MODEL-CHECKS FOR HOMOSCHEDASTIC SPATIAL LINEAR REGRESSION MODEL BASED ON BOOTSTRAP METHOD
Abstract: In this
paper we propose
Efron residual based-bootstrap approximation
methods in asymptotic
model-checks for homoschedastic spatial
linear regression models.
It is shown
that under some
regularity conditions given to the known regression functions the
bootstrap version of the sequence of least squares residual partial sums
processes converges in
distribution to a
centred Gaussian process
having sample paths in the space of continuous functions on 1,01,0:I.
Thus, Efron residual based-bootstrap is a consistent approximation in the usual sense. The
finite sample performance of the
bootstrap level Kolmogorov-Smirnov
(KS) type test is also investigated by means of Monte Carlo simulation.
Key words: residual
based-bootstrap, asymptotic model-check, homoschedastic spatial linear
regression models, partial sums, Gaussian process
Penulis: W. Somayasa
Kode Jurnal: jpmatematikagg100002
