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Fundamentals of estimation theory - L. Kubacek
Applying estimate theories makes the handling of the results of experiments both efficient and efficient and helps not only to specify our know-how, but also to define the degree of its dependability. Section 2 contains information chosen from algrams, operational analyses and the theories of likelihood, to ease the literacy of the text itself and to rescue readers seeking single propositions in various text books and paper, it is mainly aimed at the reproduction kernels Hilbert rooms, useful in resolving many estimating issues.
Appraisal: The forth section is an introductory guide to asymptotical valuation techniques. It examines the methodology of random moment statistics and the maximal similarity approach. In the fifth section, the straight-line and square calculation methodologies are discussed. Least square approach is discussed. It describes five fundamental standard revisions of the general purpose models and the uniform estimating models.
In detail, the equivalency of the least squares approach with the generalised minimum-standard inverse of the draft grid of the re-gression tool is investigated. Statistic characteristics of the numerical estimates for normally divided measuring error in the fifth section are given in section 6.
Section 7 discusses some other important methodologies of estimate theories. Sturdiness and Bahadur's approaches to estimating theories are presented in the third and forth parts of this last section.
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