Piecewise Convex Function Estimation and Model Selection
Methodology
2020-02-18 v1 Machine Learning
Signal Processing
Statistics Theory
Data Analysis, Statistics and Probability
Statistics Theory
Abstract
Given noisy data, function estimation is considered when the unknown function is known apriori to consist of a small number of regions where the function is either convex or concave. When the regions are known apriori, the estimate is reduced to a finite dimensional convex optimization in the dual space. When the number of regions is unknown, the model selection problem is to determine the number of convexity change points. We use a pilot estimator based on the expected number of false inflection points.
Cite
@article{arxiv.1803.03903,
title = {Piecewise Convex Function Estimation and Model Selection},
author = {Kurt S. Riedel},
journal= {arXiv preprint arXiv:1803.03903},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:1803.03901