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Filtering by: Advisor Schalock, H. D. Degree Field Statistics Non-Academic Affiliation Oregon State University. Graduate School

Nonparametric estimation of distributions using orthogonal expansions

Creator:

Crain, Bradford Robert

Abstract:

Methods of approximation and estimation of the density and the cumulative distribution function of a distribution over a finite interval are investigated. Goodness of the methods is measured pointwise and in terms of mean integrated squared error (MISE). If the density obeys certain regulatory conditions, i. e. , continuous, positive,...

Resource Type:

Dissertation

Full Text:

Calvin and Dr. Fred Ramsey. Without the help of Dr. H. D. Brunk this thesis could not have been written
Bayesian regression with autoregressive priors

Creator:

Haff, Leonard Ray

Resource Type:

Dissertation

Full Text:

approved: Let rYI = Redacted for Privacy Dr. H. D. Brunk y '37? )' be a multivariate normal random1' 2
Multivariate density estimation : a Bayesian approach

Creator:

Sexton, Gary Joe

Resource Type:

Dissertation

Full Text:

= 1, 2, 3, ... , set 1/ v /2 (n)H (v) e h (v) where h(n)(v) d H (v) is Nin! dv" n 20 called the
Density estimation using orthogonal series : a Bayesian approach

Creator:

Pointer, Patrick C.

Resource Type:

Dissertation

Full Text:

USING ORTHOGONAL SERIES -A BAYESIAN APPROACH Redacted for Privacy Abstract approved: Dr. H. D. Brunk
Nonparametric estimation of univariate and mixing densities : a Bayesian Least Squares approach

Creator:

Shih, John I.

Resource Type:

Dissertation

Full Text:

Dr. H. D. Brunk Assume that Y 1 ,Y 2 n are i.i.d. observations from a distribu- tion with a
A prior distribution for smooth regression

Creator:

Deaton, Leonard William

Resource Type:

Dissertation

Full Text:

DISTRIBUTION FOR SMOOTH REGRESSION Abstract approved: H. D. Brunk Let the random vector have an Nvariate