Strong Consistency for a Semiparametric Errors-in-variables Under Longitudinal Data

The problem of estimation of a semiparametric errors-in-variables functional relationship model under longitudinal data is studied.By using a nonparametric kernel weight function and total least square method, the estimators of unknown parameters and functions are constructed.Strong convergency of the estimators is investigated.The performance of the simulation is encouraging.The results obtained in the paper can be used to deal with the data processing in the fields of biological engineering, financial analysis and network engineering.