lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators

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, den...

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Bibliographic Details
Published inarXiv.org
Main Authors Cattaneo, Matias D, Chandak, Rajita, Jansson, Michael, Ma, Xinwei
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.02.2024
Subjects
Confidence intervals
Density
Polynomials
Statistical analysis
Statistical inference
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Summary: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 R packages for conditional density estimation, and showcase its main features using simulated data.
ISSN:2331-8422