论文标题
LPCDE：局部多项式条件密度估计器的估计和推断
lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators
论文作者
论文摘要
本文讨论了R软件包LPCDE，该软件包代表局部多项式的条件密度估计。它实现了基于内核的局部多项式平滑方法，在Cattaneo，Chandak，Jansson，MA（2024）中引入了条件分布，密度及其衍生物的统计估计和推断。该软件包提供了均值误差最佳带宽选择和相关点估计器，以及基于稳健的偏差校正的不确定性量化，无论是在评估点上均匀校正（例如置信区间）和均匀（例如置信频段）。每当数据得到紧凑时，实现的方法都是边界自适应。该软件包还实现了正则条件密度估计方法，以确保所得密度估计值是非负的，并将其整合到一个。我们将LPCDE的功能与现有的开源软件包进行对比，以进行有条件的密度估计，并使用模拟和真实数据集展示其主要功能。本文的缩写版本在马萨诸塞州詹森（Jansson）的Cattaneo（2025 Joss）发表。
This paper discusses the R package lpcde, which stands for local polynomial conditional density estimation. It implements the kernel-based local polynomial smoothing methods introduced in Cattaneo, Chandak, Jansson, Ma (2024) for statistical estimation and inference of conditional distributions, densities, and derivatives thereof. The package offers mean square error optimal bandwidth selection and associated point estimators, as well as uncertainty quantification based on robust bias correction both pointwise (e.g., confidence intervals) and uniformly (e.g., confidence bands) over evaluation points. The methods implemented are boundary adaptive whenever the data is compactly supported. The package also implements regularized conditional density estimation methods, ensuring the resulting density estimate is non-negative and integrates to one. We contrast the functionalities of lpcde with existing open-source packages for conditional density estimation, and showcase its main features using simulated and real datasets. An abbreviated version of this article is published in Cattaneo, Chandak, Jansson, Ma (2025 JOSS).
