Browsing by Subject "Estimation theory -- Asymptotic theory"
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Item Changepoint detection and estimation using nonparametric procedures(Texas Tech University, 1989-05) Balakumar, SivanandanNot availableItem Estimating linear functionals of indirectly observed input functionals(Texas Tech University, 2004-08) Lee, Eun-JooWe consider the usual estimator of a linear functional of the unknown input function in indirect nonparametric regression models. The unknown regression function which is the parameter of interest, is infinite dimensional. Since the output is an integral transform of the input, this transformation must be inverted to recover the input. Because such an inversion is, in general, unbounded, regularization of the inverse will be required. Since a function in a separable Hilbert space has a Fourier expansion in an orthonormal basis the Fourier coefficients will be estimated in order to recover the input. Regularization of the inverse boils down to tapering the expansion with the estimated Fourier coefficients, which would otherwise not converge. In any case this shows that estimating Fourier coefficients and linear functionals in general is an important issue. It is surprising to see that the traditional estimator of the Fourier coefficients is not asymptotically efficient according to the Hajek-LeCam convoluteion theorem. Since this estimator, however, is -y/n-consistent, it can be improved in an asymptotic sense. A simulation study is included to establish the practical effect of this asymptotic result. In this dissertation, the theory is presented in a self-contained manner. This means that a complete derivation of theorems like Hajek's convolution theorem and the theorem on possible improvement of n-consistent estimators will be given.Item Uncertainties in pressure coefficients derived from full and model scale data / $c by Fei Long.(Texas Tech University, 2004-08) Long, FeiThe purpose of this study is to build the uncertainties associated with the wind tunnel testing and full-scale to wind tunnel pressure coefficient extrapolation by comparing the model and full-scale pressure coefficients. Two types of comparisons were made: one is the comparison between the two models CSU & SLM, and the other is the comparison between the models and full-scale (WERFL). For each case, both the observed statistics (i.e., mean, standard deviation, maximum, and minimum Cp) and estimated mean extreme Cp are analyzed. Two methods are used to obtain the estimated mean hourly extreme pressure coefficients. For the first case, the uncertainties according to repeatability, model-building, use of different wind tunnels, and estimation techniques for pressure coefficients statistics (i.e., both observed statistics and estimated mean extreme Cp) were estimated. For the second case, the pressure coefficients statistics (i.e., both observed statistics and estimated mean extreme Cp) of model and full-scale records achieved for a comparison. Individual tap time series as well as the area average and moving average time series were used to establish the uncertainties. The best-fit (Palisade Corporation, 1993) program is used to obtain best-fit probability density function and summary statistics for the error terms of all observed statistics comparison.