Introduction to Kernel Density Estimation.

Abstract

Kernel density estimators are one technique for producing nonparametric estimates of a sample's underlying probability density function. Although there are numerous kernel functions, the reader is only introduced to the Gaussian, truncated Gaussian, mode centering Lognormal and median centering Lognormal kernels. These kernels are applied to two samples from the Fire Support Team (FIST) Force Development Testing and Experimentation II conducted by the US Army Field Artillery Board (Fort Sill, OK), at Fort Riley, KS, during April and May 1984. Analysis of the performance of the Gaussian and truncated Gaussian kernels is achieved by applying a recursive formula, developed by Richard A. Tapia and James R. Thompson, optimizing the kernel function's smoothing parameter for the FIST FDT&E II samples and for Monte Carlo simulations. Keywords: Kernel functions. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1985
Accession Number
ADA163920

Entities

People

  • Wendy A. Winner

Organizations

  • Ballistic Research Laboratory

Tags

Communities of Interest

  • Cyber
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artillery
  • Commerce
  • Computer Programming
  • Data Science
  • Digital Data
  • Distribution Functions
  • Estimators
  • Information Science
  • Instructions
  • Kernel Functions
  • Monte Carlo Method
  • Normal Distribution
  • Probability
  • Probability Density Functions
  • Simulations
  • Specifications
  • Statistics

Readers

  • Computer Vision.
  • Military History of the United States in the 20th Century.
  • Regression Analysis.