Pattern Recognition for Conditionally Independent Data
The Journal of Machine Learning Research
Universal Estimation of Information Measures for Analog Sources
Foundations and Trends in Communications and Information Theory
Divergence estimation for multidimensional densities via k-nearest-neighbor distances
IEEE Transactions on Information Theory
Kernel Regression in the Presence of Correlated Errors
The Journal of Machine Learning Research
| Hi-index | 754.90 |
Let X1, X2,... be an arbitrary random process taking values in a totally bounded subset of a separable metric space. Associated with Xi we observe Yi drawn from an unknown conditional distribution F(y|Xi=x) with continuous regression function m(x)=E[Y|X=x]. The problem of interest is to estimate Yn based on Xn and the data {(Xi, Yi)}i=1n-1. We construct appropriate data-dependent nearest neighbor and kernel estimators and show, with a very elementary proof, that these are consistent for every process X1, X2,.