OUR@Oakland
OUR@Oakland is Oakland University's institutional repository maintained by the University Libraries. The aim of OUR@Oakland is to collect, organize, and showcase the scholarship, creative work, and archival and special collections created by or affiliated with the Oakland University community.
Communities in DSpace
Select a community to browse its collections.
- Material related to administrative and teaching activities of the university's academic departments and support units
- Publications created by the Association for Interdisciplinary Studies (formerly Association for Integrative Studies)
- Scholarship produced by OU faculty, including publications, presentations, and research data
- Programs from performances and events presented at the Meadow Brook Music Festival, beginning in the summer of 1964
- Historic university materials documenting the foundation and operations of the university and its operations, and collections devoted to specific subjects
- Catalogs from various exhibitions presented at the Oakland University Art Gallery
- Digitized collections of The Oakland Press newspaper and its predecessors (specifically The Pontiac Press)
- Theses and dissertations by Oakland University students, searchable by discipline
- Scholarship developed by Oakland University undergraduate students working in various disciplines
Recent Submissions
OUWB Commencement Address, May 10, 2024
(Oakland University, 2024-05-10) Pescovitz, Ora Hirsch
Welcome Kevin Corcoran as Interim Provost, May 2024
(Oakland University, 2024-05-01) Pescovitz, Ora Hirsch
OU Employees Honored for Years of Service, May 6, 2024
(Oakland University, 2024-05-06) Pescovitz, Ora Hirsch
Point Estimators and Confidence Intervals Under Sequential Sampling Strategies with Applications
(2024-01-01) Alanazi, Ibtihal Hamoud; Hu, Jun; Drignei, Dorin; Perla, Subbaiah; Yiu So, Hon; Li, Li
Statistical inference is the process of making informed decisions about a larger population by analyzing a smaller group of data collected with some form of sampling. In many statistical inference problems, where some prescribed accuracy is desired, the required sample size often depends on unknown population parameters and thus remains unknown. Then, it is necessary to conduct a sequential sampling procedure, where an experimenter takes one observation at a time successively until a predefined stopping rule is satisfied. This thesis involves sequential sampling procedures dealing with three statistical inference problems. These are (i) bounded variance point estimation (BVPE) of a function of the scale parameter in a gamma distribution with known shape parameter; (ii) fixed-width confidence interval (FWCI) estimation for comparing two independent Bernoulli proportions; and (iii) fixed-accuracy confidence interval (FACI) estimation for the shape parameter of a Weibull distribution based on record data. In the first research problem, given a gamma population with known shape parameter α, we develop a general theory for estimating a function g(·) of the scale parameter β with bounded variance. We begin by defining a sequential sampling procedure with g(·) satisfying some desired condition in proposing the stopping rule, and show the procedure enjoys appealing asymptotic properties. After these general conditions, we substitute g(·) with specific functions including the gamma mean, the gamma variance, the gamma rate parameter, and a gamma survival probability as four possible illustrations. For each illustration, Monte Carlo simulations are carried out to justify the remarkable performance of our proposed sequential sampling procedure. This is further substantiated with a real data study on the weights of newborn babies. In the second research problem, we are interested in the proportions of a common characteristic possessed by two independent dichotomous populations, denoted by P1 and P2. We propose sequential sampling procedures for constructing FWCIs to compare the magnitude of P1 and P2 based on the log transformation and the logit transformation, respectively, which are followed by Monte Carlo simulations. We then implement these sequential sampling procedures to solve a real-world problem of mobile games A/B testing. In the third research problem, we focus on utilizing the record data to estimate the shape parameter of a two-parameter Weibull population, which is widely used in lifetime data analysis. A sequential sampling procedure is developed for constructing a FACI for the Weibull shape parameter β, no matter whether the scale parameter α is known or unknown.
Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects
(2024-01-01) Tran, Nguyen Quynh Huong; Khattree, Ravindra; Drignei, Dorin; Li, Li; Roy, Anuradha; So, Hon Yiu
This dissertation considers three critical aspects of modern statistical analyses and machine learning, namely, (i) addressing the challenges posed by assessing the distributional assumptions of a univariate dataset, (ii) constructing graphical tests for the multivariate normality assumption, and (iii) exploring new algorithms for solving group classification problems, especially when the distance-based methods are not applicable. First, we focus on the development and evaluation of graphical statistical tests for univariate datasets, aiming to assess any specific distributional assumption. Examination of normality is given special emphasis. Recognizing the impact of outliers on the normality assumption, this study also incorporates outlier detection methodologies and provides some graphical tools for their identification. T4 plot is introduced as an additional effective tool for this purpose. Examination of multivariate normality is more challenging as in this case nonnormality may exhibit or mask itself in many different ways. In the second part, we thus emphasize graphical assessments of the multivariate normality assumption. This is done via MT3 and MT4 plots based on the derivatives of the cumulant generating function. The final segment of the dissertation shifts the discussion towards machine learning algorithms devised specifically for group classification problems. This involves the exploration of new methodologies that address the challenges inherent in classification and discrimination within complex datasets where other standard methods based on the classification of individual observation may not be very effective. For this we rely on the eigenstructures of the data. In the process, we also address the problem of dimensionality reduction. The problem of selection of copulas can be viewed as a corollary of the group classification problem. This has also been discussed in a separate chapter.
Uncertain Interval Systems with Application to Separately Excited DC Motor and DC-DC Converters
(2024-01-01) Kintali, Narendra; Cheok, Ka C; Cesmelioglu, Aycil; Llamocca, Daniel; Sangeorzan, Brian P
Electric drive systems in automobiles, aircraft, and maritime crafts havesignificantly advanced due to changes in hardware and software applications. Drive systems consisting of multidisciplinary subsystems often post non-trivial control problems that must be overcome. For example, redundant input systems related to a separately excited DC motor (SEDCM) or highly varying gains related to DC-DC converters. This thesis introduces a novel approach utilizing an optimization scheme to transform the redundant input process into an uncertain interval system. An armature voltage minimization scheme was developed for the SEDCM to address these redundancies and uncertainties. The comprehensive analysis and feedback control design for an uncertain interval optimal redundant input system is a significant departure from traditional methods, such as Root Locus. The Kharitonov stability criterion is used to analyze the interval systems and determine the parameter boundaries for the controller gains. At the same time, the Root-Locus method is employed to visualize the stable regions of the controller parameters. Next, the Lyapunov stability criterion-based adaptive controller is designed to guarantee stability and tracking for the optimal redundant input systems. For illustration, a PI-controlled separately excited DC motor (SEDCM) will be used. The proposed uncertain interval technique is extended to analyze and design a robust PI controller for DC-DC power electronic converters. The results unify the control design for the buck, boost, and buck-boost converters.
Multi-Objective Optimal Routing Schemes for High Mobility Vehicular Networks: A Path to Efficiency
(2024-01-01) Alolaiwy, Muhammad Musaad M; Zohdy, Mohamed A; Kaur, Amanpreet; Louis, Steven; Rugge, Erica
Technological advancements in wireless communication networks have enabled futuristic applications that support massive device access and pervasive communications. Moreover, vehicular networks in Intelligent Transportation Systems (ITS) require efficient communication and routing schemes to accommodate Electric and Flying Vehicles (EnFVs). A centralized approach is often flawed due to the high mobility and dynamic nature of device movement. Therefore, efficient and novel solutions are required to provide connectivity to EnFVs without any centrally connected unit. Our main focus in this study is to enable a faster, better, and improved communication platform for EnFVs, support a wide range of applications.
Farewell to Provost Rios-Ellis, May 14, 2024
(Oakland University, 2024-05-14) Pescovitz, Ora Hirsch
SECS Research Building Opening, May 20, 2024
(Oakland University, 2024-05-20) Pescovitz, Ora Hirsch
A Rite of Passage Commencement Message to Undergraduates, Apr. 2024
(Oakland University, 2024-04-01) Pescovitz, Ora Hirsch