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.
CONTI P., L., Marella, D., Scanu, M. (2008). Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 53(2), 354-365 [10.1016/j.csda.2008.07.041].
Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators
MARELLA, Daniela;
2008-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
