Geodesic
Using randomization to improve performance of a variance estimator of strongly dependent errors
Artur Bryk1
1 Department of Mathematics and Mathematical Economics Warsaw School of Economics Al. Niepodległości 162 02-554 Warszawa, Poland
Applicationes Mathematicae, Tome 39 (2012) no. 3, pp. 273-282 Cet article a éte moissonné depuis la source Institute of Mathematics Polish Academy of Sciences

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We consider a fixed-design regression model with long-range dependent errors which form a moving average or Gaussian process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We estimate the variance of the errors using the Rice estimator. The estimator is shown to exhibit weak (i.e. in probability) consistency. Simulation results confirm this property for moderate and large sample sizes when randomization is employed.
DOI : 10.4064/am39-3-2
Keywords: consider fixed design regression model long range dependent errors which form moving average gaussian process introduce artificial randomization grid points which observations taken order diminish impact strong dependence estimate variance errors using rice estimator estimator shown exhibit weak probability consistency simulation results confirm property moderate large sample sizes randomization employed

Artur Bryk 1

1 Department of Mathematics and Mathematical Economics Warsaw School of Economics Al. Niepodległości 162 02-554 Warszawa, Poland 
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Artur Bryk. Using randomization to improve performance of a variance estimator of
 strongly dependent errors. Applicationes Mathematicae, Tome 39 (2012) no. 3, pp. 273-282. doi: 10.4064/am39-3-2

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