# Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting

@article{Izbicki2015NonparametricCD, title={Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting}, author={Rafael Izbicki and Ann B. Lee}, journal={Journal of Computational and Graphical Statistics}, year={2015}, volume={25}, pages={1297 - 1316} }

In some applications (e.g., in cosmology and economics), the regression is not adequate to represent the association between a predictor x and a response Z because of multi-modality and asymmetry of f(z|x); using the full density instead of a single-point estimate can then lead to less bias in subsequent analysis. As of now, there are no effective ways of estimating f(z|x) when x represents high-dimensional, complex data. In this article, we propose a new nonparametric estimator of f(z|x) that…

## 33 Citations

Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation

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Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict…

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