To obtain a suitable mathematical model of the input-output behavior of highly nonlinear, multi-scale, nonparametric phenomena, we introduce an adaptive radial basis function approximation approach. We use this approach to estimate the discrepancy between traditional model areas and the multiscale physics of systems involving distributed sensing and technology. Radial Basis Function Networks offers the possible approach to nonparametric multi-scale modeling for dynamical systems like the adaptive wing with the Synthetic Jet Actuator (SJA). We use the Regularized Orthogonal Least Square method (Mark, 1996) and the RAN-EKF (Resource Allocating Network-Extended Kalman Filter) as a reference approach. The first part of the algorithm determines the location of centers one by one until the error goal is met and regularization is achieved. The second process includes an algorithm for the adaptation of all the parameters in the Radial Basis Function Network, centers, variances (shapes) and weights. To demonstrate the effectiveness of these algorithms, SJA wind tunnel data are modeled using this approach. Good performance is obtained compared with conventional neural networks like the multi layer neural network and least square algorithm. Following this work, we establish Model Reference Adaptive Control (MRAC) formulations using an off-line Radial Basis Function Networks (RBFN). We introduce the adaptive control law using a RBFN. A theory that combines RBFN and adaptive control is demonstrated through the simple numerical simulation of the SJA wing. It is expected that these studies will provide a basis for achieving an intelligent control structure for future active wing aircraft.

To obtain a suitable mathematical model of the input-output behavior of highly nonlinear, multi-scale, nonparametric phenomena, we introduce an adaptive radial basis function approximation approach. We use this approach to estimate the discrepancy between traditional model areas and the multiscale physics of systems involving distributed sensing and technology. Radial Basis Function Networks offers the possible approach to nonparametric multi-scale modeling for dynamical systems like the adaptive wing with the Synthetic Jet Actuator (SJA). We use the Regularized Orthogonal Least Square method (Mark, 1996) and the RAN-EKF (Resource Allocating Network-Extended Kalman Filter) as a reference approach. The first part of the algorithm determines the location of centers one by one until the error goal is met and regularization is achieved. The second process includes an algorithm for the adaptation of all the parameters in the Radial Basis Function Network, centers, variances (shapes) and weights. To demonstrate the effectiveness of these algorithms, SJA wind tunnel data are modeled using this approach. Good performance is obtained compared with conventional neural networks like the multi layer neural network and least square algorithm. Following this work, we establish Model Reference Adaptive Control (MRAC) formulations using an off-line Radial Basis Function Networks (RBFN). We introduce the adaptive control law using a RBFN. A theory that combines RBFN and adaptive control is demonstrated through the simple numerical simulation of the SJA wing. It is expected that these studies will provide a basis for achieving an intelligent control structure for future active wing aircraft.

The choice of firm boundaries is one of the most fundamental elements of organizational strategy. It determines industry positioning, enables the development and leverage of distinctive capabilities, and ultimately establishes the basis for sustainable competitive advantage. In the modern economy, organizational unbundling and vertical disintegration has become a recurrent theme across many industries, reflecting a major transformation in industrial organization and firm strategy. What are the drivers of this modern trend of vertical disintegration? How do we reconcile this modern phenomenon with the vertical integration logic of previous decades? Beginning with Transaction Cost Economics (TCE) as the underlying framework, we draw from the Information Processing, Coordination and Capabilities literatures to develop an integrated theoretical framework for examining and rationalizing the determinants of vertical disintegration. The recent restructuring of the US electric utility industry provides a suitable empirical context to undertake a rigorous examination of this theoretical framework: a context that manifests institutional heterogeneity, high levels of uncertainty, intense coordination requirements, and a variety of competing market and organizational institutions in transition. We employ multi-level modeling techniques, to account for firm heterogeneity and time-variant institutional parameters in our longitudinal panel data, thus allowing for a richer analysis of institutional effects. The results show that there has been systematic vertical disintegration in the electric utility industry over the period of study, 1994-2002, influenced by both firm-level and state/federal-level institutional factors as well as structural market attributes, which serve as proxies for demand and supply uncertainty. Using IT investment intensity as a proxy for firm-level IT Capability, we also established an overall significant negative effect of IT on vertical integration, consistent with previous studies about the effects of IT on firm size. The main findings confirmed several standard TCE propositions, and also address several known shortcomings, most notably the ambiguity in the treatment of uncertainty. In addition, the robust examination of the empirical evidence associated with the restructuring of the Electric Utility industry allowed us to isolate the relative effects of various institutional mechanisms and structural market attributes. These findings help to illuminate the understanding and ultimately the programming of deregulation effects.

