An empirical Bayes approach to variance function estimation /.

Bibliographic Details
Main Author: Hwang, Lie-Ju, 1957-
Other Authors: Cline, Daren B. H. (degree committee member.), Spielgelman, Clifford H. (degree committee member.), Zinn, Joel (degree committee member.)
Format: Thesis Book
Language:English
Published: 1990.
Subjects:
Online Access:Link to ProQuest copy
Link to OAKTrust copy
ProQuest, Abstract

MARC

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099 |a 1990  |a Dissertation  |a H9915 
100 1 |a Hwang, Lie-Ju,  |d 1957- 
245 1 3 |a An empirical Bayes approach to variance function estimation /. 
264 1 |c 1990. 
300 |a ix, 120 leaves :  |b illustrations ;  |c 29 cm 
336 |a text  |b txt  |2 rdacontent 
337 |a unmediated  |b n  |2 rdamedia 
338 |a volume  |b nc  |2 rdacarrier 
500 |a "Major subject: Statistics." 
500 |a Typescript (photocopy). 
500 |a Vita. 
502 |b Ph. D.  |c Texas A & M University  |d 1990 
504 |a Includes bibliographical references. 
520 3 |6 520-00  |a The problem concerns the analysis of assay data when there have been previous similar experiments. Assay data usually fall under the framework of nonlinear regression when the variability about the regression line is non-constant, i.e. heteroscedastic. The typical model for assay data contains parameters β for the mean function and parameters θ for the variance function. Of interest are quantities such as the parameters themselves as well as nonlinear functions of them, e.g. the minimum detectable concentration. There are three basic ways in which such data have been analyzed: (a) take the means of the estimates of β and θ from all assays and use this as the estimate for the current assay; (b) use only the current assay to estimate β and θ and (c) use an empirical Bayes estimate. A small error asymptotic theory, in which all three methods are analyzed in a unified way, has been constructed and the empirical Bayes estimate has a non-normal limit distribution... 
650 0 |a Bayesian statistical decision theory.  |0 http://id.loc.gov/authorities/subjects/sh85012506 
650 0 |a Estimation theory.  |0 http://id.loc.gov/authorities/subjects/sh85044957 
650 0 |a Regression analysis.  |0 http://id.loc.gov/authorities/subjects/sh85112392 
650 4 |a Major statistics. 
655 7 |a Academic theses.  |2 lcgft  |0 http://id.loc.gov/authorities/genreForms/gf2014026039 
700 1 |a Carroll, Raymond J.,  |e degree supervisor.  |0 http://id.loc.gov/authorities/names/n82073617 
700 1 |a Cline, Daren B. H.,  |e degree committee member.  |0 http://id.loc.gov/authorities/names/no2018132627 
700 1 |a Spielgelman, Clifford H.,  |e degree committee member. 
700 1 |a Zinn, Joel,  |e degree committee member. 
710 2 |a Texas A & M University,  |e degree granting institution.  |0 http://id.loc.gov/authorities/names/n80125885 
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