RT Journal Article
T1 Model checks for nonparametric regression with missing data : a comparative study
A1 Cotos Yáñez, Tomas Raimundo
A1 Pérez González, Ana
A1 González Manteiga, Wenceslao
AB This paper analyses the behaviour of the goodness-of-fit tests for regression models. To this end, it uses statistics based on an estimation of the integrated regression function with missing observations either in the response variable or in some of the covariates. It proposes several versions of one empirical process, constructed from a previous estimation, that uses only the complete observations or replaces the missing observations with imputed values. In the case of missing covariates, a link model is used to fill the missing observations with other complete covariates. In all the situations, Bootstrap methodology is used to calibrate the distribution of the test statistics. A broad simulation study compares the different procedures based on empirical regression methodology, with smoothed tests previously studied in the literature. The comparison reflects the effect of the correlation between the covariates in the tests based on the imputed sample for missing covariates. In addition, the paper proposes a computational binning strategy to evaluate the tests based on an empirical process for large data sets. Finally, two applications to real data illustrate the performance of the tests.
PB Journal of Statistical Computation and Simulation
SN 00949655
YR 2016
FD 2016-03-04
LK http://hdl.handle.net/11093/1144
UL http://hdl.handle.net/11093/1144
LA eng
NO Journal of Statistical Computation and Simulation, 86(16): 3188-3204 (2016)
NO Xunta de Galicia | Ref. GRC ED431C 2016/040
DS Investigo
RD 11-sep-2024