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Journal : Jurnal Matematika

Model-Check Based on Residual Partial Sums Process of Heteroscedastic spatial Linear Regression Models Wayan Somayasa
Jurnal Matematika Vol 1 No 2 (2011)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2011.v01.i02.p15

Abstract

It is common in practice to evaluate the correctness of an assumed linear regressionmodel by conducting a model-check method in which the residuals of the observations areinvestigated. In the asymptotic context instead of observing the vector of the residuals directly,one investigates the partial sums of the observations. In this paper we derive a functional centrallimit theorem for a sequence of residual partial sums processes when the observations comefrom heteroscedastic spatial linear regression models. Under a mild condition it is shown thatthe limit process is a function of Brownian sheet. Several examples of the limit processes arealso discussed. The limit theorem is then applied in establishing an asymptotically Kolmogorovtype test concerning the adequacy of the fitted model. The critical regions of the test for finitesample sizes are constructed by Monte Carlo simulation.
BY MEANS OF LINDEBERG’S CENTRAL LIMIT THEOREM WAYAN SOMAYASA
Jurnal Matematika Vol 1 No 1 (2010)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2010.v01.i01.p11

Abstract

We study the construction of a version of standard Brownian sheet called h -generalized standardBrownian sheet. It is shown by means of Lindeberg’s theorem that it is a limit process of a sequence ofpartial sums processes of independent random variables in the sense of weak convergence in the metricspace of continuous functions on the compact region [0,1]×[0,1]. Based on this convergence weapproximate by simulation the quantiles of Kolmogorov, Kolmogorov-Smirnov and Cramér-von Misestype statistics which are defined as continuous functionals of the process.