SIMEX method and the corrected score method
Application of regression calibration in the model with additive error in exposure doses

A propos de l'auteur

S. Masiuk, A. Kukush, S. Shklyar, M. Chepurny, I. Likhtarov, Radiation Protection Institute, Ukraine.

Commentaire

"[...] this monograph is highly recommended."
Xia Wang in: Mathematical Reviews Clippings (2018), MR3726857

Détails du produit

• Auteurs: Mykola Chepurny, Alexande Kukush, Alexander Kukush, Illya Likhtarov, Sergi Masiuk, Sergii Masiuk, Sergiy Shklyar, Sergiy e Shklyar
• Edition: De Gruyter
• Langues: Anglais
• Format d'édition: Livre Relié
• Sortie: 01.03.2017
• EAN: 9783110441802
• ISBN: 978-3-11-044180-2
• Pages: 240
• Dimensions: 170 mm x 22 mm x 240 mm
• Poids: 594 g
• Illustrations: 13 b/w ill.
• Thèmes: De Gruyter Series in Mathematics and Life Sciences; De Gruyter Series in Mathematics and Life Sciences; ISSN
• Catégorie: Sciences naturelles, médecine, informatique, technique > Mathématiques > Autres