Statistics and Probability Commons^™

(R2060) An M/G/1 Retrial Queue With Recurrent Customers, General Retrial Times And Working Vacation, S. Pazhani Bala Murugan, A. Jeba Pauline Veronica

Applications and Applied Mathematics: An International Journal (AAM)

In this article, we analyze an M/G/1 retrial queue with recurrent customers, general retrial times and working vacation. In this model, we use two types of customers. They are recurrent customers who rejoin the retrial queue after completion of their service and transit customers who leaves the system once their service complete. All the service times and retrial times for transit customers follows general distribution, retrial time for recurrent customers and working vacation time of the server are assumed to have an exponential distribution and also service time of the recurrent customers follows general distribution. We have used working vacation …

Models Of Shared Care For The Management Of Psychotic Disorder After First Diagnosis In Ontario., Joshua C. Wiener, Rebecca Rodrigues, Jennifer N S Reid, Kelly K. Anderson

Epidemiology and Biostatistics Publications

OBJECTIVE: To describe the provision of care for young people following first diagnosis of psychotic disorder.

DESIGN: Retrospective cohort study using health administrative data.

SETTING: Ontario.

PARTICIPANTS: People aged 14 to 35 years with a first diagnosis of nonaffective psychotic disorder in Ontario between 2005 and 2015 (N=39,449).

MAIN OUTCOME MEASURES: Models of care, defined by psychosis-related service contacts with primary care physicians and psychiatrists during the 2 years after first diagnosis of psychotic disorder.

RESULTS: During the 2-year follow-up period, 29% of the cohort received only primary care, 30% received only psychiatric care, and 32% received both primary and …

Exploration And Statistical Modeling Of Profit, Caleb Gibson

Undergraduate Honors Theses

For any company involved in sales, maximization of profit is the driving force that guides all decision-making. Many factors can influence how profitable a company can be, including external factors like changes in inflation or consumer demand or internal factors like pricing and product cost. Understanding specific trends in one's own internal data, a company can readily identify problem areas or potential growth opportunities to help increase profitability.

In this discussion, we use an extensive data set to examine how a company might analyze their own data to identify potential changes the company might investigate to drive better performance. Based …

Generalized Ratio-Product Cum Regression Variance Estimator In Two-Phase Sampling, Isah Muhammad

CBN Journal of Applied Statistics (JAS)

This study develops a flexible and efficient generalized ratio-product cum regression type estimator of population variance utilizing auxiliary variable in two-phase sampling that incorporates the properties of ratio-type and product-type estimators. The properties of the estimator were derived using first order approximation. The theoretical conditions under which the precision and the flexibility of the estimator is better than some classical estimators are also provided. Empirical evidence from five real datasets suggests that the proposed estimator outperforms the classical variance, ratio variance, product, and exponential ratio type estimators in terms of precision and efficiency. The estimator can be utilized to provide …

Modelling The Naira Exchange Rate Dependence Using Static And Time-Varying Copula, Kabir Katata

CBN Journal of Applied Statistics (JAS)

This paper examines the dependence structure of different currencies versus the Nigerian Naira using constant and time-varying copula. Daily Naira/USD, Naira/Yuan, Naira/Pound, and Naira/Euro exchange rates from 23 December 2011 to 12 May 2020 were utilised. We fitted eight constant and time-varying copula families using the exchange rate standardised residuals. The study finds that the Naira exchange rate may be estimated with student t-copula, Symmetrized Joe-Clayton (SJC), or Rotated Gumbel copula models and Autoregressive (AR)– Glosten Jagannathan RunkleGeneralized Autoregressive Conditional Heteroscedastic (GJR-GARCH) (1,1) models with skewed t residuals for margins. The Naira exchange rate returns is timevarying, tail-dependent, and asymmetric. …

External Debt Pass-Through To Inflation In Nigeria, Emmanuel A. Asue, James V. Ikyaator

CBN Journal of Applied Statistics (JAS)

This study examines external debt pass-through to inflation in Nigeria using annual data from 1981 to 2020 based on structural vector autoregressive (SVAR) model. The results reveal that an increase in external debt service leads to a significant depreciation of the exchange rate, which leads to a contemporaneous increase in inflation, while the direct response of inflation to external debt is statistically not significant. The impulse response confirms these results. The forecast error variance decomposition depicts that future values of official exchange rate depend on external debt, inflation and external debt service. The study recommends that the Nigerian government should …

Financial Inclusion And Poverty Reduction In Nigeria: The Role Of Microfinance Institutions, Okwudili W. Ugwuoke, Oliver E. Ogbonna, Aye-Agele Freeman

CBN Journal of Applied Statistics (JAS)

