This thesis focuses on estimation and inference for a large class of semiparametric estimands: the class of continuous functionals of regression functions. This class includes a number of estimands derived from causal inference problems, among then the average treatment effect for a binary treatment when treatment assignment is unconfounded and many of its generalizations for non-binary treatments and individualized treatment policies. Chapter 2, based on work with Stefan Wager, introduces the augmented minimax linear es- timator (AMLE), a general approach to the problem of estimating a continuous linear functional of a regression function. In this approach, we estimate the regression function, then subtract from a simple plug-in estimator of the functional a weighted combination of the estimated regression function’s residuals. For this, we use weights chosen to minimize the maximum of the mean squared error of the resulting estimator over regression functions in a chosen neighborhood of our estimated regression function. These weights are shown to be a universally consistent estimator our linear functional’s Riesz representer, the use of which would result in an exact bias correction for our plug- in estimator. While this convergence can be slow, especially when the Riesz representer is highly nonsmooth, the action of these weights on functions in the aforementioned neighborhood imitates that of the Riesz representer accurately even when they are slow to converge in other respects. As a result, we show that under no regularity conditions on the Riesz representer and minimal regularity conditions on the regression function, the proposed estimator is semiparametrically efficient. In simulation, it is shown to perform very well in the context of estimating the average partial effect in the conditional linear model, a simultaneous generalization of the average treatment effect to address continuous-valued treatments and of the partial linear model to address treatment effect heterogeneity. Chapter 3, based on work with Arian Maleki and José Zubizarreta, studies the minimax linear estimator, a simplified version of the AMLE in which the estimated regression function is taken to be zero, for a class of estimands generalizing the mean with outcomes missing at random. We show semiparametric efficiency under conditions that are only slightly stronger than those required for the AMLE. In addition, we bound the deviation of our estimator’s error from the averaged efficient influence function, characterizing the degree to which the first order asymptotic characterization of semiparametric efficiency is meaningful in finite samples. In simulation, this estimator is shown to perform well relative to alternatives in high-noise, small-sample settings with limited overlap between the covariate distribution of missing and nonmissing units, a setting that is challenging for approaches reliant on accurate estimation of either or both of the regression function and the propensity score. Chapter 4 discusses an approach to rounding linear estimators for the targeted average treatment effect into matching estimators. The targeted average treatment effect is a generalization of the average treatment effect and the average treatment effect on the treated units.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Abstract An improvement and more accuracy method for spectrum analysis has been achieved in this thesis. There are three major parts in this thesis: the signal parameter estimation, the optimization of spectrum analysis, and the supervision to electric machinery. All these parts suggest the improvement ways to theories and applications of signal process. Parameter estimation is the base of dynamic designs, controls, and supervisions. This thesis infers the complete method to estimate parameters. The method estimates signal parameters in frequency domain. In electric machinery analysis, the most signals can consist of complex exponents. The component parameters include frequency, damping, amplitude, and phase. Basing on the damping existed or not, signals can be classified into two parts: periodic and non-periodic. Each complex exponent component will produce its band on spectrum. This method references the scales with highest amplitudes to estimate exact parameters. In suitable conditions, these mathematical equations can be simplified substantially to save computing time. The developed technologies of spectrum analysis take FFT to deal with the time-frequency transform work extensively. However, the sample of discrete signal is at random, and FFT suffers specific restrictions. When FFT transforms signal into frequency domain, the signal will cause errors on spectrum inevitably. This thesis corrects the errors by the optimization method. When frequency scales can match with signal characteristics, the picket-fence effect and leakage effect that the signal caused on spectrum will decrease to minimum. This method consists of three new technologies: parameter estimation, selection for optimal scale parameters, and adjustable spectrum. The method not only displays signal parameters on spectrum exactly and clearly, but also keeps the ability of fast process. When analyzing the more complex signal, the result of optimization will be restricted. Under this condition, the method can focus on the partial components and analyze them, then the result will keep accurate. This thesis combines supervisory technologies via a signal measurement. The signal sampling of these technologies is more convenient and simple. The system monitors operating conditions and fault conditions of the electric machinery with sound signal analysis. This signal analysis not only keeps normal measurement in the place which other signals can¡¦t be detected, but also can expand the monitoring ability. In operation conditions, the system monitors the speed and the input power of electric machinery through sound signal analysis. In fault conditions, the system recognizes type of fault under variation loads successfully. The recognition system is established by artificial neural network. The improvement of recognition ability is also discussed in this thesis. The methods discussed in the thesis give powerful estimation method for the signal analysis accurately and practically.

