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INTRODUCTORY THEORY 1 Introduction
Differentiation and Integration of Stochastic Processes
Some Special Models
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absolutely continuous approximation assume asymptotically normal autoregressive Chapter coefficients column components computed condition consider continuous converges almost surely converges in mean converges in probability converges to zero corresponding course covariance function covariance matrix defined diagonal discuss eigenvalues elements estimate evidently example expression F(dX factor filter finite follows formula Fourier Fourier series frequency Gaussian Hermitian Hilbert space holomorphic independent interval least squares likelihood function linear process Mathematical Appendix mean correction mean square modulus moving average multivariate normal distribution nonnegative notation null observations obtain orthogonal increments points polynomial probability to zero problem procedure proof of Theorem random variables regression replace response function result satisfies scalar Section sequence spectral density spectrum square integrable stationary process statistic subspace Theorem 11 theory transformation uniformly unit circle unity variance vector process x(ri zero mean