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On Fan's adaptive Neyman tests for comparing two spectral densities
- Source :
- Journal of Statistical Computation and Simulation. 83:1585-1601
- Publication Year :
- 2013
- Publisher :
- Informa UK Limited, 2013.
-
Abstract
- In this paper, we consider tests for assessing whether two stationary and independent time series have the same spectral densities (or same autocovariance functions). Both frequency domain and time domain test statistics for this purpose are reviewed. The adaptive Neyman tests are then introduced and their performances are investigated. Our tests are adaptive, that is, they are constructed completely by the data and do not involve any unknown smoothing parameters. Simulation studies show that our proposed tests are at least comparable to the current tests in most cases. Furthermore, our tests are much more powerful in some cases, such as against the long orders of autoregressive moving average (ARMA) models such as seasonal ARMA series.
- Subjects :
- Statistics and Probability
Series (mathematics)
Applied Mathematics
Spectral density
Autocovariance
Modeling and Simulation
Frequency domain
Statistics
Applied mathematics
Autoregressive–moving-average model
Time domain
Statistics, Probability and Uncertainty
Smoothing
Mathematics
Statistical hypothesis testing
Subjects
Details
- ISSN :
- 15635163 and 00949655
- Volume :
- 83
- Database :
- OpenAIRE
- Journal :
- Journal of Statistical Computation and Simulation
- Accession number :
- edsair.doi...........63574959a8e36d5867c5307da286882c