Compositional Nonparametric Prediction: Statistical Efficiency and Greedy Regression Algorithm
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Compositional Nonparametric Prediction: Statistical Efficiency and Greedy Regression Algorithm
In this paper, we propose a compositional nonparametric method in which a model is expressed as a labeled binary tree of $2k+1$ nodes, where each node is either a summation, a multiplication, or the application of one of the $q$ basis functions to one of the $p$ covariates. We show that in order to recover a labeled binary tree from a given dataset, the sufficient number of samples is $O(klog(pq)+log(k!))$, and the necessary number of samples is $Omega(klog (pq)log(k!))$. We further propose a greedy algorithm for regression in order to validate our theoretical findings through synthetic experiments.
Compositional Nonparametric Prediction: Statistical Efficiency and Greedy Regression Algorithm
by Yixi Xu, Jean Honorio, Xiao Wang
https://arxiv.org/pdf/1704.01896v3.pdf
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