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The familiar Gaussian models do not allow for large deviations and
are thus often inadequate for modeling high variability.
Non-Gaussian stable models do not possess such limitations. They
all share a familiar feature which differentiates them from the
Gaussian ones. Their marginal distributions possess heavy
"probability tails", always with infinite variance and in some
cases with infinite first moment. The aim of this book is to make
this exciting material easily accessible to graduate students and
practitioners. Assuming only a first-year graduate course in
probability, it includes material which has appeared only recently
in journals and unpublished materials. Each chapter begins with a
brief overview and concludes with a range of exercises at varying
levels of difficulty. Proofs are spelled out in detail. The book
includes a discussion of self-similar processes, ARMA, and
fractional ARIMA time series with stable innovations.
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The High Notes
Danielle Steel
Paperback
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Discovery Miles 2 660
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