Unified Hybrid Censoring Samples From Power Pratibha Distribution And Its Applications,
2025
Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
Unified Hybrid Censoring Samples From Power Pratibha Distribution And Its Applications, Mahmoud Mansour, Hebatalla H. Mohammad Dr, Khalaf S. Sultan Prof.
Basic Science Engineering
This paper suggests an extensive inferential method for the Power Pratibha Distribution (PPD) under Unified Hybrid Censoring Schemes (UHCSs), since there is a growing interest in flexible models in both reliability and service operations. This work studies the PPD model using standard Maximum Likelihood Estimation methods and modern Bayesian approaches too. Using a complex architecture, UHCS simulates tests more closely to what is done in practice than by using more basic censoring schemes. Using analysis, the probability and statistical ranges are carefully calculated for the parameters. Tests demonstrate that Bayesian estimation gives better results than many other methods for estimation, …
Welfare Implication Of Alternative Tax Rates Adjustment Policy In Nigeria: A Dsge Analysis,
2025
Department of Economics, Ahmadu Bello University, Zaria, Nigeria.
Welfare Implication Of Alternative Tax Rates Adjustment Policy In Nigeria: A Dsge Analysis, Umar B. Ibrahim, Isah F. Abubakar
CBN Journal of Applied Statistics (JAS)
This study sets out to determine the desirable policy adjustment in the tax rate for Nigeria that ensures the least welfare cost. A calibrated small open-economy New Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model of the Nigerian economy is applied to achieve this objective. Within this framework, we examined the impact of an increase in value-added tax (VAT) rate from 7.5 to 15 percent on key macroeconomic variables relative to the impact of an increase in company income tax (CIT) rate from 30 to 35 percent on macroeconomic variables. Furthermore, we examined the welfare costs of the increases in the …
Bayesian Statistics: Origins And Applications,
2025
CUNY New York City College of Technology
Bayesian Statistics: Origins And Applications, Evelyn Pulla
Publications and Research
Bayesian Statistics applies Bayes' Theorem to update beliefs through new evidence. In this project, I explored how Bayesian Statistics applies into real supporting decision-making under uncertainty. By solving problems using data, I was able to realize how prior knowledge and evidence collaborate to make better conclusions. The project also demonstrates how Bayesian reasoning corrects our intuition to make decisions based on logical reasoning. Through this project, I was able to learn why using probability to make informed decisions matters both in science and real life.
The Little Diagram That Could: Geometric Properties And Statistical Applications Of Persistence Diagrams In Topological Data Analysis,
2025
Washington University in St. Louis
The Little Diagram That Could: Geometric Properties And Statistical Applications Of Persistence Diagrams In Topological Data Analysis, Eugene Kler
McKelvey School of Engineering Graduate Student Theses & Dissertations
Topological Data Analysis (TDA) is a collection of techniques for data analysis that leverages topological invariants of spaces formed from data points. These methods excel at extracting useful information from noisy or sparse data, making them attractive to many mathematicians, statisticians, and scientists. In this thesis, we explore TDA on three fronts: algebraic foundations, statistical applications, and metric properties. Throughout, the central object of study is the Persistence Diagram (PD), a summary of the changes in homology that occur as one builds simplicial complexes from the data by increasing a parameter.
