A New Method for Estimating Smooth Regression Functions

Eunji Lim

A New Method for Estimating Smooth Regression Functions

Eunji Lim * §

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§ Kean University

A New Method for Estimating Smooth Regression Functions

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Abstract

We propose a new method for estimating a regression function from noisy data when the underlying function is known to satisfy a certain smoothness condition. The proposed method fits a function to the data set so that the roughness of the fitted function is minimized while ensuring that the sum of the absolute deviations of the fitted function from the data points does not exceed a certain limit. It is shown that the fitted function exists and can be computed by solving a quadratic program. Numerical results demonstrate that the proposed method generates more efficient estimates than its alternative in terms of the mean square error and the amount of time required to compute the fit.

References

7 Cites in Article

Reference Format

1. Marguerite Frank,Philip Wolfe (1956). An algorithm for quadratic programming. Naval Research Logistics Quarterly, 3(1-2), 95-110.
2. M Grant,S Boyd (2014). CVX: Matlab software for disciplined convex programming. CVX: Matlab software for disciplined convex programming
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4. Eunji Lim,Mina Attallah (2016). Estimation of Smooth Functions via Convex Programs. International Journal of Statistics and Probability, 5(4), 150.
5. C Reinsch (1967). Smoothing by spline functions. Numer. Math, 10, 177-183.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Eunji Lim. 2016. "A New Method for Estimating Smooth Regression Functions". Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 16 (GJSFR Volume 16 Issue F5).

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GJSFR Volume 16 Issue F5

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

Not Found

Classification

GJSFR-F Classification MSC 2010: 62J05

Submission ReceivedAugust 3, 2016
Peer Review Double Blind
Handling Editor
Accepted August 28, 2016
Published September 9, 2016

Version of record

v1.2

Issue date

September 16, 2016

Language

English

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Article Matrices

Total Score: 132

Country: United States

Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision

Authors: Eunji Lim, Annerys Matos (PhD/Dr. count: 0)

View Count (all-time): 346

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Total Downloads (simulated): 84

Publish Date: 2016 09, Fri

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