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Full-Text Articles in Physical Sciences and Mathematics
Formulating A Crowd State Prediction Problem For Application To Crowd Control, Brooks A. Butler
Formulating A Crowd State Prediction Problem For Application To Crowd Control, Brooks A. Butler
Theses and Dissertations
This project considers a new application of crowd control, namely, keeping the public safe during large scale demonstrations. This problem is difficult for a variety of reasons, including limited access to informative sensing and effective actuation mechanisms, as well as limited understanding of crowd psychology and dynamics. This project takes a first step towards solving this problem by formulating a crowd state prediction problem in consideration of recent work involving crowd behavior identification, crowd movement modeling, and crowd psychology modeling. We build a non-linear crowd behavior model incorporating components of personality modeling, human emotion modeling, group opinion dynamics, and group …
Estimation Problems For Pooled Data, Xichen Mou
Estimation Problems For Pooled Data, Xichen Mou
Theses and Dissertations
In epidemiological applications, individual specimens (e.g., blood, urine, etc.) are often pooled together to detect the presence of disease or to measure the concentration level of a specific biomarker. Due to the advantage of cost efficiency, pooled data are also seen in diverse areas such as genetics, animal ecology, and environmental science. With pooled data, individual observations are masked and new statistical methods are needed to estimate characteristics such as disease prevalence, the underlying density function of a biomarker, etc. We focus on three estimation problems for pooled data. Chapters 2 and 3 propose nonparametric estimators for the density function …
Fitting A Complex Markov Chain Model For Firm And Market Productivity, Julia Ruth Valder
Fitting A Complex Markov Chain Model For Firm And Market Productivity, Julia Ruth Valder
Theses and Dissertations
This thesis develops a methodology of estimating parameters for a complex Markov chain model for firm productivity. The model consists of two Markov chains, one describing firm-level productivity and the other modeling the productivity of the whole market. If applicable, the model can be used to help with optimal decision making problems for labor demand. The need for such a model is motivated and the economical background of this research is shown. A brief introduction to the concept of Markov chains and their application in this context is given. The simulated data that is being used for the estimation is …
Uncertainty Estimation Of Deep Neural Networks, Chao Chen
Uncertainty Estimation Of Deep Neural Networks, Chao Chen
Theses and Dissertations
Normal neural networks trained with gradient descent and back-propagation have received great success in various applications. On one hand, point estimation of the network weights is prone to over-fitting problems and lacks important uncertainty information associated with the estimation. On the other hand, exact Bayesian neural network methods are intractable and non-applicable for real-world applications. To date, approximate methods have been actively under development for Bayesian neural networks, including but not limited to: stochastic variational methods, Monte Carlo dropouts, and expectation propagation. Though these methods are applicable for current large networks, there are limits to these approaches with either underestimation …
Semiparametric Statistical Estimation And Inference With Latent Information, Qianqian Wang
Semiparametric Statistical Estimation And Inference With Latent Information, Qianqian Wang
Theses and Dissertations
In Chapter 1, we predicted disease risk by transformation models in the presence of missing subgroup identifiers. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure. We further propose methods to …
Dimension Reduction For Classification With Many Covariates And Pathway Activity Level Estimation, Seungchul Baek
Dimension Reduction For Classification With Many Covariates And Pathway Activity Level Estimation, Seungchul Baek
Theses and Dissertations
The development of science and technology has enabled the use of more covariates. As a result, it has become more difficult to identify dependencies among many covariates. Dimension reduction provides an efficient way to handle this issue by summarizing the effect of covariates via a few linear combinations of covariates. In this dissertation, two methodologies for real-life problems are provided by using dimension reduction equipped with semiparametric theory. The use of semiparametrics allows maximal flexibility of the model by letting some features of the model completely unspecified, while we still enjoy the interpretability of the model through estimating the parameters …
Sensitivity To Distributional Assumptions In Estimation Of The Odp Thresholding Function, Wendy Jill Bunn
Sensitivity To Distributional Assumptions In Estimation Of The Odp Thresholding Function, Wendy Jill Bunn
Theses and Dissertations
Recent technological advances in fields like medicine and genomics have produced high-dimensional data sets and a challenge to correctly interpret experimental results. The Optimal Discovery Procedure (ODP) (Storey 2005) builds on the framework of Neyman-Pearson hypothesis testing to optimally test thousands of hypotheses simultaneously. The method relies on the assumption of normally distributed data; however, many applications of this method will violate this assumption. This thesis investigates the sensitivity of this method to detection of significant but nonnormal data. Overall, estimation of the ODP with the method described in this thesis is satisfactory, except when the nonnormal alternative distribution has …