Accession Number : ADA113018

Title :   Nonparametric Estimation of Signals Mixed with Noise.

Descriptive Note : Final technical rept. 1 Dec 80-30 Sep 81,

Corporate Author : TEXAS TECH UNIV LUBBOCK DEPT OF MATHEMATICS

Personal Author(s) : Chanda,Kamal C

PDF Url : ADA113018

Report Date : Feb 1982

Pagination or Media Count : 11

Abstract : Analysis of a set of evolutionary or nonstationary time series data is traditionally carried out by the use of regression and spectral methods. Although these procedures are, to some extent, nonparametric in nature, the basic assumption implicit in such data analysis has usually been that the time series is Gaussian or nearly so. We do not know very well how efficient and useful these procedures are in case the data structure is distinctly non-Gaussian. Classical data analysts who handle data sets composed of independent observations have noticed over the last three decades that robust and adaptive nonparametric methods have worked very efficiently even for situations where large implicit variability in the data has effectively ruled out a Gaussian model. (Author)

Descriptors :   *Time series analysis, *Nonparametric statistics, *Signal to noise ratio, Estimates, Signal processing, Chi square test, Kernel functions, Stochastic processes, White noise, Real numbers, Probability density functions, Hypotheses, Sampling, Variables, Data processing, Noise

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE