233 research outputs found
Not all Anionic Polyelectrolytes complex with DTAB
No full textYuguo Cui, Robert Pelton, Terence Cosgrove, Robert Richardson, Sheng Dai, Stuart Prescott, Isabelle Grillo, Howard Ketelson and David Meadow
Municipal managers’ responsiveness to public demands: connecting attitudinal willingness, behavioral willingness, environmental and organizational factors
No full textPublic responsiveness, or bureaucratic responsiveness to citizen demands, is central in public administration theories. It has become a key concept regarding the appropriate role of bureaucracy and professional administrators in a democratic political system. In city management, responsiveness to public demands should be particularly addressed given the fact that local professionals have constant and direct contact with local residents. This dissertation builds on existing studies that identify the determinants of public responsiveness. One significant research gap of existing researches was noticed, that is, few studies have included public administrators’ willingness into the analysis framework. Current studies have identified organizational factors, environmental factors, features of policy clients and problem intensity as predictors of public responsiveness. However, examining public responsiveness without assessing individuals’ willingness would neglect their own interpretation and interaction with the environmental and institutional factors. It is at the individual level that the functioning of environmental and organizational factors is enacted. This dissertation project focuses municipal managers’ public responsiveness in the formulation of local budgets. The main research questions of this study include: (1) What is the actual level of municipal managers’ public responsiveness? (2) Given the importance of municipal managers’ attitudes, how can we foster their favorable attitude toward public responsiveness? In other words, what are the determinants of their attitudinal willingness to be responsive to public demands? (3) What are the determinants of municipal manages’ public responsiveness? How do municipal managers’ attitudinal and behavioral willingness connect environmental and organizational factors in determining their public responsiveness? The data in this dissertation was collected from New Jersey and Pennsylvania municipal managers. The seemingly unrelated regression (SUR) result indicates that the factor with the strongest impact on municipal managers’ attitudinal willingness is successful implementation and practices in other municipalities. It highlights the importance of social learning in acquiring and assimilating social knowledge. In the public responsiveness model, the structural equation modeling (SEM) result confirms that a thorough understanding of the determinants of public responsiveness cannot be separated from examining municipal managers’ attitudinal and behavioral willingness. It further suggests that environmental and organizational factors tend to enhance municipal managers’ public responsiveness (1) through institutional constraints; (2) through enhancing their perceived behavioral control.Ph. D.Includes bibliographical referencesIncludes vitaby Yuguo Lia
Inferring influence in dynamic networks and multiple sampling for estimation of fractional Brownian motion
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01The student, Xiang Cui, accepted the attached license on 2022-04-15 at 10:37.The student, Xiang Cui, submitted this Dissertation for approval on 2022-04-15 at 11:08.This Dissertation was approved for publication on 2022-04-19 at 07:53.DSpace SAF Submission Ingestion Package generated from Vireo submission #17699 on 2022-11-11 at 12:47:02This thesis is divided into two parts. In the first part, we focus on network influence analysis in dynamic networks. In the second part, we focus on multiple sampling methods to estimate the fractional Brownian motion strategically. In Chapter 2, we explore degrees of influence in dynamic networks. We propose a longitudinal influence model to represent how an individual's behavior can be influenced by others in dynamic networks. A sequential hypothesis testing procedure is proposed to determine the degrees of influence. We provide a theoretical justification of our proposed sequential testing procedure. Simulation studies show our testing procedure can preserve the level of the test and is more powerful for a larger network. We also apply our proposed method to detect the degrees of influence for Higgs Twitter data set and Digg2009 data set. In Chapter 3, we investigate another aspect of network influence analysis, which is influence power. The influence power describes the magnitude of influence that each node has on the other nodes in the network. In this chapter, we build a network influence autoregression model to model the influence powers among different nodes in dynamic networks. We use the maximum likelihood estimation method to estimate the parameters in the model. We show the estimation consistency of parameter estimates and demonstrate the performance of our proposed methods using simulation studies. We also illustrate the usefulness of our model by applying it to the China fiscal revenue data. In Chapter 4, we focus on multiple sampling problems for the estimation of the fractional Brownian motion when the maximum number of samples is limited, extending existing results in the literature in a non-Markovian framework. Two classes of sampling schemes are proposed: a deterministic scheme and a level-triggered scheme. For the deterministic sampling scheme, the sampling times are selected beforehand and do not depend on the process trajectory. For the level-triggered sampling scheme, the sampling times are the times when the process crosses predetermined thresholds. The sampling times are selected sequentially in time and depend on the process trajectory. For each of the schemes, we derive the optimal sampling times by minimizing the aggregate squared error distortion. We then show that the optimal sampling strategies heavily depend on the dependence structure of the process
Contributions to modeling parasite dynamics and dimension reduction
No full textFor my thesis, I have worked on two projects: modeling parasite dynamics (Chapter 2) and complementary dimensionality analysis (Chapter 3).
