Determining the Number of Effective Parameters in Kernel Density Estimation

Nadine McCloud, Christopher F. Parmeter

Research output: Contribution to journalArticle

Abstract

The hat matrix maps the vector of response values in a regression to its predicted counterpart. The trace of this hat matrix is the workhorse for calculating the effective number of parameters in both parametric and nonparametric regression settings. Drawing on the regression literature, the standard kernel density estimate is transformed to mimic a regression estimate thus allowing extraction of a usable hat matrix for calculating the effective number of parameters of the kernel density estimate. Asymptotic expressions for the trace of this hat matrix are derived under standard regularity conditions for mixed, continuous, and discrete densities. Simulations validate the theoretical contributions. Several empirical examples demonstrate the usefulness of the method suggesting that calculating the effective number of parameters of a kernel density estimator maybe useful in interpreting differences across estimators.

Original languageEnglish (US)
Article number106843
JournalComputational Statistics and Data Analysis
Volume143
DOIs
StatePublished - Mar 2020

Fingerprint

Kernel Density Estimation
Kernel Density Estimate
Drawing (graphics)
Regression
Trace
Parametric Regression
Regression Estimate
Kernel Density Estimator
Nonparametric Regression
Regularity Conditions
Estimator
Demonstrate
Simulation
Standards

Keywords

  • Degrees of freedom
  • Hat matrix
  • Matrix trace
  • Nonparametric density estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Determining the Number of Effective Parameters in Kernel Density Estimation. / McCloud, Nadine; Parmeter, Christopher F.

In: Computational Statistics and Data Analysis, Vol. 143, 106843, 03.2020.

Research output: Contribution to journalArticle

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