论文标题
通过加权逻辑回归估计有条件的密度估计
Conditional Density Estimation via Weighted Logistic Regressions
论文作者
论文摘要
与条件平均值作为简单的点估计器相比，条件密度函数具有多模式，不对称性或异性态性的分布更具信息性。在本文中，我们提出了一种新型的参数条件密度估计方法，通过显示不均匀泊松过程模型的一般密度和可能性函数之间的联系。可以通过加权逻辑回归来获得最大似然估计，并且可以通过结合块的交替最大化方案和局部病例对照采样来显着放松计算。我们还提供了示例研究。
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.
