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Sequential Estimates of a Regression Function by Orthogonal Series with Applications in Discrimination

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The First Pannonian Symposium on Mathematical Statistics

Part of the book series: Lecture Notes in Statistics ((LNS,volume 8))

Abstract

Let (X,Y) be a pair of random variables. X takes values in a Borel set A, A⊂ Rp, whereas Y takes values in R. Let f be the marginal Leb. esgue density of X. Based on a sample (X1, Y1),…, (Xn, Yn) of independent observations of (X,Y) we wish to estimate the regression r of Y on X, i.e

$${\rm{r(x) = E[Y|X = x]}}{\rm{.}}$$

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References

  1. Ahmad, I. A. and Lin, P. E., “Nonparametric sequential estimation of a multiple regression function,” Bull. Math. Statist., vol. 17, pp. 63–75, 1976.

    MathSciNet  MATH  Google Scholar 

  2. Čencov, N. N., “Evaluation of an unknown distribution density from observations,” Soviet Math., vol. 3, pp. 1559–1562, 1962.

    Google Scholar 

  3. Devroye, L. P., “Universal consistency in nonparametric regression and nonparametric discrimination,” Technical Report School of Computer Science, McGill university, 1978.

    Google Scholar 

  4. Devroye, 1. P. and Wagner, T. J., “On the L1 convergence of kernel estimators of regression functions with applications in discrimination,” to appear in Z. Wahrscheinlichkeitstheorie und Verw. Gebiete.

    Google Scholar 

  5. Greblicki, W., “Asymptotically optimal probabilistic algorithms for pattern recognition and identification,” Scientific Papers of the Institute of Technical Cybernetics of Wroclaw Technical University Wo. 18, Series: Monographs No. 3, Wroclaw 1974.

    Google Scholar 

  6. Greblicki, W., “Asymptotically optimal pattern recognition procedures with density estimates,” IEEE Trans. Inform. Theory, vol. IT-24, pp. 250–251, 1978.

    Article  MathSciNet  MATH  Google Scholar 

  7. Loéve, M., “Probability Theory I,” 4th Edition, Springer-Verlag, 1977.

    Google Scholar 

  8. Sansone, G., “Orthogonal functions,” Interscience Publishers Inc., New York, 1959.

    MATH  Google Scholar 

  9. Szegö, G., “Orthogonal polynomials,” Amer. Math. Soc. Coll. Publ., vol. 23, 1959.

    Google Scholar 

  10. Tucker, H. G., “A graduate course in probability,” Academic Press, 1967.

    Google Scholar 

  11. Wertz, W. and Schneider, B., “Statistical density estimation: a bibliography,” Intexnat. Statist. Rev., vol. 47, pp. 155–175, 1979.

    MathSciNet  MATH  Google Scholar 

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© 1981 Springer-Verlag New York Inc.

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Rutkowski, L. (1981). Sequential Estimates of a Regression Function by Orthogonal Series with Applications in Discrimination. In: The First Pannonian Symposium on Mathematical Statistics. Lecture Notes in Statistics, vol 8. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-5934-3_21

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  • DOI: https://doi.org/10.1007/978-1-4612-5934-3_21

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-90583-9

  • Online ISBN: 978-1-4612-5934-3

  • eBook Packages: Springer Book Archive

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