What question did this study set out to answer?

To develop a semiparametric estimation and inference method for logistic regression when predictors are measured with error, using a small validation dataset without assuming specific distributions for the true or surrogate predictors.

July 1, 1991Journal of the Royal Statistical Society Series B (Statistical Methodology)

Semiparametric Estimation in Logistic Measurement Error Models

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To develop a semiparametric estimation and inference method for logistic regression when predictors are measured with error, using a small validation dataset without assuming specific distributions for the true or surrogate predictors.
Analyzed a measurement error structure comprising a primary dataset with binary response Y and surrogate predictor W, alongside a smaller validation dataset containing (Y, X, W).
Constructed a semiparametric estimator for the logistic regression parameter using kernel regression techniques.
Derived the asymptotic distribution and analyzed mean-squared error for scalar predictors to determine optimal bandwidth selection.
The semiparametric parameter estimator is computationally feasible and achieves asymptotic normality without requiring parametric distributional assumptions for the predictors.
Analytical evaluation of mean-squared error for scalar predictors yielded an explicit representation for optimal bandwidth selection.

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Implication

Methodological study demonstrates semiparametric estimation for logistic models with measurement error, suggesting accurate inference using validation subsets.

Cite This Study

Carroll et al. (1991) studied this question.

synapsesocial.com/papers/6a174ee54c7f4e15c15df7d3 https://doi.org/10.1111/j.2517-6161.1991.tb01846.x

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Implication

Methodological study demonstrates semiparametric estimation for logistic models with measurement error, suggesting accurate inference using validation subsets.

Semiparametric Estimation in Logistic Measurement Error Models

Key Points

Discussion

Implication

Cite This Study

Semiparametric Estimation in Logistic Measurement Error Models

Key Points

Discussion

Implication

Cite This Study

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