A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples

@article{Liu2013AHT,
  title={A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples},
  author={Shen Liu and Elizabeth Ann Maharaj},
  journal={Comput. Stat. Data Anal.},
  year={2013},
  volume={60},
  pages={32-49}
}
  • Shen Liu, Elizabeth Ann Maharaj
  • Published 2013
  • Mathematics, Engineering, Computer Science
  • Comput. Stat. Data Anal.
  • A new test of hypothesis for classifying stationary time series based on the bias-adjusted estimators of the fitted autoregressive model is proposed. It is shown theoretically that the proposed test has desirable properties. Simulation results show that when time series are short, the size and power estimates of the proposed test are reasonably good, and thus this test is reliable in discriminating between short-length time series. As the length of the time series increases, the performance of… CONTINUE READING
    9 Citations

    Figures, Tables, and Topics from this paper

    Polarization of forecast densities: A new approach to time series classification
    • 14
    • PDF
    Clustering time series based on dependence structure
    • 5
    • PDF
    A random-projection based test of Gaussianity for stationary processes
    • 7
    • PDF
    Dynamic classification using multivariate locally stationary wavelet processes
    • 6
    • Highly Influenced
    • PDF
    Visualisation and statistical modelling techniques for the management of inventory stock levels
    • PDF
    Big data from mobile devices
    • 1

    References

    SHOWING 1-10 OF 58 REFERENCES
    Bootstrap prediction intervals for autoregression using asymptotically mean-unbiased estimators
    • 31
    Bias reduction in autoregressive models
    • 26
    • Highly Influential
    • PDF
    The Bias of Autoregressive Coefficient Estimators
    • 228
    Clustering heteroskedastic time series by model-based procedures
    • E. Otranto
    • Mathematics, Computer Science
    • Comput. Stat. Data Anal.
    • 2008
    • 68
    • Highly Influential
    • PDF
    Non-linear time series clustering based on non-parametric forecast densities
    • 34
    A SIGNIFICANCE TEST FOR CLASSIFYING ARMA MODELS
    • 76
    • PDF
    A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS
    • 209
    • Highly Influential