Flexible parametric approach to classical measurement error variance estimation without auxiliary data
نویسندگان
چکیده
منابع مشابه
Flexible parametric measurement error models.
Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from standard parametric models. We use mixtures of norma...
متن کاملNonparametric variance estimation in the analysis of microarray data: a measurement error approach.
This article investigates the effects of measurement error on the estimation of nonparametric variance functions. We show that either ignoring measurement error or direct application of the simulation extrapolation, SIMEX, method leads to inconsistent estimators. Nevertheless, the direct SIMEX method can reduce bias relative to a naive estimator. We further propose a permutation SIMEX method wh...
متن کاملFlexible Parametric Measurement
Inferences in measurement error models can be sensitive to modeling assumptions. Speciically, if the model is incorrect then the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the eeciency of parametric inference we propose to use exible parametric models which can accommodate departures from standard parametric models. We use mixtures of norma...
متن کاملDensity Estimation with Normal Measurement Error with Unknown Variance
Abstract: This paper deals with the problem of estimating a density based on observations which are contaminated by a normally distributed error whose variance is unknown. In the case of a completely unknown error variance, the impossibility of a uniformly consistent estimation is shown; however, a semi-uniformly consistent estimator is constructed under nonparametric smoothness conditions on t...
متن کاملParametric Measurement Error Models
We describe a simulation-based method of inference for parametric measurement error models in which the measurement error variance is known or at least well estimated. The method entails adding additional measurement error in known increments to the data, computing estimates from the contaminated data, establishing a trend between these estimates and the variance of the added errors, and extrap...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biometrics
سال: 2018
ISSN: 0006-341X,1541-0420
DOI: 10.1111/biom.12960