Anomaly detection is a relevant problem in the field of Mechanical Engineering, because the analysis of mechanical systems often relies on identifying deviations from what is considered "normal". The mechanical sciences are represented by a heterogeneous collection of data types: some systems may be highly dimensional, may contain exclusively spatial or temporal data, may be spatiotemporally linked, or may be non-deterministic and best described probabilistically. Given the broad range of data types in this field, it is not possible to propose a single processing method that will be appropriate, or even usable, for all data types. This has led to human observation remaining a common, albeit costly and inefficient, approach to detecting anomalous signals or patterns in mechanical data. The advantages of automated anomaly detection in mechanical systems include reduced monitoring costs, increased reliability of fault detection, and improved safety for users and operators. This dissertation proposes a hierarchical framework for anomaly detection through machine learning, and applies it to three distinct and heterogeneous data types: state-based data, parameter-driven data, and spatiotemporal sensor network data. In time-series data, anomaly detection results were robust in synthetic data generated using multiple simulation algorithms, as well as experimental data from rolling element bearings, with highly accurate detection rates (>99% detection, <1% false alarm). Significant developments were shown in parameter-driven data by reducing the sample sizes necessary for analysis, as well as reducing the time required for computation. The event-space model extends previous work into a geospatial sensor network and demonstrates applications of this type of event modeling at various timescales, and compares the model to results obtained using other approaches. Each data type is processed in a unique way relative to the others, but all are fitted to the same hierarchical structure for system modeling. This hierarchical model is the key development proposed by this dissertation, and makes both novel and significant contributions to the fields of mechanical analysis and data processing. This work demonstrates the effectiveness of the developed approaches, details how they differ from other relevant industry standard methods, and concludes with a proposal for additional research into other data types. / Ph. D.

