Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1147583 | Journal of Statistical Planning and Inference | 2012 | 16 Pages |
This paper proposes a class of lack-of-fit tests for fitting a linear regression model when some response variables are missing at random. These tests are based on a class of minimum integrated square distances between a kernel type estimator of a regression function and the parametric regression function being fitted. These tests are shown to be consistent against a large class of fixed alternatives. The corresponding test statistics are shown to have asymptotic normal distributions under null hypothesis and a class of nonparametric local alternatives. Some simulation results are also presented.
► We fitted a linear regression model when response variables are missing at random. ► Data set is completed by imputation method. ► Lack-of-fit tests are based on minimum distance method. ► These tests are consistent against a large class of fixed alternatives. ► Test statistics are asymptotic normal under null hypothesis and local alternatives.