The Simulation Extrapolation Method for Fitting Generalized Linear Models with Additive Measurement Error

Simulation - Extrapolation : the Measurement Error

Generalized Partially Linear Measurement Error Models

We consider generalized partially linear measurement error models when the linear covariate is measured with additive errors. Estimators of parameter and nonparametric function obtained by local liner regression with kernel weight, the SIMEX technique, and generalized estimating equation are proposed. The asymptotic normality of the estimate of the parameter and the asymptotics of the estimate ...

Detection of Outliers and Influential Observations in Linear Ridge Measurement Error Models with Stochastic Linear Restrictions

The aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. In addition, we derive the corrected score test statistic for outliers detection ba...

Fitting Generalized Additive Models with the GAM Procedure

This paper describes the use of the GAM procedure for fitting generalized additive models (Hastie and Tibshirani, 1990). PROC GAM, production in Release 8.2, provides an array of powerful tools for data analysis, incorporating nonparametric regression and smoothing techniques as well as generalized distributional modeling. Compared with other regression procedures such as PROC REG and PROC GLM,...

Data for Measurement Error in Generalized Linear Models

Key WOrds and Phrases: errors-in-variab7es; functiona7 mode7s; genera7ized 7inear mode7; /II-estimation; measurement error; structura7 mode7s. ABSTRACT This paper discussed a general strategy for reducing measurement-error-induced bias in statistical models. It is assu.ed that the measure.ent ·error is unbiased with a known variance although no other distributional assulIPtions on the measur..e...