Statistics and Probability Commons^™

Lévy Processes: Characterizing Volcanic And Financial Time Series, Peter Kwadwo Asante

Open Access Theses & Dissertations

In this work, we use the Diffusion Entropy Analysis (DEA) to analyze and detect the scaling properties of time series from both emerging and well established markets as well as volcanic eruptions recorded by a seismic station, both financial and volcanic time series data are known to have high frequencies (i.e they are collected at an extremely fine scale). The objective is to determine the characterization i.e whether they follow a Gaussian or Lévy distribution. If they do follow a Lévy distribution we are then interested in finding if they are characterized by a Lévy walk which has a finite …

Predicting Stochastic Volatility For Extreme Fluctuations In High Frequency Time Series, Md Al Masum Bhuiyan

Open Access Theses & Dissertations

This work is devoted to the study of modeling high frequency time series including extreme fluctuations. As the high frequency data are collected at extremely fine scales, the fluctuations can capture the dynamics of data that evolve over time. A class of volatility models with time-varying parameters is used to forecast the volatility in a stationary condition at different lags. The modeling of stationary time series with consistent properties facilitates prediction with much certainty.

A large set of high frequency financial returns, closing prices of stock markets, high magnitudes of seismograms generated by the natural earthquakes, and the mining explosions …

Robust Estimation And Inference For Multivariate Financial Data, Afua Kwakyewaa Amoako Dadey

Open Access Theses & Dissertations

Predicting and forecasting are routine day-to-day activities that guide us in making the best possible choices. They play an integral role in financial analysis. A lot of work has been done on one dimensional geometric Brownian motion (GBM) in stock price prediction. In this line of work, we focus mainly on how to use the one dimensional geometric Brownian motion and the multidimensional geometric Brownian motion in predicting future stock prices. There are several stock prices in the financial market and the multidimensional geometric Brownian motion gives a more realistic prediction compared to the one dimensional GBM. The reason being …

General Penalized Logistic Regression For Gene Selection In High-Dimensional Microarray Data Classification, Derrick Kwesi Bonney

Open Access Theses & Dissertations

High-dimensional data has become a major research area in the field of genetics, bioinformatics and bio-statistics due to advancement of technologies. Some common issues of modeling high-dimensional gene expression data are that many of the genes may not be relevant. Also, reducing the dimensions of the data using penalized logistic regression is one of the major challenges when there exists a high correlation among genes. High-dimension data correspond to the situation where the number of variables is greater or larger than the number of observations. Gene selection proved to be an effective way to improve the results of many classification …

Utilizing Design Structure For Improving Design Selection And Analysis, Ahlam Ali Alzharani

Theses and Dissertations

Recent work has shown that the structure for design plays a role in the simplicity or complexity of data analysis. To increase the knowledge of research in these areas, this dissertation aims to utilize design structure for improving design selection and analysis. In this regard, minimal dependent sets and block diagonal structure are both important concepts that are relevant to the orthogonality of the columns of a design. We are interested in finding ways to improve the data analysis especially for active effect detection by utilizing minimal dependent sets and block diagonal structure for design.

We introduce a new classification …

Applications Of Ornstein-Uhlenbeck Type Stochastic Differential Equations, Osei Kofi Tweneboah

Open Access Theses & Dissertations

In this Dissertation, we show with plausible arguments that the Stochastic Differential Equations (SDEs) arising on the superposition and coupling system of independent Ornstein-Uhlenbeck process is a new method available in modern literature that takes the properties and behavior of the data into consideration when performing the statistical analysis of the time series.

