|Title:||Sieve maximum likelihood estimation in a semi-parametric regression model with errors in variables|
|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.|
|Subject Headings:||errors-in-variables model|
|Appears in Collections:||Sonderforschungsbereich (SFB) 823|
Files in This Item:
|DP_1516_SFB823_Belomestny_Klochkov_Spokoiny.pdf||DNB||4.97 MB||Adobe PDF||View/Open|
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