Reference: | 1. Boor, C. D. (1972) “On calculating with B-splines.”, Journal of Approximation theorey, 6, 50-62. 2. Cressie, N. A. C. and Read, T. R. C. (1984) “Multinomial goodness-of-fit tests.”, J. Roy. Statist. Soc. Ser. B, 46, 440-464 3. Du, P., Parmeter, C. F. and Racine, J. S. (2013) “Nonparametricc kernel regression with multiple predictors and multiple shape constraints.”, Statistica Sinica, 23, 1347-1371. 4. Fan, J. (1992) “Design-adaptive nonparametric regression.”, J. Amer. Statist. Assoc., 87, 998-1004. 5. Gasser, T. and Müller, H.-G. (1979) “Kernel estimation of regression functions.”, In Smoothing Techniques for Curve Estimation, 23(68), Springer-Verlag, New York. 6. Hall, P. and Haung, L.-S. (2001) “Noparametric kernel regresson subject to monotonicity constraints.”, Ann. Statist, 29(3), 624-647. 7. He, X., and Shi, P.(1998) “Monotone B-spline smoothing.”, J. Amer. Statist. Assoc., 93(442), 643-650. 8. Mammen, E. and Thomas-Agnan, C. (1998) “Smoothing splines and shape restrictions.”, Scandinavian Journal of Statistics, 26, 239-252. 9. Nadaraya, E. A. (1965) “On nonparametric estimates of density functions and regression curves”, Theory Probab. Appl., 10, 186-190. 10. Priestley, M. B. and Chao, M. T. (1972) “Nonparametric function fitting.”, J. Roy. Statist. Soc. Ser. B, 34, 385-392 11. Racine, J. and Li, Q. (2004) “Nonparametric estimation of regression functions with both categorical and continuous data.”, J. Econometrics, 119, 99-130. 12. Schumaker, L. L. (1981) Spline functions, Wiley, New York. 13. Stone, M. (1974) “Cross-validatory choice and assessment of statistical predictions.”, Roy. Statist. Soc. Ser. B, 36(2), 111-147 14. Stout, F. (2008) “Unimodal regression via prefix isotonic regression.”, Computational Statistics and Data Analysis, 53, 289-297. 15. Watson, G. S. (1964) “Smooth regression analysis.”, Sankhya ̅, 26(15), 175-184. |