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A generalization of histogram type estimators

dc.contributor.authorRío Bueno , Manuel del
dc.contributor.authorDelicado, Pedro
dc.date.accessioned2023-06-20T09:44:05Z
dc.date.available2023-06-20T09:44:05Z
dc.date.issued2003-02
dc.description.abstractWe introduce nonparametric density estimators that generalize the classical histogram and frequency polygon. The new estimators are expressed as linear combinations of density functions that are piecewise polynomials, where the coefficients are optimally chosen in order to minimize an approximate version of the integrated square error of the estimator. We establish the asymptotic behaviour of the proposed estimators, and study their performance in a simulation study.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/17618
dc.identifier.doi10.1080/1048525031000074523
dc.identifier.issn1048-5252
dc.identifier.relatedurlhttp://www.econ.upf.edu/ca/recerca/paper.php?id=422
dc.identifier.urihttps://hdl.handle.net/20.500.14352/50269
dc.issue.number1
dc.journal.titleJournal of Nonparametric Statistics
dc.page.final135
dc.page.initial113
dc.publisherAmerican Statistical Association
dc.rights.accessRightsmetadata only access
dc.subject.cdu519.8
dc.subject.keywordConvolution
dc.subject.keywordFrequency polygon
dc.subject.keywordNonparametric density estimation
dc.subject.keywordSimulation
dc.subject.keywordSplines
dc.subject.keywordToeplitz matrix
dc.subject.keywordKerrnel density estimators
dc.subject.keywordBinned data
dc.subject.ucmInvestigación operativa (Matemáticas)
dc.subject.unesco1207 Investigación Operativa
dc.titleA generalization of histogram type estimators
dc.typejournal article
dc.volume.number15
dcterms.referencesBoneva, L. I., Kendall, D. and Stefanov, I. (1971). Spline transformations: Three new diagnostic aids for the statistical data-analyst. J. Roy. Statist. Soc. Ser. B, 33, 1–70. Feller, W. (1971). An Introduction to Probability Theory and its Applications, 2nd ed., Vol. II. J. Wiley, New York. Gajek, L. (1986). On improving density estimators which are not bona fide functions. The Annals of Statistics, 14, 1612–1618. Hall, P. (1982). The influence of rounding errors on some nonparametric estimators of a density and its derivatives. SIAM J. Appl. Math., 42(2), 390–399. Hall, P. and Murison, R. D. (1993). Correcting the negativity of high-order kernel density estimators. Journal of Multivariate Analysis, 47, 103–122. Härdle, W. (1991). Smoothing Techniques with Implementation in S. Springer, New York. Jones, M. C. (1989). Discretized and interpolated kernel density estimates. J. Amer. Statist. Assoc., 84(407), 733–741. Jones, M. C. (1992). Differences and derivatives in kernel estimation. Metrika, 39(6), 335–340. Jones, M. C., Samiuddin, M., Al-Harbey, A. H. and Maatouk, T. A. H. (1998). The edge frequency polygon. Biometrika, 85(1), 235–239. Jones, M. C. and Signorini, D. F. (1997). A comparison of higher-order bias kernel density estimators. Journal of the American Statistical Association, 92, 1063–1073. Marron, J. S. and Wand, M. P. (1992). Exact mean integrated squared error. The Annals of Statistics, 20, 712–736. Minnotte, M. C. (1996). The bias-optimized frequency polygon. Computational Statistics, 11, 35–48. Minnotte, M. C. (1998). Achieving higher-order convergence rates for density estimation with binned data. Journal of the American Statistical Association, 93, 663–672. Rudemo, M. (1982). Empirical choice of histograms and kernel density estimators. Scandinavian Journal of Statistics, 9, 65–78. Schumaker, L. L. (1981). Spline Functions. Basic Theory. J. Wiley, New York. Scott, D. W. (1992). Multivariate Density Estimation. J. Wiley, New York. Scott, D. W. and Sheather, S. J. (1985). Kernel density estimation with binned data. Comm. Statist. Theory Meth., 14, 1353–1359. Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, Methodological, 53, 683–690. Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. Simonoff, J. S. (1996). Smoothing Methods in Statistics. Springer, New York. Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London. Waterloo Maple Inc. (1998). Maple V. Release 5.1. Wolfram, S. (1991). Mathematica: A System for Doing Mathematics by Computer, 2nd ed., Addison-Wesley.
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