Abstract
Multivariate Projection Pursuit Density Estimation (PPDE) does not suffer from the “curse of dimensionnality” as the more classical kernel density estimation does, however a means of evaluating its stability and precision is needed, and this paper shows how the bootstrap can provide certain useful confidence intervals, the method used for constructing them starts by a pre-pivoting process.
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References
Beran R., 1987, “Pre-pivoting to reduce level error of confidence sets”, Biometrika, vol. 74, pp. 457–468.
Efron B., 1987, “Better Bootstrap Confidence Intervals”, J.A.S.A., vol. 82, pp. 171–185.
Elguero E.,(to appear in 1988), “Contribution à l’Elaboration d’une Méthodologie pour VAnalyse Statistique d’un Réseau de Télémesure”, Thèse à FU.S.T.L., Montpellier.
Friedman J.H., Stuetzle W., and Schroeder A., 1984, “Projection Pursuit Density Estimation”, J.A.S.A., vol. 79.
Hall P.,1986, “On the number of Bootstrap Simulations Required to Construct a Confidence Interval”, Ann. Stat., vol. 14, pp.1453–1462.
Huber P.J., 1985, “Projection Pursuit”, Ann. Stat., vol. 13, pp.435–475.
Kennedy W.J. and Gentle J.E., 1985, Statistical Computing, Ed. M. Dekker, New York.
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© 1988 Physica-Verlag Heidelberg
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Elguero, E., Holmes-Junca, S. (1988). Confidence Regions for Projection Pursuit Density Estimates. In: Edwards, D., Raun, N.E. (eds) Compstat. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-46900-8_6
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DOI: https://doi.org/10.1007/978-3-642-46900-8_6
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-0411-9
Online ISBN: 978-3-642-46900-8
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