In the first project, we study a longitudinal data of infection with the parasite Giardia lamblia among children in Kenya. Understanding the infection and recovery rate from parasitic infections is valuable for public health planning. Two challenges in modeling these rates are (1) infection status is only observed at discrete times even though infection and recovery take place in continuous time and (2) detectability of infection is imperfect. We address these issues through a Bayesian hierarchical model based on a random effects Weibull distribution. The model incorporates heterogeneity of the infection and recovery rate among individuals and allows for imperfect detectability. We estimate the model by a Markov chain Monte Carlo algorithm with data augmentation. We present simulation studies and an application to an infection study about the parasite Giardia lamblia among children in Kenya.
The second project focuses on supervised dimension reduction. The goal of supervised dimension reduction (SDR) is to find a compact yet informative representation of the original data space via some transformation. Most SDR algorithms are formulated as an optimization problem with the objective being a linear function of the second order statistics of the data. However, such an objective function tends to overemphasize those directions already achieving large between-class distances yet making little improvement over the classification accuracy. To address this issue, we introduce two objective functions, which are directly linked to the classification accuracy, then present an algorithm that sequentially solves the nonlinear objective functions.Item withdrawn by Mark Zulauf ([email protected]) on 2012-04-10T14:35:31Z
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University of Illinois Theses & Dissertations (ID: 1)
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Graduate Theses and Dissertations at Illinois (ID: 204)
Dissertations and Theses - Statistics (ID: 774)
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Characterizing Hydroxypropyl Guar - Borate Interactions with Model Tear Film Components
No full text Hydroxypropyl guar (HPG) is an effective ingredient in lubricant eye drops used by patients with dry eye disease. The overall goal of the work described in this thesis is to understand the physical-chemical properties of HPG in the presence ofmodel surfaces and solutes with view to understanding the behavior of HPG in the tear film. HPG behaviors are complex because borate ions bind to HPG, which converts nonionic HPG into anionic polyelectrolyte, RPG-borate. The borate binding constants are very low, meaning the charges on RPG-borate are labile. Another consequence ofweak binding is that the equilibrium electrolyte concentration with HPG-borate is relatively high. Mathematical models were developed to predict the structure of HPG-borate as functions of pH. This thesis probes the question "When does HPG-borate behave as an anionic polyelectrolyte?" This work shows that HPG-borate exhibits deviant behaviors of an anionic polyelectrolyte: does not interact with cationic surfactants below the CMC; does not interact with lysozyme (cationic protein), and does not adsorb onto cationic liposomes. By contrast, anionic polyelectrolytes such as carboxymethyl guar display generic behaviors. On the other hand, HPG-borate forms polyelectrolyte complexes with cationic polyelectrolytes at low ionic strength and other work from our laboratory has shown that HPG-borate flocculates cationic polystyrene latex. This complex range of RPG-borate behaviors was rationalized by proposing that the labile nature ofthe charge groups means that the charge density on RPG-borate is regulated by the local electrostatic environment. Near a cationic surface HPG-borate charge density increases whereas near an anionic surface the charge density is lower. Anionic liposome interactions with HPG-borate were characterized. HPG concentrations close to clinical levels induced depletion flocculation ofthe anionic liposomes. This is the first example we have found depletion interactions were proposed for the tear film. To summarize the main implications for the ophthalmic application of HPG are: 1) under ophthalmic conditions HPG-borate behaves as a nonionic water soluble polymer; 2) RPG-borate will adsorb onto hydrophobic domains but will not interact with lysozyme; 3) depletion interactions are important and have the potential to stabilize the lipid layer and destabilize emulsion droplets and other dispersed species in the tear film. ThesisDoctor of Philosophy (PhD
Why don’t we just open the windows?