The time series to be analyzed is thought of as a source of fluctuations, and thus we need a model that takes this behavior into consideration when performing such analysis. Most of the standard methods fail to take into account the physical behavior of the time series, …

Spatially Adaptive Estimation Of Spectrum, Yi None Xie

Open Access Theses & Dissertations

When analyzing a stationary time series, one of the questions we are often interested in is how to estimate its spectrum. Many approaches have been proposed to this end. Most are focused on smoothing the periodogram using a single smoothing parameter across all Fourier frequencies. In this paper, we smooth the log periodogram by placing a spatially adaptive prior called the dynamic shrinkage prior, so that varying degrees of smoothing may be applied to different intervals of Fourier frequencies, resulting in less biased estimates of the spectrum. Further research will extend this approach to spectral estimation for nonstationary time series.

Psychometric Analysis Of Forensic Examiner Behavior, Amanda Luby, A. Mazumder, B. Junker

Mathematics & Statistics Faculty Works

Forensic science often involves the comparison of crime-scene evidence to a known-source sample to determine if the evidence and the reference sample came from the same source. Even as forensic analysis tools become increasingly objective and automated, final source identifications are often left to individual examiners’ interpretation of the evidence. Each source identification relies on judgements about the features and quality of the crime-scene evidence that may vary from one examiner to the next. The current approach to characterizing uncertainty in examiners’ decision-making has largely centered around the calculation of error rates aggregated across examiners and identification tasks, without taking …

Evaluating The Accuracy Of Firearm Examiner Conclusions Using Cartridge Case Reproductions, Eric Freeman Law

Graduate Theses, Dissertations, and Problem Reports

The forensic science pattern comparison areas, including fingerprints, footwear, and firearms, have been criticized for their subjective nature. While much research has attempted to move these disciplines to more objective methods, a majority of examiners are still coming to conclusions based on their own training and experience. To compare accuracy between examiners, a method called double-casting was used in this study to create plastic cartridge case reproductions. In the first part of this study, double-cast accuracy was evaluated using two automated comparison systems to quantify the similarity. It was determined that the double-casting method used here produces accurate reproductions with …

Association Between Baseline Abundance Of Peptoniphilus, A Gram-Positive Anaerobic Coccus, And Wound Healing Outcomes Of Dfus, Kyung R. Min, Adriana Galvis, Katherine L. Baquerizo Nole, Rohita Sinha, Jennifer Clarke, Robert S. Kirsner, Dragana Ajdic

Department of Statistics: Faculty Publications

Diabetic foot ulcers (DFUs) lead to nearly 100,000 lower limb amputations annually in the United States. DFUs are colonized by complex microbial communities, and infection is one of the most common reasons for diabetes-related hospitalizations and amputations. In this study, we examined how DFU microbiomes respond to initial sharp debridement and off- loading and how the initial composition associates with 4 week healing outcomes. We employed 16S rRNA next generation sequencing to perform microbial profiling on 50 sam- ples collected from 10 patients with vascularized neuropathic DFUs. Debrided wound sam- ples were obtained at initial visit and after one week …

Representation Of Features As Images With Neighborhood Dependencies For Compatibility With Convolutional Neural Networks, Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated …

Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh

Department of Statistics: Faculty Publications

Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist …

In Praise Of Partially Interpretable Predictors, Tri Le, Bertrand S. Clarke

Department of Statistics: Faculty Publications

Often there is an uninterpretable model that is statistically as good as, if not better than, a successful interpretable model. Accordingly, if one restricts attention to interpretable models, then one may sacrifice predictive power or other desirable properties. A minimal condition for an interpretable, usually parametric, model to be better than another model is that the first should have smallermean-squared error or integratedmean-squared error.We show through a series of examples that this is often not the case and give the asymptotic forms of a variety of interpretable, partially interpretable, and noninterpretable methods. We find techniques that combine aspects of both …

Tumor Ablation Due To Inhomogeneous Anisotropic Diffusion In Generic Three-Dimensional Topologies, Erdi Kara, Aminur Rahman, Eugenio Aulisa, Souparno Ghosh

Department of Statistics: Faculty Publications

In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal …

Statistical Downscaling With Spatial Misalignment: Application To Wildland Fire Pm2.5 Concentration Forecasting, Suman Majumder, Yawen Guan, Brian J. Reich, Susan O’Neill, Ana G. Rappold

