Journal of Mathematical Sciences: Advances and Applications
[1] A. Basu, I. R. Harris, N. L. Hjort and M. C. Jones, Robust and
efficient estimation by minimising a density power divergence,
Biometrika 85(3) (1998), 549-559.
DOI: https://doi.org/10.1093/biomet/85.3.549
[2] A. W. Bowman, An alternative method of cross-validation for the
smoothing of density estimates, Biometrika 71(2) (1984), 353-360.
DOI: https://doi.org/10.1093/biomet/71.2.353
[3] A. W. Bowman, A comparative study of some kernel-based
nonparametric density estimators, Journal of Statistical Computation
and Simulation 21(3-4) (1985), 313-327.
DOI: https://doi.org/10.1080/00949658508810822
[4] J. P. Copas and M. J. Fryer, Density estimation and suicide risks
in psychiatric treatment, Journal of the Royal Statistical Society:
Series A 143(2) (1980), 167-176.
[5] P. Hall, Large sample optimality of least squares cross-validation
in density estimation, The Annals of Statistics 11(4) (1983),
1156-1174.
DOI: http://dx.doi.org/10.1214/aos/1176346329
[6] P. Hall, Asymptotic Theory of Minimum Integrated Square Error for
Multivariate Density Estimation, In Multivariate Analysis-VI, Editor
P. R. Krishnaiah, Elsevier Science, Amsterdam (1985), 289-309.
[7] P. Hall and J. S. Marron, Local minima in cross-validation
functions, Journal of the Royal Statistical Society: Series B 53(1)
(1991), 245-252.
[8] W. Hardle, Smoothing Techniques: With Implementation in S,
Springer-Verlag, New York, 1991.
[9] M. C. Jones, J. S. Marron and S. J. Sheather, A brief survey of
bandwidth selection for density estimation, Journal of the American
Statistical Association 91(433) (1996), 401-407.
[10] M. C. Jones and R. F. Kappenman, On a class of kernel density
estimate bandwidth selectors, Scandinavian Journal of Statistics 19(4)
(1992), 337-349.
[11] Y. Kanazawa, Hellinger distance and Kullback-Leibler loss for the
kernel density estimator, Statistics and Probability Letters 18(4)
(1993), 315-321.
DOI: https://doi.org/10.1016/0167-7152(93)90022-B
[12] C. R. Loader, Bandwidth selection: Classical or plug-in, The
Annals of Statistics 27(2) (1999), 415-438.
DOI: http://dx.doi.org/10.1214/aos/1018031201
[13] J. S. Marron and M. P. Wand, Exact mean integrated squared error,
The Annals of Statistics 20(2) (1992), 712-736.
DOI: http://dx.doi.org/10.1214/aos/1176348653
[14] M. Minami and S. Eguchi, Robust blind source separation by
beta-divergence, Neural Computation 14(8) (2002), 1859-1886.
DOI: https://doi.org/10.1162/089976602760128045
[15] B. U. Park and B. S. Turlach, Practical performance of several
data driven bandwidth selectors, Computational Statistics 7 (1992),
251-270.
[16] E. Parzen, On estimation of a probability density function and
mode, Annals of Mathematical Statistics 33(3) (1962), 1065-1076.
[17] M. Rudemo, Empirical choice of histograms and kernel density
estimators, Scandinavia Journal of Statistics 9(2) (1982), 65-78.
[18] W. D. Scott, Multivariate Density Estimation: Theory, Practice,
and Visualization, Wiley, New York, 1992.
[19] D. W. Scott and G. R. Terrell, Biased and unbiased
cross-validation in density estimation, Journal of the American
Statistical Association 82(400) (1987), 1131-1146.
[20] S. J. Sheather and M. C. Jones, A reliable data-based bandwidth
selection method for kernel density estimation, Journal of the Royal
Statistical Society: Series B 53(3) (1991), 683-690.
[21] B. W. Silverman, Density Estimation for Statistics and Data
Analysis, Chapman and Hall, London, 1986.
[22] C. J. Stone, An asymptotically optimal window selection rule for
kernel density estimates, The Annals of Statistics 12(4) (1984),
1285-1297.
DOI: http://dx.doi.org/10.1214/aos/1176346792
[23] W. N. Venables and B. D. Ripley, Modern Applied Statistics with
S, 4th Edition, Springer, New York, 2002.
[24] M. P. Wand and M. C. Jones, Kernel Smoothing, Chapman and Hall,
London, UK, 1995.
[25] S. Weisberg, Applied Linear Regression, Wiley, New York, 1980.
[26] J. Zhang, Generalized least squares cross-validation in kernel
density estimation, Statistica Neerlandica 69(3) (2015), 315-328.
DOI: https://doi.org/10.1111/stan.12061
