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Influence of design on the rate of convergence to normality in L1 regression

Published online by Cambridge University Press:  28 June 2011

Peter Hall
Affiliation:
Statistics Department, Australian National University, Canberra, Australia
A. H. Welsh
Affiliation:
Statistics Department, Australian National University, Canberra, Australia

Abstract

We provide a concise account of the influence of design variables on the convergence rate in an L1 regression problem. In particular, we show that the convergence rate may be characterized precisely in terms of third and fourth moments of the design variables. This result leads to necessary and sufficient conditions on the design for the Berry-Esseen rate to be achieved. We also show that a moment condition on the error distribution is necessary and sufficient for a non-uniform Berry-Esseen theorem, and that an Edgeworth expansion is possible if the design points are not too clumped.

Type
Research Article
Copyright
Copyright © Cambridge Philosophical Society 1989

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References

[1] Bassett, G. and Koenker, R.. Asymptotic theory of least absolute error regression. J. Amer. Statist. Assoc. 73 (1978), 618622.CrossRefGoogle Scholar
[2] Bikelis, A.. Estimates of the remainder term in the central limit theorem. Litovsk. Mat. Sb. 6 (1966), 323346.Google Scholar
[3] Dodge, T.. Statistical Data Analysis Based on the L1-Norm and Related Topics (Elsevier Science Publishers, 1987).Google Scholar
[4] Gnedenko, B. V. and Kolmogorov, A. N.. Limit Distributions for Sums of Independent Random Variables (Addison-Wesley, 1968).Google Scholar
[5] Hall, P.. Rates of Convergence in the Central Limit Theorem (Pitman, 1982).Google Scholar
[6] Hoeffding, W.. Probability inequalities for sums of bounded random variables. J. Amer. Statist. Assoc. 58 (1963), 1330.CrossRefGoogle Scholar
[7] Petrov, V. V.. Sums of Independent Random Variables (Springer-Verlag, 1975).Google Scholar
[8] Reiss, R.-D.. On the accuracy of the normal approximation for quantiles. Ann. Probab. 2 (1974), 741744.CrossRefGoogle Scholar
[9] Reiss, R.-D.. Asymptotic expansions for sample quantiles. Ann. Probab. 4, (1976), 249258.CrossRefGoogle Scholar