We discuss and illustrate the method of simulation extrapolation for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). As in Hardin, Schmiediche, and Carroll (2003), our discussion includes specified measurement error and measurement error estimated by replicate error-prone proxies. In addition, we discuss and illustrate three extrapolant functions
Measurement error in the continuous covariates of a model generally yields bias in the estimators. I...
Abstract. Generalized linear models with covariate measurement error can be estimated by maximum lik...
This paper proposes a structural analysis for generalized linear models when some explanatory variab...
We discuss and illustrate the method of simulation extrapolation for fitting models with additive me...
This paper discusses and illustrates the method of regression calibration. This is a straightforward...
This paper introduces additive measurement error in a generalized linear-model context. We discuss t...
Measurement error is pervasive in statistics due to the non-availability of authentic data. The reas...
This paper describes an EM algorithm for maximum likelihood estimation in generalized linear models ...
Abstract. This paper discusses and illustrates the method of regression calibra-tion. This is a stra...
While most of the literature on measurement error focuses on additive measurement error, we consider...
Graduation date: 1990This thesis considers the problem of estimating the linear\ud parameters of gen...
A method for making inferences about the components of a generalized additive model is described. It...
It is well known that ignoring measurement errors in covariates in the model leads to biased estimat...
1. Measurement error and other forms of uncertainty are commonplace in ecology and evolution, and ma...
Generalized linear models with covariate measurement error can be estimated by maximum likelihood us...
Measurement error in the continuous covariates of a model generally yields bias in the estimators. I...
Abstract. Generalized linear models with covariate measurement error can be estimated by maximum lik...
This paper proposes a structural analysis for generalized linear models when some explanatory variab...
We discuss and illustrate the method of simulation extrapolation for fitting models with additive me...
This paper discusses and illustrates the method of regression calibration. This is a straightforward...
This paper introduces additive measurement error in a generalized linear-model context. We discuss t...
Measurement error is pervasive in statistics due to the non-availability of authentic data. The reas...
This paper describes an EM algorithm for maximum likelihood estimation in generalized linear models ...
Abstract. This paper discusses and illustrates the method of regression calibra-tion. This is a stra...
While most of the literature on measurement error focuses on additive measurement error, we consider...
Graduation date: 1990This thesis considers the problem of estimating the linear\ud parameters of gen...
A method for making inferences about the components of a generalized additive model is described. It...
It is well known that ignoring measurement errors in covariates in the model leads to biased estimat...
1. Measurement error and other forms of uncertainty are commonplace in ecology and evolution, and ma...
Generalized linear models with covariate measurement error can be estimated by maximum likelihood us...
Measurement error in the continuous covariates of a model generally yields bias in the estimators. I...
Abstract. Generalized linear models with covariate measurement error can be estimated by maximum lik...
This paper proposes a structural analysis for generalized linear models when some explanatory variab...