Simple Local Polynomial Density Estimators
This paper introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automatic, but does not require pre-binning or any other transformation of the data. We study the main asymptotic properties of the estimator, and use these results to provide principled estimation, inference, and bandwidth selection methods. As a substantive application of our results, we develop a novel discontinuity in density testing procedure, an important problem in regression discontinuity designs and other program evaluation settings. An illustrative empirical application is given. Two companion Stata and R software packages are provided.
Code (2)
Tasks
regressionSimilar Papers 제목 키워드 기반
Kernel Choice Matters for Boundary Inference Using Local Polynomial Density: With Application to Manipulation Testing
The local polynomial density (LPD) estimator has been a useful tool for inference concerning boundary points of density functions. While it is commonly believed that kernel selection is not crucial for the performance of…
Common Sense ReasoningLocal Polynomial Estimation of Time-Varying Parameters in Nonlinear Models
We develop a novel asymptotic theory for local polynomial (quasi-) maximum-likelihood estimators of time-varying parameters in a broad class of nonlinear time series models. Under weak regularity conditions, we show the …
Time SeriesTime Series AnalysisMinmax Trend Filtering: Generalizations of Total Variation Denoising via a Local Minmax/Maxmin Formula
Total Variation Denoising (TVD) is a fundamental denoising and smoothing method. In this article, we identify a new local minmax/maxmin formula producing two estimators which sandwich the univariate TVD estimator at ever…
DenoisingregressionMedian of Forests for Robust Density Estimation
Robust density estimation refers to the consistent estimation of the density function even when the data is contaminated by outliers. We find that existing forest density estimation at a certain point is inherently resis…
Anomaly DetectionDensity EstimationEnsemble LearningEfficient Estimation in the Tails of Gaussian Copulas
We consider the question of efficient estimation in the tails of Gaussian copulas. Our special focus is estimating expectations over multi-dimensional constrained sets that have a small implied measure under the Gaussian…