Download Handbook of Statistics 10: Signal Processing and its by N. K. Bose, C. R. Rao PDF

By N. K. Bose, C. R. Rao

Hardbound. This quantity of the guide of facts provides a state-of-the artwork exposition of present subject matters in signal/image processing. It offers an outstanding stability among either concept and functions. the gathering of chapters offers with issues equivalent to quick computations and transforms in sign processing, sampling theorems, parameter estimation and sign modeling, snapshot and multidimensional sign processing, array processing, direction-of-arrival estimation, beamforming, adaptive algorithms, multiscale sign processing and wavelet transforms. different matters comprise VLSI implementations in undefined, photo collecting and video coding, spectrum estimation, neural internet sensor fusion, hidden Markov versions with purposes in speech acceptance, layout of designated forms of electronic filter out buildings (and leading edge tools for functionality review of such structures), in addition to extra normal structures that are encountered in sign processin

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Extra resources for Handbook of Statistics 10: Signal Processing and its Applications

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Taniguchi and Kakizawa (2000) present a wealth of modern asymptotic theory of inference for various time series models, including (linear) ARMA processes, long-memory processes, nonlinear time series, continuous-time processes, nonergodic processes, diffusion processes, and others. Brillinger (1981) and Priestley (1981) offer wide coverage as well as in-depth accounts of the spectral analysis of time series. Early monographs on nonlinear time series include Priestley (1988). Tong (1990) provides comprehensive coverage of parametric nonlinear time series analysis.

For other parametric models, we refer to Chapter 3 of Tong (1990). We always assume {εt } ∼ IID(0, σ 2 ) instead of WN(0, σ 2 ) when we introduce various nonlinear time series models in this section. Technically, this assumption may be weakened when we proceed with theoretical explorations later on. However, as indicated in a simple example below, a white noise process is no longer a pertinent building block for nonlinear models, as we have to look for measures beyond the second moments to characterize the nonlinear dependence structure.

Therefore, a purely nondeterministic stationary Gaussian process is always linear in the sense that it can be written as an MA(∞) process with normal white noise. A particularly simple case is a q-dependent stationary Gaussian process in the sense that Xt and Xt+k are independent for all k > q. 6). Therefore {Xt } ∼ MA(q). On the other hand, if, given {Xt−1 , · · · , Xt−p }, Xt is independent of {Xt−k , k > p}, it is easy to see that εt ≡ Xt − E(Xt |Xt−1 , · · · , Xt−p ) is independent of {Xt−k , k ≥ 1} since Cov(εt , Xt−k ) = 0 for k ≥ 1.

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