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Two broad surveys of community- level water consumption and pricing behavior are used to answer questions about water demand in a more flexible and dynamic context than is provided in the literature. Central themes of price representation, aggregation, and dynamic adjustment tie together three econometric demand analyses. The centerpiece of each analysis is an exogenous weighted price representation. A model in first-differences is estimated by ordinary least squares using data from a personally-conducted survey of Texas urban water suppliers. Annual price elasticity is found to vary with weather and income, with a value of -0.127 at the data mean. The dynamic model becomes a periodic error correction model when the residuals of 12 static monthly models are inserted into the difference model. Distinct residential, commercial, and industrial variables and historical climatic conditions are added to the integrated model, using new national data. Quantity demanded is found to be periodically integrated with a common stochastic root. Because of this, the structural monthly models must be cointegrated to be consistent, which they appear to be. The error correction coefficient is estimated at -0.187. Demand is found to be seasonal and slow to adjust to shocks, with little or no adjustment in a single year and 90% adjustment taking a decade or more. Residential and commercial demand parameters are found to be indistinguishable. The sources of price endogeneity and historical fixes are reviewed. Ideal properties of a weighted price index are identified. For schedules containing exactly two rates, weighting is equivalent to a distribution function in consumption. This property is exploited to derive empirical weights from the national data, using values from a nonparametric generalization of the structural demand model and a nonparametric cumulative density function. The result is a generalization of the price difference metric to a weighted level-price index. The validity of a uniform weighting is not rejected. The weighted price index is data intensive, but the payoff is increased depth and precision for the economist and accessibility for the practitioner.

Raman spectroscopy has been successfully employed in the classification of breast pathologies involving basis spectra for chemical constituents of breast tissue and resulted in high sensitivity (94%) and specificity (96%) (Haka et al, 2005). Motivated by recent developments in nonparametric regression, in this work, we adapt stacking, boosting, and dynamic ensemble learning into a nonparametric regression framework with application to Raman spectroscopy analysis for breast cancer diagnosis. In Chapter 2, we apply compound estimation (Charnigo and Srinivasan, 2011) in Raman spectra analysis to classify normal, benign, and malignant breast tissue. We explore both the spectra profiles and their derivatives to differentiate different types of breast tissue. In Chapters 3-5 of this dissertation, we develop a novel paradigm for incorporating ensemble learning classification methodology into a nonparametric regression framework. Specifically, in Chapter 3 we set up modified stacking framework and combine different classifiers together to make better predictions in nonparametric regression settings. In Chapter 4 we develop a method by incorporating a modified AdaBoost algorithm in nonparametric regression settings to improve classification accuracy. In Chapter 5 we propose a dynamic ensemble integration based on multiple meta-learning strategies for nonparametric regression based classification. In Chapter 6, we revisit the Raman spectroscopy data in Chapter 2, and make improvements based on the developments of the methods from Chapter 3 to Chapter 4. Finally we summarize the major findings and contributions of this work as well as identify opportunities for future research and their public health implications.

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<p>In this thesis, I develop two sets of methods to help understand two distinct but also</p><p>related issues in financial economics.</p><p>First, representative agent models have been successfully applied to explain asset</p><p>market phenomenons. They are often simple to work with and appeal to intuition by</p><p>permitting a direct link between the agent's optimization behavior and asset market</p><p>dynamics. However, their particular modeling choices sometimes yield undesirable</p><p>or even counterintuitive consequences. Several diagnostic tools have been developed by the asset pricing literature to detect these unwanted consequences. I contribute to this literature by developing a new continuum of nonparametric asset pricing bounds to diagnose representative agent models. Chapter 1 lays down the theoretical framework and discusses its relevance to existing approaches. Empirically, it uses bounds implied by index option returns to study a well-known class of representative agent models|the rare disaster models. Chapter 2 builds on the insights of Chapter 1 to study dynamic models. It uses model implied conditional variables to sharpen asset pricing bounds, allowing a more powerful diagnosis of dynamic models.</p><p>While the first two chapters focus on the diagnosis of a particular model, Chapter</p><p>3 and 4 study the joint inference of a group of models or risk factors. Drawing on</p><p>multiple hypothesis testing in the statistics literature, Chapter 3 shows that many of</p><p>the risk factors documented by the academic literature are likely to be false. It also</p><p>proposes a new statistical framework to study multiple hypothesis testing under test</p><p>correlation and hidden tests. Chapter 4 further studies the statistical properties of</p><p>this framework through simulations.</p> / Dissertation

Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms’ optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans’ innate cognitive learning ability. Active learning is one such biologically inspired model, designed to mimic humans’ dynamic, evolving, adaptive and intelligent cognitive learning ability. Active learning is a class of learning algorithms that aim to create an accurate classifier by iteratively selecting essentially important unlabeled data points by the means of adaptive querying and training the classifier on those data points which are potentially useful for the targeted learning task (Tong & Koller, 2002). The traditional active learning techniques are implemented under supervised or semi-supervised learning settings (Pang et al., 2009). Our proposed model performs the active learning in an unsupervised setting by introducing a discriminative selective sampling criterion, which reduces the computational cost by substantially decreasing the number of irrelevant instances to be learned by the classifier. The methods based on passive learning (which assumes the entire dataset for training is truly informative and is presented in advance) prove to be inadequate in a real world application (Pang et al., 2009). To overcome this limitation, we have developed Active Mode Incremental Nonparametric Discriminant Analysis (aIncNDA) which undertakes adaptive discriminant selection of the instances for an incremental NDA learning. NDA is a discriminant analysis method that has been incorporated in our selective sampling technique in order to reduce the effects of the outliers (which are anomalous observations/data points in a dataset). It works with significant efficiency on the anomalous datasets, thereby minimizing the computational cost (Raducanu & Vitri´a, 2008). NDA is one of the methods used in the proposed active learning model. This thesis presents the research on a discrimination-based active learning where NDA is extended for fast discrimination analysis and data sampling. In addition to NDA, a base classifier (such as Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN)) is applied to discover and merge the knowledge from the newly acquired data. The performance of our proposed method is evaluated against benchmark University of California, Irvine (UCI) datasets, face image, and object image category datasets. The assessment that was carried out on the UCI datasets showed that Active Mode Incremental NDA (aIncNDA) performs at par and in many cases better than the incremental NDA with a lower number of instances. Additionally, aIncNDA also performs efficiently under the different levels of redundancy, but has an improved discrimination performance more often than a passive incremental NDA. In an application that undertakes the face image and object image recognition and retrieval task, it can be seen that the proposed multi-example active learning system dynamically and incrementally learns from the newly obtained images, thereby gradually reducing its retrieval (classification) error rate by the means of iterative refinement. The results of the empirical investigation show that our proposed active learning model can be used for classification with increased efficiency. Furthermore, given the nature of network data which is large, streaming, and constantly changing, we believe that our method can find practical application in the field of Internet security.

