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
General
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