Loading...
A Nonparametric Test for Deviation from Randomness
Strandberg, Alicia Graziosi
Strandberg, Alicia Graziosi
Citations
Altmetric:
Genre
Thesis/Dissertation
Date
2012
Advisor
Committee member
Group
Department
Statistics
Subject
Permanent link to this record
Collections
Research Projects
Organizational Units
Journal Issue
DOI
http://dx.doi.org/10.34944/dspace/2452
Abstract
There are many existing tests used to determine if a series consists of a random sample. Often these tests have restrictive distributional assumptions, size distortions, or low power for key useful alternative situations. The interest of this dissertation lies in developing an alternative nonparametric test to determine whether a series consists of a random sample. The proposed test detects deviations from randomness, without a priori distributional assumption, when observations are not independent and identically distributed (i.i.d.), which is suitable for our motivating stock market index data. Departures from i.i.d. are tested by subdividing data into subintervals and then using a conditional probability measure within intervals as a binomial test. This nonparametric test is designed to detect deviations of neighboring observations from randomness when the data set consists of time series observations. Simulation results confirm correct test size for varied distributions and good power for detecting alternative cases. This test is compared to a number of other popular methods and shown to be a competitive alternative. Although the proposed test may be applicable to multiple areas, this dissertation is mostly interested in applications to stock market and regression data. The proposed test is effectively illustrated with the common three stock market index data sets using a newly created transformation, and shown to perform exceptionally well.
Description
Citation
Citation to related work
Has part
ADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu