Karl Pearson was Right,

reportActive / Technical Report | Accession Number: ADA104861 | Open PDF

Abstract:

A discussion is made of nonparametric versus parametric methods for the estimation of probability densities. A new algorithm for nonparametric density estimation is given and its performance compared with state-of-the-art kernel estimation algorithms. Author

Author(s):
Scott, David W. ; Tapia, Richard A. ; Thompson, James R.
Author Organization(s):
RICE UNIV HOUSTON TX
Supplementary Note:
Proceedings of Computer Science and Statistics: Symposium on the Interface (10th), Gaithersburg, MD, 14-15 Apr 77. Prepared in cooperation with Baylor Coll. of Medicine, Houston, TX.
Pagination:
0005

Security Markings

DOCUMENT & CONTEXTUAL SUMMARY

Distribution:
Approved For Public Release

RECORD

Collection: TR
Identifying Numbers
Contract/Grant Number(s):
E(40-1)-5046, PHS-NIH-17269
 Subject Terms
Communities of Interest:
No COI(s) Identified
Descriptor(s):
*PARAMETRIC ANALYSIS, *PROBABILITY DENSITY FUNCTIONS, *NONPARAMETRIC STATISTICS, ALGORITHMS, STATE OF THE ART, PERFORMANCE(ENGINEERING), RANDOM VARIABLES, STATISTICAL SAMPLES, ESTIMATES, KERNEL FUNCTIONS, BAYES THEOREM, NUMERICAL METHODS AND PROCEDURES, ASYMPTOTIC NORMALITY, THEOREMS
Field(s)/Group(s):
Statistics and Probability
Keyword(s):
Maximum likelihood estimation, Pearson family, WUNR042283
Report Date:
1978 Feb 01