Asymptotic Behavior of a Spline Estimate of a Density Function,

Abstract

A class of cubic spline estimators of probability density functions over a finite interval are considered in the paper. The precise asymptotic behavior of the bias and covariance of such estimators is obtained in the interior of the interval. The estimators are shown to be asymptotically normally distributed. The properties of these estimators are compared with those of kernel estimators. The kernel and spline estimators are compared in some Monte Carlo simulations as well as in the analysis of some data obtained in turbulent wind flow. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1974
Accession Number
AD0783540

Entities

People

  • Keh-shin Lii
  • Murray Rosenblatt

Organizations

  • University of California, San Diego

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computing-Related Activities
  • Covariance
  • Data Science
  • Estimators
  • Information Science
  • Interdisciplinary Science
  • Intervals
  • Mathematical Analysis
  • Mathematics
  • Monte Carlo Method
  • Optimal Estimators
  • Probability
  • Probability Density Functions
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Statistical inference.