Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

Ecologists try to understand how changing habitats alter the populations of organisms living within them, and how, in turn, these changing populations alter the environment. By linking individual or cellular (physiological) processes to system level responses, mechanistic models can help describe the feedback loops between organisms and the environment. Aquatic systems have long used mechanistic models, but increasing model complexity over the last 50 years has led to difficulty in parameterization. In fact, it is often unclear how researchers are choosing parameters at all, even though small changes in parameters can change qualitative predictions. I explore the challenges in parameter estimation present in even an ideal situation. Specifically, I conduct individual experiments for all of the needed parameters to describe a simple lab-based, aquatic system; estimate those parameters using the results from these experiments supplemented with literature data; and run a large experiment designed to test how well the lab-estimated parameters predict actual zooplankton populations and nutrient changes over time. I document best practices for finding and reporting parameter choices and show whole ecosystem level consequences of a variety of decisions. To get the best predictions I find that a mix of parameter estimation methods are necessary. Trait-based approaches are another method to understand species-environment interactions. Trait-based methods aggregate species into functional traits, perhaps making qualitative predictions easier. Theory suggests that more functionally diverse systems will be more resilient. I test this prediction in a simple aquatic system but am unable to find consistent support for this hypothesis, and instead finding that results are highly dependent on what measures of ecosystem recovery are used. Overall, more species-specific information is critical to building better models for both mechanistic and trait-based approaches. I expand species-specific data by providing new information, and collating information from literature on a small, tropical Cladocera. / Thesis / Doctor of Philosophy (PhD) / Predicting what will happen to a habitat after a disturbance is critical for conservation and management. Species specific information is useful for building a mechanistic understanding of ecology. Predictions that include underlying processes (mechanisms) may be more robust to a changing environment than predictions based on correlations. Eutrophication, the addition of excess nutrients, is a common problem in freshwater habitats. Being able to predict the effects of nutrient addition is critical for ensuring the health of freshwater ecosystems. By using species-specific life history and morphological information and a simple lab system, I test different methods of predicting and understanding the consequences of eutrophication. I find that the ramifications of eutrophication are not easily predicted by species' categorizations or with a more detailed mechanistic model.

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The following thesis explores the use of Bayes’ theorem for modelling bioprocesses, specifically using a combination of data-driven modelling techniques and Bayesian inference, to address practical concerns that arise when estimating parameters. This thesis is divided into four chapters, including a novel contribution to the use of sur- rogate modelling and parameter estimation algorithms for noisy data. The 2nd chapter addresses the problem of high computational expense when estimat- ing parameters using complex models. The main solution here is the use of surrogate modelling. This method was then applied to a high-fidelity model provided by Sarto- rius AG. In this, a 3-batch run (simulated) of the bioreactor was passed through the algorithm, and two influential parameters, the growth and death rates of the live cell cultures, were estimated. The 3rd chapter addresses other challenges that arise in parameter estimation prob- lems. Specifically, the issue of having limited data on a new process can be addressed using historical data, a distinct feature in Bayesian Learning. Finally, the problem with choosing the “right” model for a given process is studied through the use of a term in Bayesian inference known as the evidence. In this, the evidence is used to select between a series of models based on both model complexity and goodness-of-fit to the data. / Thesis / Master of Applied Science (MASc)

When studying detection systems, parameters associated with the Receiver Operating Characteristic (ROC) curve are often estimated to assess system performance. In some applied settings it is often not possible to test the detection system with large numbers of stimuli. The resulting small sample statistics many have undesirable properties. The characteristics of these small sample ROC estimators were examined in a Monte Carlo simulation. Three popular ROC parameters were chosen for study. One of the parameters was a single parameter index of system performance, Area under the ROC curve. The other parameters, ROC intercept and slope, were considered as a pair. ROC intercept and slope were varied along with sample size and points on the certainty rating scale to form a four way factorial design. Several types of estimators were examined. For the parameter, Area under the curve, Maximum Likelihood (ML), three types of Least Squares (LS), and Distribution Free (DF) estimators were considered. Except for the DF estimator, the same estimators were considered for the parameters, intercept and slope. These estimators were compared with respect to three characteristics: bias, efficiency, and consistency. For Area under the curve, the ML estimator was the least biased. The DF estimator was the most efficient, and all the estimators except the DF estimator appeared to be consistent. For intercept and slope the LS estimator that minimized vertical error of the points from the ROC curve (line) was the least biased for both estimators. This LS estimator was also the most efficient. This estimator along with the ML estimator also appeared to be the most consistent. The other two estimators had no significant trend toward consistency. These results along with other findings, illustrate that different estimators may be "best" for different sample sizes and for different parameters. Therefore, researchers should carefully consider the characteristics of ROC estimators before using them as indices of system performance.