No full textGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Indoor Environmen
Sampling for network motif detection and estimation of Q-matrix and learning trajectories in DINA model
No full text"Monte Carlo methods provide tools to conduct statistical inference on models that are difficult or impossible to compute analytically and are widely used in many areas of statistical applications, such as bioinformatics and psychometrics. This thesis develops several sampling algorithms to address open issues in network analysis and educational assessments.
The first problem we investigate is network motif detection. Network motifs are substructures that appear significantly more often in the given network than in other random networks. Motif detection is crucial for discovering new characteristics in biological, developmental, and social networks. We propose a novel sequential importance sampling strategy to estimate subgraph frequencies and detect network motifs. The method is developed by sampling subgraphs sequentially node by node using a carefully chosen proposal distribution. The method generates subgraphs from a distribution close to uniform and performs better than competing methods. We apply the method to four real-world networks and demonstrate outstanding performance in practical examples.
The other two issues are related to educational measurement in psychometrics. Cognitive diagnosis models (CDMs) are partially ordered latent class models to classify students into skill mastery profiles. In educational assessment, these models help researchers analyze students' mastery of skills and learning process based on their responses to test items. The deterministic inputs, noisy ""AND"" gate model (DINA) is a popular psychometric model for cognitive diagnosis. We investigate the estimation of Q-matrix in DINA model. Q matrix is a binary matrix which maps the test item to its corresponding required attributes. We propose a Bayesian framework for estimating the DINA Q matrix. The proposed algorithms ensure that the estimated Q matrices always satisfy the identifiability constraints. We present Monte Carlo simulations to support the accuracy of parameter recovery and apply our algorithms to Tatsuoka's fraction-subtraction dataset.
The last project is related to the recovery of learning process. The increasing presence of electronic and online learning resources presents challenges and opportunities for psychometric techniques that can assist in the measurement of abilities and even hasten their mastery. CDMs can assist in carefully navigating through the training and assessment of these skills in e-learning applications. We propose a class of CDMs for modeling changes in attributes, which we refer to as learning trajectories. We focus on the development of Bayesian procedures for estimating parameters of a first-order hidden Markov model and apply the developed model to a spatial rotation experimental intervention."Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01The student, Yinghan Chen, accepted the attached license on 2017-06-06 at 10:11.The student, Yinghan Chen, submitted this Dissertation for approval on 2017-06-06 at 10:33.This Dissertation was approved for publication on 2017-06-06 at 15:14.DSpace SAF Submission Ingestion Package generated from Vireo submission #11195 on 2017-09-29 at 10:45:41Made available in DSpace on 2017-09-29T17:45:12Z (GMT). No. of bitstreams: 3
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Statistical inference based on characteristic functions for intractable likelihood problems
No full textThis dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven Ornstein-Uhlenbeck processes), the transition density may not be available. However, the (conditional) characteristic function is sometimes known. We study various statistical inference methods for fitting those processes with implicit characteristic functions.
In the first part, an efficient sampling method based on Bayesian empirical likelihood is developed. The method involves pseudo-marginal Markov chain Monte Carlo with temperature and is shown to be effective for Lévy processes. In the second part and third part, we study maximum likelihood methods and empirical characteristic function estimation based on characteristic functions. We find the analyticity of the characteristic function can make efficient implementations of both methods possible, guaranteeing asymptotic properties as well. We also find, for certain models, very large samples might be needed to accurately identify the true parameters. Numerical results show the appealingness of some infinite activity models. In the last part, this dissertation includes my another project, which is about truth discovery in data mining. A dynamic model is developed to discover the truth between information sources across time. Experiments on real-world applications demonstrate its advantages over previous approaches.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01The student, Fan Yang, accepted the attached license on 2018-04-16 at 11:52.The student, Fan Yang, submitted this Dissertation for approval on 2018-04-16 at 12:21.This Dissertation was approved for publication on 2018-04-17 at 08:05.DSpace SAF Submission Ingestion Package generated from Vireo submission #12252 on 2018-08-31 at 17:29:05Made available in DSpace on 2018-09-04T20:47:14Z (GMT). No. of bitstreams: 2
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