Radiation Risk Estimation

Sergii Masiuk, Alexander Kukush, Sergiy Shklyar, Mykola Chepurny, Illya Likhtarov

ISBN 10: 3110441802 / ISBN 13: 9783110441802

Published by De Gruyter, DE, 2017

About this Item

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 covariatesMeasurement error modelsLinear models with classical errorPolynomial regression with known variance of classical errorNonlinear and generalized linear models Part II Radiation risk estimation under uncertainty in exposure dosesOverview of risk models realized in program package EPICUREEstimation of radiation risk under classical or Berkson multiplicative error in exposure dosesRadiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accidentElements of estimating equations theoryConsistency of efficient methodsEfficient SIMEX method as a combination of the SIMEX method and the corrected score methodApplication of regression calibration in the model with additive error in exposure doses.

Seller Inventory # LU-9783110441802

Bibliographic Details

• Title: Radiation Risk Estimation
• Publisher: De Gruyter, DE
• Publication Date: 2017
• Language: English
• ISBN 10: 3110441802
• ISBN 13: 9783110441802
• Binding: Hardback
• Edition: 1st.
• Condition: New
• Dimensions: 6.69W x 0.63H x 9.61D inches 6.69 inches width by 0.63 inches height by 9.61 inches depth
• Weight: 592 grams

About this title

Synopsis

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

About the Author

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