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For a time series generated by polynomial trend with stationary long-memory errors, the ordinary least squares estimator (OLSE) of the trend coefficients is asymptotically normal, provided the error process is linear. The asymptotic distribution may no longer be normal, if the error is in the...
Persistent link: https://www.econbiz.de/10014065621
Demonstration that our noise filtering procedure is extremely robust on the basis of the following experiment. The noise filtering procedure was applied first to an empirical correlation matrix and, second, to the matrix built from the same time series deliberately contaminated with noise. The...
Persistent link: https://www.econbiz.de/10013060875
Demonstration that our noise filtering procedure is extremely robust on the basis of the following experiment. The noise filtering procedure was applied first to an empirical correlation matrix and, second, to the same matrix deliberately contaminated with noise. The final, noise filtered...
Persistent link: https://www.econbiz.de/10013060876
Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics) of Market Memory Trading, L.L.C., present in a series of nine (9) white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the...
Persistent link: https://www.econbiz.de/10013062120
Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics) of Market Memory Trading, L.L.C., present in a series of nine (9) white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the...
Persistent link: https://www.econbiz.de/10013062139
Data transformations are commonly used across statistics to transform data distributions into distributions with properties that make them more user friendly. In time-series, stationarity is one of the most common assumptions that is violated because the mean and variance are time dependent....
Persistent link: https://www.econbiz.de/10012913053
Data from the automatic monitoring of intensive care patients exhibits trends, outliers, and level changes as well as periods of relative constancy. All this is overlaid with a high level of noise and there are dependencies between the different items measured. Current monitoring systems tend to...
Persistent link: https://www.econbiz.de/10009775959