OCDE: Odds Conditional Density Estimator

Conditional density estimation (CDE) models can be useful for many statistical applications, especially because the full conditional density is estimated instead of traditional regression point estimates, revealing more information about the uncertainty of the random variable of interest. In this pa...

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Bibliographic Details
Published inarXiv.org
Main Authors Alex Akira Okuno, Felipe Maia Polo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.06.2021
Subjects
Density
Random variables
Statistical analysis
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Summary:Conditional density estimation (CDE) models can be useful for many statistical applications, especially because the full conditional density is estimated instead of traditional regression point estimates, revealing more information about the uncertainty of the random variable of interest. In this paper, we propose a new methodology called Odds Conditional Density Estimator (OCDE) to estimate conditional densities in a supervised learning scheme. The main idea is that it is very difficult to estimate \(p_{x,y}\) and \(p_{x}\) in order to estimate the conditional density \(p_{y|x}\), but by introducing an instrumental distribution, we transform the CDE problem into a problem of odds estimation, or similarly, training a binary probabilistic classifier. We demonstrate how OCDE works using simulated data and then test its performance against other known state-of-the-art CDE methods in real data. Overall, OCDE is competitive compared with these methods in real datasets.
ISSN:2331-8422