It takes the average reader 10 hours and 10 minutes to read Large Sample Inference For Long Memory Processes by Donatas Surgailis
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Box and Jenkins (1970) made the idea of obtaining a stationary time series by differencing the given, possibly nonstationary, time series popular. Numerous time series in economics are found to have this property. Subsequently, Granger and Joyeux (1980) and Hosking (1981) found examples of time series whose fractional difference becomes a short memory process, in particular, a white noise, while the initial series has unbounded spectral density at the origin, i.e. exhibits long memory.Further examples of data following long memory were found in hydrology and in network traffic data while in finance the phenomenon of strong dependence was established by dramatic empirical success of long memory processes in modeling the volatility of the asset prices and power transforms of stock market returns.At present there is a need for a text from where an interested reader can methodically learn about some basic asymptotic theory and techniques found useful in the analysis of statistical inference procedures for long memory processes. This text makes an attempt in this direction. The authors provide in a concise style a text at the graduate level summarizing theoretical developments both for short and long memory processes and their applications to statistics. The book also contains some real data applications and mentions some unsolved inference problems for interested researchers in the field./a
Large Sample Inference For Long Memory Processes by Donatas Surgailis is 596 pages long, and a total of 152,576 words.
This makes it 201% the length of the average book. It also has 186% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 13 hours and 53 minutes to read Large Sample Inference For Long Memory Processes aloud.
Large Sample Inference For Long Memory Processes is suitable for students ages 12 and up.
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