Evaluating The Performance Of Bayesian Removal Models For Estimating Population Density And Detecting Trends With Variable Detection Probability,
2025
University of New Mexico - Main Campus
Evaluating The Performance Of Bayesian Removal Models For Estimating Population Density And Detecting Trends With Variable Detection Probability, David R. Stewart
Mathematics & Statistics ETDs
Removal models have long been used to estimate population abundance by progressively capturing and removing individuals from a closed population. These models provide a valuable tool for ecological monitoring, but their accuracy depends heavily on assumptions about detection probability, which may decline over successive sampling passes. Traditional removal models assume constant detection probabilities, an assumption that is often violated in real-world applications. This thesis aims to advance hierarchical Bayesian models by accounting for variable detection probabilities, improving the reliability of abundance estimates and trend detection. By integrating simulation-based analyses with empirical data from Lahontan Cutthroat Trout (Oncorhynchus clarkia henshawi …
Quaternary Subsurface Characterization Of The Mississippi River Valley Alluvial Aquifer: Insights From Interval Kriging And Airborne Em-Borehole Data Integration,
2025
Louisiana State University and Agricultural and Mechanical College
Quaternary Subsurface Characterization Of The Mississippi River Valley Alluvial Aquifer: Insights From Interval Kriging And Airborne Em-Borehole Data Integration, Yuqi Song
LSU Doctoral Dissertations
The Pleistocene period significantly contributed to the formation of alluvial aquifer worldwide. These productive aquifers are crucial for domestic, industrial, and agricultural water supplies. The Mississippi River Valley alluvial aquifer (MRVA), a principal aquifer in the U.S., is crucial for national food security and global agricultural supply. This study aims to characterize the subsurface architecture of the MRVA, thereby enhancing fundamental understanding of sedimentological processes involved in the genesis of glacio-fluvial aquifers worldwide. The research objectives are threefold: (1) to provide a detailed characterization of the MRVA; (2) to develop 3D geostatistical methods for geological modeling; and (3) to develop …
On The Gumbel-Weibull{Cauchy} Distribution,
2025
Marshall University
On The Gumbel-Weibull{Cauchy} Distribution, Jennifer D. Pippin
Theses, Dissertations and Capstones
Developing new statistical distributions and seeking higher flexibility in modeling different shapes of data remain a strong emphasis in research. The T-R{Y } framework, introduced in [3], utilizes three statistical distributions in order to generate a new distribution. Many research papers appeared in literature to develop distributions based on the T-R{Y } framework. In this thesis, a member of the T-R{Y } framework, namely the Gumbel-Weibull{Cauchy} (GWC), is introduced. Statistical properties of the GWC are studied, such as the quantile function, the hazard function, transformations, Shannon entropy, the …
Theoretical Analysis Of Cnns For Automatic Seizure Detection In Eeg Signals,
2025
University of Central Florida
Theoretical Analysis Of Cnns For Automatic Seizure Detection In Eeg Signals, Jackson T. Small
Honors Undergraduate Theses
Epilepsy is a common brain disorder where neurons in the brain rapidly fire, causing recurring seizures. The brain activity during a seizure can be detected by electroencephalogram (EEG) signals; however, this process is not only labor-intensive and time-consuming but is also subject to inter-rater variability, with a study showing only moderate agreement when diagnosing patients, even among experts. Convolutional Neural Networks (CNNs) are often proposed to detect seizures automatically, achieving high performance. The focus on performance comes at a cost of losing interpretability, leaving the model as effective but seen as a ’black box’. This thesis confronts the interpretability knowledge …
“Regression To The Mean”: The Confluence Of Eugenics And Statistics In The 19th And 20th Centuries,
2025
Pomona College
“Regression To The Mean”: The Confluence Of Eugenics And Statistics In The 19th And 20th Centuries, Emrys G. King
Pomona Senior Theses
The work of this thesis is twofold — first, qualitatively characterizing the confluence between the British eugenics and statistics movements in the late 19th and early 20th centuries, and second, quantitatively analyzing the effect of this foundation on pedagogical materials in the growing field of statistics between 1880 and 1970. Towards the first goal, the history of the method of least squares, state statistics, and positive and negative eugenics are outlined, followed by a close reading of the foundational texts authored by Francis Galton and Karl Pearson that introduced linear regression. Towards the latter goal, English-language statistics textbooks published between …
Further Results On Learning Quantum Measurement Classes: Quantum Pac Model For Povm Hypothesis Classes,
2025
University at Albany, State University of New York
Further Results On Learning Quantum Measurement Classes: Quantum Pac Model For Povm Hypothesis Classes, Arka Prabha Das
Electronic Theses & Dissertations (2024 - present)
