Graduate Thesis Or Dissertation
 

An atom approach to assessing the outlier properties of probability models

Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/qz20sw57b

Descriptions

Attribute NameValues
Creator
Abstract
  • This thesis is concerned with the problem of developing a method to categorize probability models by their outlier properties. There have been two such categorization methods proposed in the literature. Neyman and Scott (1971) classify an entire family of distributions into the outlier properties outlier-prone completely (OPC) and outlier resistant. Green (1974) classifies particular distributions into relative outlier resistance and proneness (ROR and ROP) and absolute outlier resistance and proneness (AOR and AOP). Green has pointed out that the Neyman and Scott approach allows no finite member families to be OPC. We have found that Green's approach does not allow discrete distributions to be AOR. We have extended Green's results to show outlier relationships for functions of random variables, functions of densities and functions of distribution functions. We have more clearly defined Green's six possible classes of distributions by their outlier properties. We have looked at the outlier properties of mixtures of distributions. We have presented a categorized listing of the common probability distributions by classes and provide an extension of Green's outlier characteristic property for mutivariate distributions whose densities are a function of the quadratic form X'1-'x. In response to the problem that no discrete distribution can be AOR, we have developed an atom approach to assessing the outlier properties of distributions. We have defined low, medium and high tendency for outlier distributions and have provided classification schemes for determining the outlier tendencies for a particular distribution of interest. A listing of common distributions that have low, medium and high tendency for outliers has also been provided.
Resource Type
Date Available
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Academic Affiliation
Non-Academic Affiliation
Subject
Rights Statement
Publisher
Peer Reviewed
Language
Digitization Specifications
  • File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0.82 on a Canon DR-9080C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
Replaces

Relationships

Parents:

This work has no parents.

In Collection:

Items

Thumbnail Title Date Uploaded Visibility Actions
file details HanlenRichardC1984.pdf 2017-08-16 Public Download