It is well known that when the input variables of the linear regression model are subject to noise contamination, the model parameters can not be estimated uniquely. This, in the statistical literature, is referred to as the identifiability problem of the errors-in-variables models. Further, in linear regression there is an explicit assumption of the existence of a single linear relationship. The statistical properties of the errors-in-variables models under the assumption that the noise variances are either known or that they can be estimated are well documented. In many situations, however, such information is neither available nor obtainable. Although under such circumstances one can not obtain a unique vector of parameters, the space, Ω, of the feasible solutions can be computed. Additionally, assumption of existence of a single linear relationship may be presumptuous as well. A multi-equation model similar to the simultaneous-equations models of econometrics may be more appropriate. The goals of this dissertation are the following: (1) To present analytical techniques or algorithms to reduce the solution space, Ω, when any type of prior information, exact or relative, is available; (2) The data covariance matrix, Σ, can be examined to determine whether or not Ω is bounded. If Ω is not bounded a multi-equation model is more appropriate. The methodology for identifying the subsets of variables within which linear relations can feasibly exist is presented; (3) Ridge regression technique is commonly employed in order to reduce the ills caused by collinearity. This is achieved by perturbing the diagonal elements of Σ. In certain situations, applying ridge regression causes some of the coefficients to change signs. An analytical technique is presented to measure the amount of perturbation required to render such variables ineffective. This information can assist the analyst in variable selection as well as deciding on the appropriate model; (4) For the situations when Ω is bounded, a new weighted regression technique based on the computed upper bounds on the noise variances is presented. This technique will result in identification of a unique estimate of the model parameters.

"This dissertation documents a study on parameter estimation methods for comprehensive pyrolysis modeling. There are four parts to this work, which are (1) evaluating effects of applying different kinetic models to pyrolysis modeling of fiberglass reinforced polymer composites; (2); evaluation of pyrolysis parameters for fiberglass reinforced polymer composites based on multi-objective optimization; (3) parameter estimation for comprehensive pyrolysis modeling: guidance and critical observations; and (4) engineering guide for estimating material pyrolysis properties for fire modeling. In the first section (Section 1), evaluation work is conducted to determine the effects of applying different kinetic models (KMs), developed based on thermal analysis using TGA data, when used in typical 1D pyrolysis models of fiberglass reinforced polymer (FRP) composites. The study shows that that increasing complexity of KMs to be used in pyrolysis modeling is unnecessary for the FRP samples investigated. Additionally, the findings from this research indicates that the basic assumption of considering thermal decomposition of each computational cell in comprehensive pyrolysis modeling as equivalent to that in a TGA experiment becomes inapplicable at depth and higher heating rates. The second part of this dissertation (Section 2) reports the results from a study conducted to investigate the ability of global, multi-objective and multi-variable optimization methods to estimate material parameters for comprehensive pyrolysis models. The research materials are two fiberglass reinforced polymer (FRP) composites that share the same fiberglass mats but with two different resin systems. One resin system is composed of a single component and the other system is composed of two components (resin and fire retardant additive). The results show that for a well-configured parameter estimation exercise using the optimization method described above, (1) estimated results are within ± 100% of the measurements in general; (2) increasing complexity of the kinetic modeling for a single component system has insignificant effect on estimated values; (3) increasing complexity of the kinetic modeling for a multiple component system with each element having different thermal characteristics has positive effect on estimated values; and (4) parameter estimation using an optimization method with appropriate level of complexity in kinetic model and optimization targets can find estimations that can be considered as effective material property values. The third part of this dissertation (Section 3) proposes a process for conducting parameter estimation for comprehensive pyrolysis models. The work describes the underlying concepts considered in the proposed process and gives discussions of its limitations. Additionally, example cases of parameter estimation exercise are shown to illustrate the application of the parameter estimation process. There are four materials considered in the example cases – thermoplastics (PMMA), corrugated cardboard, fiberglass reinforced polymer composites and plywood. In the last part (Section 4), the actual Guide, a standardized procedure for obtaining material parameters for input into a wide range of pyrolysis models is presented. This is a step-by-step process that provides a brief description of modeling approaches and assumptions; a typical mathematical formulation to identify model parameters in the equations; and methods of estimating the model parameters either by independent measurements or optimization in pair with the model. In the Guide, example cases are given to show how the process can be applied to different types of real-world materials. "

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.