1. Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators
- Author
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Daniela Marella, Pier Luigi Conti, Mauro Scanu, CONTI P., L, Marella, Daniela, and Scanu, M.
- Subjects
Statistics and Probability ,education.field_of_study ,Applied Mathematics ,Population ,Local regression ,Estimator ,Regression analysis ,Missing data ,Nonparametric regression ,Computational Mathematics ,Computational Theory and Mathematics ,Statistics ,Linear regression ,Imputation (statistics) ,education ,matching noise ,nonparametrics ,statistical matching ,Mathematics - Abstract
A new matching procedure based on imputing missing data by means of a local linear estimator of the underlying population regression function (that is assumed not necessarily linear) is introduced. Such a procedure is compared to other traditional approaches, more precisely hot deck methods as well as methods based on kNN estimators. The relationship between the variables of interest is assumed not necessarily linear. Performance is measured by the matching noise given by the discrepancy between the distribution generating genuine data and the distribution generating imputed values.
- Published
- 2008