This thesis investigates the problem of learning from quantum systems, where each example consists of a quantum state paired with a classical outcome. The task centers on choosing an effective measurement rule from a fixed set to enable accurate prediction of the classical outcome from the quantum state. A central focus lies in understanding whether joint measurement strategies that cannot be separated into local operations offer a real benefit in terms of the number of examples needed for successful learning. We examine conditions under which a non-separable measurement within a given hypothesis class achieves strictly better sample complexity bounds compared …
Theoretical Foundations And Applied Performance Of Periodicity-Aware Imputation: Variable Bandpass Block Bootstrap Methods For Incomplete Time Series,
2025
University at Albany, State University of New York
Theoretical Foundations And Applied Performance Of Periodicity-Aware Imputation: Variable Bandpass Block Bootstrap Methods For Incomplete Time Series, Asmaa Ahmad
Electronic Theses & Dissertations (2024 - present)
Time series data are prevalent across a wide range of disciplines, including health surveillance, public policy, and environmental monitoring. In the presence of underlying cyclical patterns, the integrity of time series analysis depends critically on the ability to detect, model, and impute structured missing data without compromising the temporal structure. This dissertation introduces and validates a novel imputation framework that integrates the Variable Bandpass Periodic Block Bootstrap (VBPBB) into multiple imputation procedures, improving the accuracy, robustness, and interpretability of time series models under high rates of missingness and noise. The overarching goal of this dissertation was to develop and evaluate …
Predictive Modeling For Healthcare Data Using Nonlinear Bayesian Methods,
2025
Illinois State University
Predictive Modeling For Healthcare Data Using Nonlinear Bayesian Methods, Prince Kofi Asare
Theses and Dissertations
Unplanned hospital readmissions represent a significant challenge for healthcare systems, contributing to substantial financial burdens and highlighting gaps in patient care coordination. In the U.S., approximately 20% of Medicare beneficiaries are readmitted within 30 days, costing billions annually. Social determinants of health, such as income, housing stability, and social support, account for up to 80% of health outcomes, yet their integration into predictive models remains underexplored. This study introduces a novel Bayesian framework for predicting 30-day readmission risk, combining Gaussian Process models with spike-and-slab priors and Bayesian Lasso regression with Laplace priors. Utilizing Markov Chain Monte Carlo methods, the approach …
Learning Problems Related To Stochastic Differential Equations,
2024
Louisiana State University and Agricultural and Mechanical College
Learning Problems Related To Stochastic Differential Equations, Jinpu Zhou
LSU Doctoral Dissertations
Stochastic differential equations (SDEs) are essential for modeling systems influenced by both deterministic dynamics and random fluctuations, with applications in a wide variety of disciplines. This thesis develops a Bayesian framework for nonparametric learning in SDEs, addressing key challenges in inference, particularly when dealing with complex systems and incomplete data. The thesis begins by establishing a theoretical foundation in optimization over Hilbert spaces, including a generalized representer theorem to address infinite-dimensional optimization problems encountered in nonparametric inference. Building on this, we introduce a Bayesian framework with shrinkage priors to learn drift functions from high-frequency data. Bayesian approach incorporates low-cost sparse …
A Uniformly Most Powerful Test For The Mean Of A Beta Distribution,
2024
Stephen F Austin State University
A Uniformly Most Powerful Test For The Mean Of A Beta Distribution, Richard Ntiamoah Kyei
Electronic Theses and Dissertations
The beta distribution is used in numerous real-world applications, including areas such as manufacturing (quality control) and analyzing patient outcomes in health care. It also plays a key role in statistical theory, including multivariate analysis of variance (MANOVA) and Bayesian statistics. It is a flexible distribution that can account for many different characteristics of real data. To our surprise, there has been very little work or discussion on performing statistical hypothesis testing for the mean when it is reasonable to assume that the population is beta distributed. Many analysts conduct traditional analyses using a t-test or nonparametric approach, try transformations, …
Simulation Study On Confidence Interval Estimation For Standard Deviation With Non-Normal Distributions,
2024
Stephen F. Austin State University
Simulation Study On Confidence Interval Estimation For Standard Deviation With Non-Normal Distributions, Theophilus Oppong Kyeremeh
Electronic Theses and Dissertations
This study explores innovative approaches to constructing confidence intervals for the population standard deviation, σ, in non-normal data scenarios. While the sample standard deviation, s, is widely used, its reliability is compromised when dealing with skewed or heavy-tailed distributions and exhibits sensitivity to outliers. Our research addresses these limitations by investigating alternative estimation methods that offer greater robustness and accuracy.