Department of Statistics: Faculty Publications

Fine particulate matter, PM_2.5, has been documented to have adverse health effects, and wildland fires are a major contributor to PM_2.5 air pollution in the USA. Forecasters use numerical models to predict PM_2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and …

Joint Simulation Of Continuous And Categorical Variables For Mineral Resource Modeling And Recoverable Reserves Calculation, Sentle Augustinus Hlajoane

Dissertations, Master's Theses and Master's Reports

Spatial variability and uncertainty of continuous variables (grade) and categorical variables (rock-types) in mineral evaluation significantly impact the economics of mining projects. The conventional approach of simulating grades using deterministic rock- types is problematic since spatial variability, and uncertainty of grades at rock-type contacts are not well captured in deposits where the grade changes gradually between rock-types. Therefore, jointly simulating these variables can improve confidence (reduce uncertainty) in a resource model. Also, resource classification and recoverable reserve calculation can significantly improve the understanding of the deposit and its economic viability. This research utilized the Plural-Gaussian geostatistical simulation to jointly simulate …

Novel Random Forest Methods And Algorithms For Autism Spectrum Disorders Research, Afrooz Jahedi

CGU Theses & Dissertations

Random Forest (RF) is a flexible, easy to use machine learning algorithm that was proposed by Leo Breiman in 2001 for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Its superior prediction accuracy has made it the most used algorithms in the machine learning field. In this dissertation, we use the random forest as the main building block for creating a proximity matrix for multivariate matching and diagnostic classification problems that are used for autism research (as an exemplary application). In observational studies, matching is used to optimize the balance …

A Multinational Study Of The Etiology And Clinical Teleology Of Moral Evaluations Of Patient Behaviors, Anna Yu Lee

CGU Theses & Dissertations

This dissertation is a collection of four studies which collectively explore a hypothesized construct of ‘moral evaluation of patient behaviors’ (MEPB) as a driver of health professionals’ readiness to interact humanistically with their patients. In these studies, ‘humanistic interactions’ refer to the non-technical, intangible skills and factors of clinical competence; the factors specifically explored in these studies were compassion toward patients, self-efficacy for treating patients, and optimism toward patient treatment. For the purpose of specificity, all factors were examined as they pertained to patients with substance use disorders. Survey data from a convenience sample of 524 health professionals (i.e. physicians, …

Enhancing Models And Measurements Of Traffic-Related Air Pollutants For Health Studies Using Dispersion Modeling And Bayesian Data Fusion, Stuart A. Batterman, Veronica J. Berrocal, Chad Milando, Owais Gilani, Saravanan Arunachalam, K. Max Zhang

Faculty Journal Articles

Research Report 202 describes a study led by Dr. Stuart Batterman at the University of Michigan, Ann Arbor and colleagues. The investigators evaluated the ability to predict traffic-related air pollution using a variety of methods and models, including a line source air pollution dispersion model and sophisticated spatiotemporal Bayesian data fusion methods. Exposure assessment for traffic-related air pollution is challenging because the pollutants are a complex mixture and vary greatly over space and time. Because extensive direct monitoring is difficult and expensive, a number of modeling approaches have been developed, but each model has its own limitations and errors.

Dr. …

Nonparametric Tests Of Lack Of Fit For Multivariate Data, Yan Xu

Theses and Dissertations--Statistics

A common problem in regression analysis (linear or nonlinear) is assessing the lack-of-fit. Existing methods make parametric or semi-parametric assumptions to model the conditional mean or covariance matrices. In this dissertation, we propose fully nonparametric methods that make only additive error assumptions. Our nonparametric approach relies on ideas from nonparametric smoothing to reduce the test of association (lack-of-fit) problem into a nonparametric multivariate analysis of variance. A major problem that arises in this approach is that the key assumptions of independence and constant covariance matrix among the groups will be violated. As a result, the standard asymptotic theory is not …