published_or_final_version / Physics / Master / Master of Philosophy

Thesis (Ph. D.)--Virginia Polytechnic Institute, 1963. / Vita. Abstract. Includes bibliographical references (leaves 124-126). Also available via the Internet.

Thesis (Ph.D.)--University of Rhode Island, 2005. / Includes bibliographical references (leaves 90-95).

Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 115-117).

Current environmental and economic trends have forced grid operators to maximize the utilization of the existing assets, which is causing systems to be operated closer to their stability limits than ever before. This requires, among other things, better knowledge and modelling of the existing power system equipment to increase the accuracy of the assessment of current stability margins.This research investigates the possibility of improving the quality of load modeling. The thesis presents a review of the traditional methods for estimation of load model parameters and proposes to use Improved Particle Swarm Optimization. Different algorithms are tested and compared in terms of accuracy, reliability and CPU requirements using computer simulations and real-data captured in a power system.Estimation of frequency and power components has also been studied in this thesis. A review of the existing methods has been provided and the use of an Unscented Kalman Filter proposed. This nonlinear recursive algorithm has been thoroughly tested and compared against selected traditional techniques in a number of experiments involving computer-generated signals as well as measurements obtained in laboratory conditions.

The collection of biological data has been limited by instrumentation, the complexity of the systems themselves, and even the ability of graduate students to stay awake and record the data. However, increasing measurement capabilities and decreasing costs may soon enable the collection of reasonably sampled time course data characterizing biological systems, though in general only a subset of the system's species would be measured. This increase in data volume requires a corresponding increase in the use and interpretation of such data, specifically in the development of system identification techniques to identify parameter sets in proposed models. In this paper, we present the results of identifiability analysis on a small test system, including the identifiability of parameters with respect to different measurements (proteins and mRNA), and propose a working definition for "biologically meaningful estimation". We also analyze the correlations between parameters, and use this analysis to consider effective approaches to determining parameters with biological meaning. In addition, we look at other methods for determining relationships between parameters and their possible significance. Finally, we present potential biologically meaningful parameter groupings from the test system and present the results of our attempt to estimate the value of select groupings. / Master of Science

In previous studies, evidence of thermal wave behavior was found in heterogeneous materials. Thus, the overall goal of this study was to experimentally verify those results, and develop a parameter estimation scheme to estimate the thermal properties of various heterogeneous materials. Two types of experiments (Experiments 1 and 2) were conducted to verify the existence or non-existence of thermal wave behavior in heterogeneous materials. In Experiment 1 sand, ion exchanger, and sodium bicarbonate were used as test materials, while processed meat (bologna) was used in Experiment 2. The measured temperature profiles of the samples were compared with the parabolic and hyperbolic heat conduction model results. The values of thermal diffusivity and thermal conductivity were obtained using the Box-Kanemasu parameter estimation method which is based on the comparison between temperature measurements and the solutions of the theoretical model. Overall, no clear experimental evidence was found to justify the use of hyperbolic heat conduction models rather than parabolic for the materials tested. Further comprehensive experimentation using different heating rates is warranted to definitely identify the accurate type of heat conduction process associated with such materials, and to describe the physical mechanisms which produce wave-like heat conduction in heterogeneous materials. / Master of Science