Projecting environmental niche models through time is a common goal when studying species response to climatic change. Species distribution models (SDMs) are commonly used to estimate a species' niche from observed patterns of occurrence and environmental predictors. However, a species niche is also shaped by non-environmental factors--including biotic interactions and dispersal barrier—truncating SDM estimates. Though truncated SDMs may accurately predict present-day species niche, projections through time are often biased by environmental condition change. Modeling niche in a phylogenetic framework leverages a clade's shared evolutionary history to pull species estimates closer towards phylogenetic conserved values and farther away from species specific biases. We propose a new Bayesian model of phylogenetic niche implemented in R. Under our model, species SDM parameters are transformed into biologically interpretable continuous parameters of environmental niche optimum, breadth, and tolerance evolving under multivariate Brownian motion random walk. Through simulation analyses, we demonstrated model accuracy and precision that improved as phylogeny size increased. We also demonstrated our model on a clade of eastern United States Plethodontid salamanders by accurately estimating species niche, even when no occurrence data is present. Our model demonstrates a novel framework where niche changes can be studied forwards and backwards through time to understand ancestral ranges, patterns of environmental specialization, and niche in data deficient species. / Master of Science / As many species face increasing pressure in a changing climate, it is crucial to understand the set of environmental conditions that shape species' ranges--known as the environmental niche--to guide conservation and land management practices. Species distribution models (SDMs) are common tools that are used to model species' environmental niche. These models treat a species' probability of occurrence as a function of environmental conditions. SDM niche estimates can predict a species' range given climate data, paleoclimate, or projections of future climate change to estimate species range shifts from the past to the future. However, SDM estimates are often biased by non-environmental factors shaping a species' range including competitive divergence or dispersal barriers. Biased SDM estimates can result in range predictions that get worse as we extrapolate beyond the observed climatic conditions. One way to overcome these biases is by leveraging the shared evolutionary history amongst related species to "fill in the gaps". Species that are more closely phylogenetically related often have more similar or "conserved" environmental niches. By estimating environmental niche over all species in a clade jointly, we can leverage niche conservatism to produce more biologically realistic estimates of niche. However, currently a methodological gap exists between SDMs estimates and macroevolutionary models, prohibiting them from being estimated jointly. We propose a novel model of evolutionary niche called PhyNE (Phylogenetic Niche Evolution), where biologically realistic environmental niches are fit across a set of species with occurrence data, while simultaneously fitting and leveraging a model of evolution across a portion of the tree of life. We evaluated model accuracy, bias, and precision through simulation analyses. Accuracy and precision increased with larger phylogeny size and effectively estimated model parameters. We then applied PhyNE to Plethodontid salamanders from Eastern North America. This ecologically-important and diverse group of lungless salamanders require cold and wet conditions and have distributions that are strongly affected by climatic conditions. Species within the family vary greatly in distribution, with some species being wide ranging generalists, while others are hyper-endemics that inhabit specific mountains in the Southern Appalachians with restricted thermal and hydric conditions. We fit PhyNE to occurrence data for these species and their associated average annual precipitation and temperature data. We identified no correlations between species environmental preference and specialization. Pattern of preference and specialization varied among Plethodontid species groups, with more aquatic species possessing a broader environmental niche, likely due to the aquatic microclimate facilitating occurrence in a wider range of conditions. We demonstrated the effectiveness of PhyNE's evolutionarily-informed estimates of environmental niche, even when species' occurrence data is limited or even absent. PhyNE establishes a proof-of-concept framework for a new class of approaches for studying niche evolution, including improved methods for estimating niche for data-deficient species, historical reconstructions, future predictions under climate change, and evaluation of niche evolutionary processes across the tree of life. Our approach establishes a framework for leveraging the rapidly growing availability of biodiversity data and molecular phylogenies to make robust eco-evolutionary predictions and assessments of species' niche and distributions in a rapidly changing world.

With remarkable advances in computing power, computer experiments continue to expand the boundaries and drive down the cost of various scientific discoveries. New challenges keep arising from designing, analyzing, modeling, calibrating, optimizing, and predicting in computer experiments. This dissertation consists of six chapters, exploring statistical methodologies in sequential learning, model calibration, and uncertainty quantification for heteroskedastic computer experiments and large-scale computer experiments. For heteroskedastic computer experiments, an optimal lookahead based sequential learning strategy is presented, balancing replication and exploration to facilitate separating signal from input-dependent noise. Motivated by challenges in both large data size and model fidelity arising from ever larger modern computer experiments, highly accurate and computationally efficient divide-and-conquer calibration methods based on on-site experimental design and surrogate modeling for large-scale computer models are developed in this dissertation. The proposed methodology is applied to calibrate a real computer experiment from the gas and oil industry. This on-site surrogate calibration method is further extended to multiple output calibration problems. / Doctor of Philosophy / With remarkable advances in computing power, complex physical systems today can be simulated comparatively cheaply and to high accuracy through computer experiments. Computer experiments continue to expand the boundaries and drive down the cost of various scientific investigations, including biological, business, engineering, industrial, management, health-related, physical, and social sciences. This dissertation consists of six chapters, exploring statistical methodologies in sequential learning, model calibration, and uncertainty quantification for heteroskedastic computer experiments and large-scale computer experiments. For computer experiments with changing signal-to-noise ratio, an optimal lookahead based sequential learning strategy is presented, balancing replication and exploration to facilitate separating signal from complex noise structure. In order to effectively extract key information from massive amount of simulation and make better prediction for the real world, highly accurate and computationally efficient divide-and-conquer calibration methods for large-scale computer models are developed in this dissertation, addressing challenges in both large data size and model fidelity arising from ever larger modern computer experiments. The proposed methodology is applied to calibrate a real computer experiment from the gas and oil industry. This large-scale calibration method is further extended to solve multiple output calibration problems.

Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this paper we derive the shrinkage prior of Griffin et al. (2010) for the VAR case and its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariances of the VAR along with Gamma priors on a set of local and global prior scaling parameters. This prior setup is then generalized by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. A simulation exercise shows that the proposed framework yields more precise estimates of the model parameters and impulse response functions. In addition, a forecasting exercise applied to US data shows that the proposed prior outperforms other specifications in terms of point and density predictions. (authors' abstract) / Series: Department of Economics Working Paper Series

Forward and inverse modeling studies of the relationship between tree ring width and bivariate climate are performed using a model called VS-Lite. The monthly time-step model incorporates two simple but realistic nonlinearities in its description of the transformation of climate variability into ring width index. These features ground VS-Lite in scientific principles and make it more complex than empirically-derived statistical models commonly used to simulate tree ring width. At the same time, VS-Lite is vastly simpler and more efficient than pre-existing numerical models that simulate detailed biological aspects of tree growth. A forward modeling validation study shows that VS-Lite simulates a set of observed chronologies across the continental United States with comparable or better skill than simulations derived from a standard, linear regression based approach. This extra skill derives from VS-Lite's basis in mechanistic principles, which makes it more robust than the statistical methodology to climatic nonstationarity. A Bayesian parameterization approach is also developed that incorporates scientific information into the choice of locally optimal VS-Lite parameters. The parameters derived using the scheme are found to be interpretable in terms of the climate controls on growth, and so provide a means to guide applications of the model across varying climatologies. The first reconstructions of paleoclimate that assimilate scientific understanding of the ring width formation process are performed using VS-Lite to link the proxy data to potential climate histories. Bayesian statistical methods invert VS-Lite conditional on a given dendrochronolgy to produce probabilistic estimates of local bivariate climate. Using VS-Lite in this manner produces skillful estimates, but does not present advantages compared another set of probabilistic reconstructions that invert a simpler, linear, empirical forward model. This result suggests that future data-assimilation based reconstructions will need to integrate as many data sources as possible, both across space and proxy types, in order to benefit from information provided by mechanistic models of proxy formation.

Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncertainty involve the use of Bayesian model-averaging techniques. However, Bayesian model-averaging for spatial autoregressive models suffers from severe drawbacks both in terms of computational time and possible extensions to more flexible econometric frameworks. To alleviate these problems, this paper presents two global-local shrinkage priors in the context of high-dimensional matrix exponential spatial specifications. A simulation study is conducted to evaluate the performance of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. Moreover, we use pan-European regional economic growth data to illustrate the performance of the proposed shrinkage priors.

The continued study of asymptomatic Ebolavirus infection is necessary to develop a more complete understanding of Ebola transmission dynamics. This paper conducts a meta-analysis of eight studies that measure seroprevalence (the number of subjects that test positive for anti-Ebolavirus antibodies in their blood) in subjects with household exposure or known case-contact with Ebola, but that have shown no symptoms. In our two random effects Bayesian hierarchical models, we find estimated seroprevalences of 8.76% and 9.72%, significantly higher than the 3.3% found by a previous meta-analysis of these eight studies. We also produce a variation of this meta-analysis where we exclude two of the eight studies. In this model, we find an estimated seroprevalence of 4.4%, much lower than our first two Bayesian hierarchical models. We believe a random effects model more accurately reflects the heterogeneity between studies and thus asymptomatic Ebola is more seroprevalent than previously believed among subjects with household exposure or known case-contact. However, a strong conclusion cannot be reached on the seriousness of asymptomatic Ebola without an international testing standard and more data collection using this adopted standard.