Conditional Density Estimation via Weighted Logistic Regressions

• URL: http://arxiv.org/abs/2010.10896v1
• Date: Wed, 21 Oct 2020 11:08:25 GMT
• Title: Conditional Density Estimation via Weighted Logistic Regressions
• Authors: Yiping Guo and Howard D. Bondell
• Abstract summary: We propose a novel parametric conditional density estimation method by showing the connection between the general density and the likelihood function of inhomogeneous process models. The maximum likelihood estimates can be obtained via weighted logistic regressions, and the computation can be significantly relaxed by combining a block-wise alternating scheme and local case-control sampling.
• Score: 0.30458514384586394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared to the conditional mean as a simple point estimator, the conditional density function is more informative to describe the distributions with multi-modality, asymmetry or heteroskedasticity. In this paper, we propose a novel parametric conditional density estimation method by showing the connection between the general density and the likelihood function of inhomogeneous Poisson process models. The maximum likelihood estimates can be obtained via weighted logistic regressions, and the computation can be significantly relaxed by combining a block-wise alternating maximization scheme and local case-control sampling. We also provide simulation studies for illustration.

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