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Breaking the Curse of Dimensionality

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  • Coppejans, Mark
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    Abstract

    This paper proposes a new nonparametric estimator for general regression functions with multiple regressors. The method used here is motivated by a remarkable result derived by Kolmogorov (1957) and later tightened by Lorentz (1966). In short, they show that any continuous function of multiple variables can be written as univariate functions. As it stands, this representation is difficult to estimate because of its lack of smoothness. Hence we propose to use a generalization of their representation that allows for the univariate functions to be differentiable. The model will be estimated using B-splines, which have excellent numerical properties. A crucial restriction in this representation is that some of the functions must be increasing. One of the main contributions of this paper is that we develop a method for imposing monotonicity on the cubic B-splines, a priori, such that the estimator is dense in the set of all monotonic cubic B-splines. A simulation experiment shows that the estimator works well when optimization is performed by using the back-fitting algorithm. The monotonic restriction has many other applications besides the one presented here, such as estimating a demand function. With only r + 2 more constraints, it is also possible to impose concavity.

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    Bibliographic Info

    Paper provided by Duke University, Department of Economics in its series Working Papers with number 00-13.

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    Date of creation: 2000
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    Handle: RePEc:duk:dukeec:00-13

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    Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
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    Fax: (919) 684-8974
    Web page: http://econ.duke.edu/

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    Cited by:
    1. David E. A. Giles & Robert Draeseke, 2001. "Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm," Econometrics Working Papers 0101, Department of Economics, University of Victoria.
    2. Mark Coppejans, Mico Mrkaic & Holger Sieg, 2000. "Experimentation And Learning In Rational Addiction Models With Multiple Addictive Goods," Computing in Economics and Finance 2000 81, Society for Computational Economics.

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