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This ebook constitutes the refereed lawsuits of the 14th overseas convention on wisdom Engineering and information administration, EKAW 2004, held in Whittleburg corridor, united kingdom in October 2004. The 30 revised complete papers and 21 revised brief papers have been rigorously reviewed and chosen from quite a few submissions.
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Int. Conf. Inf. Sci. , Piscataway, NJ: IEEE Press, 1976. 29. J. D. Johnson, A filter family designed for use in quadrature mirror filter banks, Proc. IEEE Int. Conf. , Piscataway, NJ: IEEE Press, 1980, pp. 291–294. 696 DATA-FLOW AND MULTITHREADED ARCHITECTURES 30. M. J. T. Smith and T. P. Barnwell III, A procedure for designing exact reconstruction filter banks for tree structured subband coders, Proc. IEEE Int. Conf. , Piscataway, NJ: IEEE Press, 1984. 31. A. Habibi, Comparison of nth-order DPCM encoder with linear transformation and block quantization techniques, IEEE Trans.
A. Croisier, D. Esteban, and C. Galand, Perfect channel splitting by use of interpolation/decimation techniques, Proc. Int. Conf. Inf. Sci. , Piscataway, NJ: IEEE Press, 1976. 29. J. D. Johnson, A filter family designed for use in quadrature mirror filter banks, Proc. IEEE Int. Conf. , Piscataway, NJ: IEEE Press, 1980, pp. 291–294. 696 DATA-FLOW AND MULTITHREADED ARCHITECTURES 30. M. J. T. Smith and T. P. Barnwell III, A procedure for designing exact reconstruction filter banks for tree structured subband coders, Proc.
Here the form of dependence on the parameter and the set ⌰ are assumed to be known. The problem of estimation of an unknown parameter or of the value g() of a function g at the point consists of constructing a function ˆ (X) from the observations, which gives a sufficiently good approximation of (or of g()). A commonly accepted approach to comparing estimators, resulting from A. Wald’s contributions, is as follows: consider a quadratic loss function q(ˆ (X) Ϫ ) (or, more generally, a nonnegative function w(ˆ (X), )), and given two estimators ˆ 1(X) and ˆ 1(X), the estimator for which the expected loss (risk) Eq(ˆ i(X) Ϫ ), i ϭ 1, 2 is the smallest is called the better, with respect to the quadratic loss function q (or to w).