کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
418147 681615 2007 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of resampling and linear spline methods to spectral and dispersional analyses of long-memory processes
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Application of resampling and linear spline methods to spectral and dispersional analyses of long-memory processes
چکیده انگلیسی

Most methods for estimating the Hurst exponent H   of long-memory processes (R/SR/S analysis, correlogram plot, dispersional analysis, periodogram plot) are based on linear regression. All of these methods share a common drawback: the regression line should be fitted on an unknown part of the data. A general remedy to this problem is proposed, consisting in fitting a linear spline with an unknown breakpoint, instead of the usual regression line. From another side, in the case of partition-based methods such as dispersional analysis, the number of points available for regression is generally small. It is shown that this number can be increased by drawing well-suited random sub-series. The proposed improvements are first tested on simulated fractional Gaussian noises. Then, two datasets are processed: the Nile series and the North Atlantic Oscillation annual index series. The former is long enough (663 values) for investigating its long memory, and we found estimates of H   close to those already published. The latter is much shorter (141 values). As a whole, it seems persistent, with a Hurst coefficient stronger than estimations (about 0.640.64) found in the literature. However, it is shown that this phenomenon could be non-stationary, with H   switching from 0.50.5 to 0.80.8 in the thirties.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 51, Issue 9, 15 May 2007, Pages 4308–4323
نویسندگان
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