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Quantiles, parametric-select density estimation, and bi-information parameter estimatorsA quantile-based approach to statistical analysis and probability modeling of data is presented which formulates statistical inference problems as functional inference problems in which the parameters to be estimated are density functions. Density estimators can be non-parametric (computed independently of model identified) or parametric-select (approximated by finite parametric models that can provide standard models whose fit can be tested). Exponential models and autoregressive models are approximating densities which can be justified as maximum entropy for respectively the entropy of a probability density and the entropy of a quantile density. Applications of these ideas are outlined to the problems of modeling: (1) univariate data; (2) bivariate data and tests for independence; and (3) two samples and likelihood ratios. It is proposed that bi-information estimation of a density function can be developed by analogy to the problem of identification of regression models.
Document ID
19830007508
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Parzen, E.
(Texas A&M Univ. College Station, TX, United States)
Date Acquired
August 11, 2013
Publication Date
January 1, 1982
Publication Information
Publication: Texas A and M Univ. Proc. of the NASA Workshop on Density Estimation and Function Smoothing
Subject Category
Earth Resources And Remote Sensing
Accession Number
83N15779
Funding Number(s)
CONTRACT_GRANT: DAAG29-80-C-0070
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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