Likelihood-based kernel estimation in semiparametric errors-in-covariables models with validation data
We present methods to handle error-in-variables models. Kernel-based likelihood score estimating equation methods are developed for estimating conditional density parameters. In particular, a semiparametric likelihood method is proposed for sufficiently using the information in the data. The asymptotic distribution theory is derived. Small sample simulations and a real data set are used to illustrate the proposed estimation methods.
Volume (Year): 98 (2007)
Issue (Month): 3 (March)
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