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Functional Gaussian Approximation for Dependent Structures (Hardcover)
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Functional Gaussian Approximation for Dependent Structures (Hardcover)
Series: Oxford Studies in Probability
Expected to ship within 12 - 17 working days
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Functional Gaussian Approximation for Dependent Structures develops
and analyses mathematical models for phenomena that evolve in time
and influence each another. It provides a better understanding of
the structure and asymptotic behaviour of stochastic processes. Two
approaches are taken. Firstly, the authors present tools for
dealing with the dependent structures used to obtain normal
approximations. Secondly, they apply normal approximations to
various examples. The main tools consist of inequalities for
dependent sequences of random variables, leading to limit theorems,
including the functional central limit theorem and functional
moderate deviation principle. The results point out large classes
of dependent random variables which satisfy invariance principles,
making possible the statistical study of data coming from
stochastic processes both with short and long memory. The
dependence structures considered throughout the book include the
traditional mixing structures, martingale-like structures, and
weakly negatively dependent structures, which link the notion of
mixing to the notions of association and negative dependence.
Several applications are carefully selected to exhibit the
importance of the theoretical results. They include random walks in
random scenery and determinantal processes. In addition, due to
their importance in analysing new data in economics, linear
processes with dependent innovations will also be considered and
analysed.
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