This thesis presents methods for improving the detection processing of active sonar systems. Measures to compensate for or even exploit particular characteristics of the detection problem for these systems are considered. Reverberation is the result of scattering of the transmitted signal from non-target features. Multipath and variability are particularly pronounced for underwater sound signals because propagation is very sensitive to spatial and temporal temperature variations. Another problem is the low pulse repetition rate due to the relatively low speed of sound. This low data rate reduces tracking and detection performance. / Reverberation often arises as the sum of many small contributions so that received data has a multivariate Gaussian distribution. Estimating the large numbers of parameters in the distribution requires a lot of data. This data is not available because of the low data rate. Representing the scattering as an autoregressive process reduced the data requirement but at some cost to modelling accuracy. A coupled estimator algorithm is developed to estimate the parameters. Detection performance is compared to other models and estimators that assume Gaussian statistics. / To counter multipath distortion the delays and strength of the paths are estimated using a version of the expectation maximisation (EM) algorithm. The magnitude of path amplitudes is then used to decide if a target is present. The EM algorithm is also suggested as a way to find the likely amplitude of reverberation from a few large scatterers that that form non-Gaussian reverberation. / Non-parametric methods are considered for detection of short duration incoherent signals in a duct. These detectors compare the ranks of the data in a region being tested for target present to another region assumed to have no target. Simulations are used to explore performance and what happens when the independent samples assumption is violated by the presence of reverberation. / More data can improve detection. Exploiting data from multiple transmissions is difficult because the slow speed of sound allows targets to move out of detection cells between transmissions. Tracking the movements of potential targets can counter this problem. The usefulness of Integrated Probabalistic Data Association (IPDA), which calculates a probability of true track as well as track properties, is considered as a detection algorithm. Improvements when multiple receivers are used as well as limitations when sensor positions are uncertain are investigated.

Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms’ optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans’ innate cognitive learning ability. Active learning is one such biologically inspired model, designed to mimic humans’ dynamic, evolving, adaptive and intelligent cognitive learning ability. Active learning is a class of learning algorithms that aim to create an accurate classifier by iteratively selecting essentially important unlabeled data points by the means of adaptive querying and training the classifier on those data points which are potentially useful for the targeted learning task (Tong & Koller, 2002). The traditional active learning techniques are implemented under supervised or semi-supervised learning settings (Pang et al., 2009). Our proposed model performs the active learning in an unsupervised setting by introducing a discriminative selective sampling criterion, which reduces the computational cost by substantially decreasing the number of irrelevant instances to be learned by the classifier. The methods based on passive learning (which assumes the entire dataset for training is truly informative and is presented in advance) prove to be inadequate in a real world application (Pang et al., 2009). To overcome this limitation, we have developed Active Mode Incremental Nonparametric Discriminant Analysis (aIncNDA) which undertakes adaptive discriminant selection of the instances for an incremental NDA learning. NDA is a discriminant analysis method that has been incorporated in our selective sampling technique in order to reduce the effects of the outliers (which are anomalous observations/data points in a dataset). It works with significant efficiency on the anomalous datasets, thereby minimizing the computational cost (Raducanu & Vitri´a, 2008). NDA is one of the methods used in the proposed active learning model. This thesis presents the research on a discrimination-based active learning where NDA is extended for fast discrimination analysis and data sampling. In addition to NDA, a base classifier (such as Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN)) is applied to discover and merge the knowledge from the newly acquired data. The performance of our proposed method is evaluated against benchmark University of California, Irvine (UCI) datasets, face image, and object image category datasets. The assessment that was carried out on the UCI datasets showed that Active Mode Incremental NDA (aIncNDA) performs at par and in many cases better than the incremental NDA with a lower number of instances. Additionally, aIncNDA also performs efficiently under the different levels of redundancy, but has an improved discrimination performance more often than a passive incremental NDA. In an application that undertakes the face image and object image recognition and retrieval task, it can be seen that the proposed multi-example active learning system dynamically and incrementally learns from the newly obtained images, thereby gradually reducing its retrieval (classification) error rate by the means of iterative refinement. The results of the empirical investigation show that our proposed active learning model can be used for classification with increased efficiency. Furthermore, given the nature of network data which is large, streaming, and constantly changing, we believe that our method can find practical application in the field of Internet security.

Thesis (Ph.D.)--Baylor University, 2008. / Includes bibliographical references (p. 64-65)

Thesis (M.Sc.) - Carleton University, 2008. / Includes bibliographical references (p. 114-117). Also available in electronic format on the Internet.