| Reference: | [1] Bowman,A.W.(1984). An alternative method of cross-validation for the smoothing of density estimates. Biometrika, 71, 353-360.
[2] C.D.Boor.(1978). A partical guide to splines. Springer New York.
[3] D.Ruppert.(2002). Selecting the number of knots for penalized splines. Journal of Computational and Graphical Statistics, 11(4), 735-757
[4] E.Halpern.(1973). Bayesian spline regression when the number of knots is unknown. Journal of the Royal Statistical Society, B, 35, 347-360.
[5] E.Parzen.(1962). On estimation of a probability density function and mode. Ann. Math. Statist. , 33(3), 1065-1076
[6] Hongmei Kang, Falai Chen, Yusheng Li, Jiansong Deng, and Zhouwang Yang. (2015). Knot calculation for spline fitting via sparse optimization. Computer-Aided Design, 58, 179–188.
[7] I.J.Schoenbreg.(1983). Contributions to the problem of approximation of equidistant data by analytic functions. Quart.~Appl.~Math., 112-144
[8] J.S.Horne and E.O.Garton.(2006). Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis. The Journal of Wildlife Management, 70, 641–648.
[9] L.Piegl and W.Tiller.(1996). The NURBS Book. Springer, 81-116
[10] M.Stone.(1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society , 36(2), 111-147.
[11] M.P.Wand and M.C.Jones.(1995). Kernel Smoothing. Chapman and Hall.
[12] Nicolas Molinari, Jean-François Durand, and Robert Sabatier.(2004). Bounded optimal knots for regression splines. Computational statistics and data analysis, 45(2), 159–178.
[13] Paul, H.E. and Brian, D.M.(1996). Flexible smoothing with b-splines and penalties. Statistical science, 89–102.
[14] Peter Hall and Huang,Li-Shan.(2001). Nonparametric kernel regression subject to monotonicity constraints. Ann. Statist, 29(3), 624-647.
[15] Randall,L.E.(1988). Spline smoothing and nonparametric regression. Marcel Dekker.
[16] Seymour, Geisser.(1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320-328.
[17] Silverman,B.W.(1986). Density estimation for statistics and data analysis.
Chapman and Hall, London, United Kingdom. |
