1 |
Bachrach LK, Hastie T, Wang MC, Narasimhan B, and Marcus R (1999). Bone mineral acquisition in healthy Asian, Hispanic, black, and Caucasian youth: a longitudinal study, The Journal of Clinical Endocrinology & Metabolism, 84, Oxford University Press, 4702-4712.
DOI
|
2 |
Chatterjee S and Hadi AS (2015). Regression Analysis By Example, John Wiley & Sons.
|
3 |
De Boor CR (1978). A Practical Guide to Splines, 27, Springer-Verlag, New York.
|
4 |
Efroymson MA (1960). Multiple regression analysis, Mathematical Methods for Digital Computers, John Wiley & Sons, 191-203.
|
5 |
Fan J, Gijbels I (1996). Local Polynomial Modelling and its Applications: Monographs on Statistics and Applied Probability 66, 66, CRC Press.
|
6 |
Garton N, Niemi J, and Carriquiry A (2020). Knot Selection in Sparse Gaussian Processes, arXiv preprint arXiv:2002.09538.
|
7 |
Green PJ and Silverman W (1993). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach(1st ed), CRC Press.
|
8 |
Jhong JH, Koo JY, and Lee SW (2017). Penalized B-spline estimator for regression functions using total variation penalty, Journal of Statistical Planning and Inference, 184, Elsevier, 77-93.
DOI
|
9 |
Marsh L and Cormier DR (2001). Spline regression models, 137, Sage.
|
10 |
Meyer MC (2012). Constrained penalized splines, Canadian Journal of Statistics, 40, Wiley Online Library, 190-206.
DOI
|
11 |
Meier L, Van de Geer S, and Buhlmann P (2009). High-dimensional additive modeling, The Annals of Statistics, 37, Institute of Mathematical Statistics, 3779-3821.
DOI
|
12 |
Osborne MR, Presnell B, and Turlach BA (1998). Knot selection for regression splines via the lasso, Computing Science and Statistics, 44-49.
|
13 |
Tibshirani R (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological), 58, Wiley Online Library, 267-288.
DOI
|
14 |
Loader C (2006). Local regression and likelihood, Springer Science & Business Media.
|
15 |
Leitenstorfer F and Tutz G (2007). Knot selection by boosting techniques, Computational Statistics & Data Analysis, 51, Elsevier, 4605-4621.
DOI
|
16 |
Wand M (2020). KernSmooth: Functions for Kernel Smoothing Supporting Wand & Jones 1995.
|
17 |
Mammen E, and Van de Geer S (1997). Locally adaptive regression splines, Annals of Statistics, 25, Institute of Mathematical Statistics, 387-413.
|
18 |
Nie Z and Racine JS (2012). The crs Package: Nonparametric Regression Splines for Continuous and Categorical Predictors, R Journal, 4.
|
19 |
Racine JS and Nie Z (2021). CRS: Categorical Regression Splines, 2021. R package version 0, 15-33.
|
20 |
Schwarz G (1978). Estimating the dimension of a model, Annals of Statistics, 6, Institute of Mathematical Statistics, 461-464.
DOI
|
21 |
Tibshirani R and Friedman J (2020). A pliable lasso, Journal of Computational and Graphical Statistics, 29, 215-225.
DOI
|
22 |
Tsybakov AB (2008). Introduction to Nonparametric Estimation, Springer, London.
|
23 |
Wright SJ (2015). Coordinate descent algorithms, Mathematical Programming, 151, 3-34.
DOI
|
24 |
Jhong JH and Koo JY (2019). Simultaneous estimation of quantile regression functions using B-splines and total variation penalty, Computational Statistics & Data Analysis, 133, Elsevier, 228-244.
DOI
|
25 |
Efromovich S (2008). Nonparametric Curve Estimation: Methods, Theory, and Applications, Springer Science & Business Media.
|
26 |
Yee TW (2015). Vector Generalized Linear and Additive Models: With An Implementation in R, Springer, New York.
|
27 |
Wahba G (1990). Spline Models for Observational Data, SIAM, Philadelphia.
|