Sieve maximum likelihood estimation in a semi-parametric regression model with errors in variables
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Date
2016
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Abstract
The paper deals with a semi-parametric regression problem under deterministic
and regular design which is observed with errors. We first
linearise the problem using a sieve approach and then apply the total
penalised maximum likelihood estimator to the linearised model. Sufficient conditions for
√n-consistency and efficiency under parametric
assumption are derived and a possible misspecification bias under different
smoothness assumptions on the design is analysed. The Monte
Carlo simulations show the performance of the estimator with simulated
data.
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Keywords
errors-in-variables model, √n-consistency, regression