Bayesian Approaches In Multi-State Markov Models And High Dimensional Time-To-Event Data.,
2024
University of Louisville
Bayesian Approaches In Multi-State Markov Models And High Dimensional Time-To-Event Data., Yuchen Han
Electronic Theses and Dissertations
This dissertation consists of two projects. The first one involves nonparametric methods on Continuous Time Markov Chains (CTMCs). The second one is centered around Bayesian shrinkage models for detecting prognostic and predictive biomarkers in high-dimensional clinical data. Both these projects build on methods from across the frequentist and Bayesian paradigm to offer novel solutions. In the first project, we aim to model the nonlinear effects of continuous variables within multistate framework in a non-parametrically by appealing to the rich mathematical framework of Reproducing Kernel Hilbert Spaces (RKHS). Then we adapted the classical Representer Theorem to penalized (squared norm) log-likelihood which …
Contributions To Nonparametric Testing In Clustered Data,
2024
Old Dominion University
Contributions To Nonparametric Testing In Clustered Data, Hasika Kalani Wickrama Senevirathne
Mathematics & Statistics Theses & Dissertations
Clustered data refers to a specific kind of correlated data where units within the same cluster are correlated while units from different clusters are independent. The number of units in each cluster, known as the cluster size, can be associated with the cluster’s outcome. This is known as the informative cluster size (ICS) and affects the inference drawn from clustered data. Recently, a hypothesis testing method has been developed to detect the presence of ICS. However, considering ICS alone may not be sufficient when comparing outcomes across multiple groups of units within clustered data. The size of a group within …
Comparison Of Value At Risk Using Historical And Monte Carlo Methods On Pt Xyz Stock Portofolio,
2024
Program Pendidikan Vokasi, Universitas Indonesia
Comparison Of Value At Risk Using Historical And Monte Carlo Methods On Pt Xyz Stock Portofolio, Eka Fitriani, Yulial Hikmah, Ira Rosianal Hikmah
Jurnal Administrasi Bisnis Terapan
One way to achieve profits in a company is through investment activities. However, everything has risks. Investing can also be risky. Therefore, the relationship between risk and investment is important because it will influence the determination of investment selection. The problem faced by investors is choosing an efficient portfolio, or a portfolio that provides the smallest risk. This risk can be done by measuring risk, one of which is using the Value at Risk (VaR) measure. Measurement using Value at Risk has several methods that are quite popular, namely the Historical Method, Variance-Covariance, and Monte Carlo. In this research, the …
Comparing The Preparation Of Youth Services Librarians To Their On-The-Ground Experiences: A Grounded Theory Study Incorporating Criticism And Connoisseurship,
2024
University of Denver
Comparing The Preparation Of Youth Services Librarians To Their On-The-Ground Experiences: A Grounded Theory Study Incorporating Criticism And Connoisseurship, Anne Holland
Electronic Theses and Dissertations
The purpose of this study was to better understand the on-the-ground preparation of youth services librarians, in contrast to their professional training in Master’s of Library Science (MLIS) programs. Classic Grounded Theory was the predominant methodology for this qualitative study, and elements of Criticism and Connoisseurship were also utilized. Document review, interviews, and journaling activities with ten participants were the primary methods of data collection. Key findings from this dissertation include a grounded theory explaining the current state of preparation for youth services librarianship, and multiple avenues for further study.
Trade Liberalization, Non-Oil Export And Economic Growth In Nigeria,
2024
Department of Economics, Benue State University, Makurdi, Nigeria.
Trade Liberalization, Non-Oil Export And Economic Growth In Nigeria, Jerome T. Andohol, Terhemen Tarzoor, Dennis T. Nomor
CBN Journal of Applied Statistics (JAS)
The study examines the impact of trade liberalization and non-oil exports on economic growth in Nigeria from 1986 to 2021. The study utilizes an autoregressive distributed lag model and found the combined effect of trade liberalization and non-oil exports to be positive and statistical significant. While trade liberalization alone may have negative consequences, its synergy with a robust non-oil export can drive sustainable economic growth. The study recommends that strategies to enhance non-oil exports should be encouraged to support the effectiveness of trade liberalization in promoting growth.
