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Testing for Randomness Against Autocorrelation: Alternative Tests
针对自相关备择的随机性检验：替代方法
The asymptotic efficiencies of the rank serial correlation and turning–points tests with respect to the serial correlation test are evaluated for first–order autocorrelation. Tests based on the periodogram of the data are suggested. A Monte Carlo study is conducted to evaluate the relative performance of likelihood derivative, distribution–free, and periodogram tests for small to moderate sample sizes with normal and nonnormal parent distributions. The serial correlation…
Testing for randomness against autocorrelation: Alternative tests
针对自相关的随机性检验：替代方法
The asymptotic efficiencies of the rank serial correlation and turning–points tests with respect to the serial correlation test are evaluated for first–order autocorrelation. Tests based on the periodogram of the data are suggested. A Monte Carlo study is conducted to evaluate the relative performance of likelihood derivative, distribution–free, and periodogram tests for small to moderate sample sizes with normal and nonnormal parent distributions. The serial correlation…
Testing for Randomness Against Autocorrelated Alternatives: The Parametric Case
Testing for randomness against autocorrelated alternatives: The parametric case
针对自相关备择的随机性检验：参数情形
Testing for randomness is studied for an alternative model of multivariate normality with the autocorrelation specified by a single parameter. First-order autoregression and first-order moving averages are special cases of this model. The likelihood derivative method is used to obtain tests. The asymptotic efficiencies are derived of these tests compared with tests based on the first-order sample serial correlation.