Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/102994
Title: Cobs: Qualitatively constrained smoothing via linear programming
Authors: He, X. 
Ng, P.
Keywords: Constraint
Information criterion
Knot selection
Linear program
Nonparametric regression
Regression quantile
Smoothing spline
Issue Date: 1999
Citation: He, X.,Ng, P. (1999). Cobs: Qualitatively constrained smoothing via linear programming. Computational Statistics 14 (3) : 315-337. ScholarBank@NUS Repository.
Abstract: Popular smoothing techniques generally have a difficult time accommodating qualitative constraints like monotonicity, convexity or boundary conditions on the fitted function. In this paper, we attempt to bring the problem of constrained spline smoothing to the foreground and describe the details of a constrained B-spline smoothing (COBS) algorithm that is being made available to S-plus users. Recent work of He & Shi (1998) considered a special case and showed that the L1 projection of a smooth function into the space of B-splines provides a monotone smoother that is flexible, efficient and achieves the optimal rate of convergence. Several options and generalizations are included in COBS: it can handle small or large data sets either with user interaction or full automation. Three examples are provided to show how COBS works in a variety of real-world applications.
Source Title: Computational Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/102994
ISSN: 09434062
Appears in Collections:Staff Publications

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