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Testing for Periodicity and Trend in Long-Memory Processes

Almasri, Abdullah LU (2003) In Doctoral Theses in Statistics
Abstract (Swedish)
Popular Abstract in Swedish

Avhandlingen handlar om tes av periodicitet och trend inom långminne-processer. Med hjälp av diskret wavelet transform och periodogram presenteras vi två test för periodicitet och tre test för trend. Vi utvärderar vår test samt studerar deras egenskaper delvis genom att använda oss av datorintensiva metoder såsom Monte Carlo och delvis genom att jämföra dem med andra likartade kända metoder som Fisher’s test och Siegel’s test. Avhandlingen avslutas med praktiska tillämpningar på verkliga data.
Abstract
This thesis presents methods of testing the periodicity and trend for the time series, which exhibit dependence over long periods of time. Many such processes can be modeled by a class of models called fractionally differenced processes. The first approach we consider to analyse such processes is by using the band periodogram, which divides the periodogram into different intervals or bands, matching the band-pass of the discrete wavelet transform (DWT). In other words, we have a number of high frequency bands and one low frequency band. We investigate the distribution of statistics based on these bands of the long memory processes at high frequency bands, and we show that these distributions are similar to those derived from white noise... (More)
This thesis presents methods of testing the periodicity and trend for the time series, which exhibit dependence over long periods of time. Many such processes can be modeled by a class of models called fractionally differenced processes. The first approach we consider to analyse such processes is by using the band periodogram, which divides the periodogram into different intervals or bands, matching the band-pass of the discrete wavelet transform (DWT). In other words, we have a number of high frequency bands and one low frequency band. We investigate the distribution of statistics based on these bands of the long memory processes at high frequency bands, and we show that these distributions are similar to those derived from white noise processes. The next approach for analysing long memory processes is by using the DWT. For long memory processes, the properties of the periodogram of the wavelet scales are similar to those of the band periodograms.



We propose two tests for testing the periodicity in the case of long memory processes. The two tests are based on the two methods mentioned above. We develop the Fisher test in the case of simple periodicity, and the Siegel test when we have multiple periodicities. We use simulations to study properties of the tests; in particular we provide results on size and power of the tests.



We also investigate the testing of trends in long memory processes. We suggest two test statistics. The first one is based on the quotient of the low frequency band in the periodogram and the high frequency bands. The second one is based on the ratio of low frequency band periodogram to the sum of other ordinates in the periodogram. We also use the DWT method for testing the trend. By the use of simulation we compare our tests with alternative tests.



We further investigate the impact of periodicity and trend on different methods of estimating the long memory parameter. We provide a simulation study that shows how the periodicity and trend may affect such methods.



We apply our methodology to several environmental time series. (Less)
Please use this url to cite or link to this publication:
author
opponent
  • Nordgaard, Anders
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Statistics, temperature data., sunspots data, wind speed data, varve data, Siegel’s test, Fisher’s test, periodogram, Fractional difference process, Discrete wavelet transform, operations research, programming, actuarial mathematics, Statistik, operationsanalys, programmering, aktuariematematik
in
Doctoral Theses in Statistics
pages
143 pages
publisher
Department of Statistics, Lund university
defense location
Sal 1048, Alfa 1
defense date
2003-06-05 13:15
ISSN
1651-7938
ISBN
91-631-3852-2
language
English
LU publication?
yes
id
2594a05e-324d-45db-86c1-319f0f48611e (old id 21293)
date added to LUP
2007-05-25 14:46:23
date last changed
2018-05-29 12:03:43
@phdthesis{2594a05e-324d-45db-86c1-319f0f48611e,
  abstract     = {This thesis presents methods of testing the periodicity and trend for the time series, which exhibit dependence over long periods of time. Many such processes can be modeled by a class of models called fractionally differenced processes. The first approach we consider to analyse such processes is by using the band periodogram, which divides the periodogram into different intervals or bands, matching the band-pass of the discrete wavelet transform (DWT). In other words, we have a number of high frequency bands and one low frequency band. We investigate the distribution of statistics based on these bands of the long memory processes at high frequency bands, and we show that these distributions are similar to those derived from white noise processes. The next approach for analysing long memory processes is by using the DWT. For long memory processes, the properties of the periodogram of the wavelet scales are similar to those of the band periodograms.<br/><br>
<br/><br>
We propose two tests for testing the periodicity in the case of long memory processes. The two tests are based on the two methods mentioned above. We develop the Fisher test in the case of simple periodicity, and the Siegel test when we have multiple periodicities. We use simulations to study properties of the tests; in particular we provide results on size and power of the tests.<br/><br>
<br/><br>
We also investigate the testing of trends in long memory processes. We suggest two test statistics. The first one is based on the quotient of the low frequency band in the periodogram and the high frequency bands. The second one is based on the ratio of low frequency band periodogram to the sum of other ordinates in the periodogram. We also use the DWT method for testing the trend. By the use of simulation we compare our tests with alternative tests.<br/><br>
<br/><br>
We further investigate the impact of periodicity and trend on different methods of estimating the long memory parameter. We provide a simulation study that shows how the periodicity and trend may affect such methods.<br/><br>
<br/><br>
We apply our methodology to several environmental time series.},
  author       = {Almasri, Abdullah},
  isbn         = {91-631-3852-2},
  issn         = {1651-7938},
  keyword      = {Statistics,temperature data.,sunspots data,wind speed data,varve data,Siegel’s test,Fisher’s test,periodogram,Fractional difference process,Discrete wavelet transform,operations research,programming,actuarial mathematics,Statistik,operationsanalys,programmering,aktuariematematik},
  language     = {eng},
  pages        = {143},
  publisher    = {Department of Statistics, Lund university},
  school       = {Lund University},
  series       = {Doctoral Theses in Statistics},
  title        = {Testing for Periodicity and Trend in Long-Memory Processes},
  year         = {2003},
}