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Finite sample performance of density estimators under moving average dependence

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  • Wand, M. P.

Abstract

We study the finite sample performance of kernel density estimators through exact mean integrated squared error formulas when the data belong to an infinite order moving average process. It is demonstrated that dependence can have a significant influence, even in situations where the asymptotic performance is unaffected.

Suggested Citation

  • Wand, M. P., 1992. "Finite sample performance of density estimators under moving average dependence," Statistics & Probability Letters, Elsevier, vol. 13(2), pages 109-115, January.
    • Handle: RePEc:eee:stapro:v:13:y:1992:i:2:p:109-115
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    Citations

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    Cited by:

    1. Matthew Pritsker, 1997. "Nonparametric density estimation and tests of continuous time interest rate models," Finance and Economics Discussion Series 1997-26, Board of Governors of the Federal Reserve System (U.S.).
    2. Fernandez, J. M. Vilar & Manteiga, W. Gonzalez, 2000. "Resampling for checking linear regression models via non-parametric regression estimation," Computational Statistics & Data Analysis, Elsevier, vol. 35(2), pages 211-231, December.
    3. Vilar, José A. & Vilar, Juan M., 2000. "Finite sample performance of density estimators from unequally spaced data," Statistics & Probability Letters, Elsevier, vol. 50(1), pages 63-73, October.
    4. Pritsker, Matt, 1998. "Nonparametric Density Estimation and Tests of Continuous Time Interest Rate Models," The Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 449-487.
    5. Saavedra, Ángeles & Cao, Ricardo, 1999. "Rate of convergence of a convolution-type estimator of the marginal density of a MA(1) process," Stochastic Processes and their Applications, Elsevier, vol. 80(2), pages 129-155, April.
    6. Wand, M. P., 1998. "Finite sample performance of deconvolving density estimators," Statistics & Probability Letters, Elsevier, vol. 37(2), pages 131-139, February.

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