Multinomial estimation from censored samples Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/jd473067n

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Consider the estimation of the category proportions in a multinomial population from a sample which is "censored" in the sense that under an appropriate, unknown permutation of the sample categories, the population proportions are all known. We are considering the estimation of an ordered set of sample proportions, known except for their order. The estimation problem reduces to one of matching a set of known sample proportions with a set of known population proportions. The method of maximum likelihood yields the matching that common sense or one's intuition would suggest; highest sample proportion associated with highest population proportion, second highest sample proportion with second highest population proportion, and so forth. The work of this thesis is to examine, by a complete enumeration of cases for some simple problems, how good the method of maximum likelihood is. We study the effectiveness of maximum likelihood matching under variation of the three factors (1) "roughness" of the set of population proportions, (2) number of categories of the multinomial population, and (3) size of the sample. The effectiveness of maximum likelihood matching is measured by the ratio "proportion of the time maximum likelihood matching correct" divided by "proportion of the time random matching correct". The empirical study confirms the conclusions suggested by intuition that: (1) the greater the "roughness" of the set of population proportions, the more effective is the method of maximum likelihood; (2) the greater the number of categories the more effective is the method of maximum likelihood; and (3) the greater the sample size the more effective is the method of maximum likelihood.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Academic Affiliation
Non-Academic Affiliation
Subject
Rights Statement
Peer Reviewed
Language
Digitization Specifications
  • File scanned at 300 ppi using Capture Perfect 3.0 on a Canon DR-9050C in PDF format. CVista PdfCompressor 5.0 was used for pdf compression and textual OCR.
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Katy Davis(kdscannerosu@gmail.com) on 2014-05-14T22:10:31Z (GMT) No. of bitstreams: 1 SakarindrPreecha1965.pdf: 1107134 bytes, checksum: d75a2809b944c9e40d6a83178ca25e29 (MD5)
  • description.provenance : Submitted by Georgeann Booth (gbscannerosu@gmail.com) on 2014-05-10T00:00:43Z No. of bitstreams: 1 SakarindrPreecha1965.pdf: 1107134 bytes, checksum: d75a2809b944c9e40d6a83178ca25e29 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2014-05-13T14:45:45Z (GMT) No. of bitstreams: 1 SakarindrPreecha1965.pdf: 1107134 bytes, checksum: d75a2809b944c9e40d6a83178ca25e29 (MD5)
  • description.provenance : Made available in DSpace on 2014-05-14T22:10:31Z (GMT). No. of bitstreams: 1 SakarindrPreecha1965.pdf: 1107134 bytes, checksum: d75a2809b944c9e40d6a83178ca25e29 (MD5) Previous issue date: 1964-12-03

Relationships

In Administrative Set:
Last modified: 08/16/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items