This study investigates the role of microfinance institutions as a vehicle for driving financial inclusion and alleviating poverty in Nigeria using the EFinA 2018 household survey data. The probit model, propensity score matching, and average treatment effect methods are applied for the analyses. The study finds that financial inclusion driven by access to, and usage of products/services provided by microfinance institutions reduces poverty. The study recommends among others the need for increased access to microfinance products/services and an integrated poverty reduction policies that identifies microfinance institutions as a critical enabler

Applications Of Causal Inference Methods For The Estimation Of Effects Of Bone Marrow Transplant And Prescription Drugs On Survival Of Aplastic Anemia Patients, Yesha M. Patel

Computational and Data Sciences (PhD) Dissertations

This dissertation provides an in-depth exploration into the treatment effectiveness for aplastic anemia using causal inference methods, structured around three pivotal research papers. Each paper contributes to a nuanced understanding of treatment impacts, specifically focusing on bone marrow transplantation (BMT) and prescription drugs, and the identification of optimal treatment strategies.

The first paper, "Causal Inference Analysis for Assessing the Effect of Bone Marrow Transplantation on the One-Year Survival of Adult and Pediatric Aplastic Anemia Patients," sets the foundation. It examines the short-term effectiveness of BMT in both adult and pediatric patients, providing crucial insights into how this treatment affects survival …

Radiation Exposure Calibration Of The Al2o3:C With Radium-226 And Cesium-137 Using The Osl Method, Selma Tepeli Aydin

All Theses

Optically stimulated luminescence (OSL) dosimetry was utilized to calibrate Al₂O₃:C powder dosimeters, available commercially as the nanoDot® from Landauer Inc., and compare the dosimeter response to radium-226 (²²⁶Ra) and cesium-137 (¹³⁷Cs). The signal from the OSL was quantified using a microSTARii^® OSL reader also produced by Landauer Inc. Dose-response curves were developed for ²²⁶Ra and ¹³⁷Cs experiments (5 dosimeters each) at thirteen absorbed doses. Individual dosimeter response was tracked by serial number. Linear regression analysis was performed to determine if there were significant differences between the intercepts of the …

Enhanced Market Timing: Long Short-Term Memory Neural Network For Optimal Entry And Exit In Stock Market, Jerome Chinua Hall

Legacy Theses & Dissertations (2009 - 2024)

This thesis investigates applying a Long Short-Term Memory (LSTM) Neural Network for forecasting optimal times to enter and exit the stock market. Given the inherent imbalance in the dataset, where most days are not opportune for market actions, and the challenge of predicting such infrequent occurrences, we will employ a five-day window before and after the identified optimal market entry or exit time. A prediction is considered correct if it is within this five-day interval. The model was trained on historical S&P 500 data, using features such as exponential and simple moving averages of the closing price, the ten-day percentage …

The Impact Of Neighborhood Socioeconomic Disadvantage On Operative Outcomes After Single-Level Lumbar Fusion, Grace Y. Ng, Ritesh Karsalia, Ryan S. Gallagher, Austin J. Borja, Jianbo Na, Scott Mcclintock, Neil R. Malhotra

Mathematics Faculty Publications

INTRODUCTION: The relationship between socioeconomic status and neurosurgical outcomes has been investigated with respect to insurance status or median household income, but few studies have considered more comprehensive measures of socioeconomic status. This study examines the relationship between Area Deprivation Index (ADI), a comprehensive measure of neighborhood socioeconomic disadvantage, and short-term postoperative outcomes after lumbar fusion surgery. METHODS: 1861 adult patients undergoing single-level, posterior-only lumbar fusion at a single, multihospital academic medical center were retrospectively enrolled. An ADI matching protocol was used to identify each patient's 9-digit zip code and the zip code-associated ADI data. Primary outcomes included 30- and …

Wavelet Compression As An Observational Operator In Data Assimilation Systems For Sea Surface Temperature, Bradley J. Sciacca

LSU New Orleans Theses and Dissertations

The ocean remains severely under-observed, in part due to its sheer size. Containing nearly billion of water with most of the subsurface being invisible because water is extremely difficult to penetrate using electromagnetic radiation, as is typically used by satellite measuring instruments. For this reason, most observations of the ocean have very low spatial-temporal coverage to get a broad capture of the ocean’s features. However, recent “dense but patchy” data have increased the availability of high-resolution – low spatial coverage observations. These novel data sets have motivated research into multi-scale data assimilation methods. Here, we demonstrate a new assimilation approach …

Bayesian Learning Of Spatiotemporal Source Distribution For Beached Microplastic In The Gulf Of Mexico, David Pojunas

Graduate Theses and Dissertations

Over the last several decades, plastic waste has gradually accumulated while slowly degrading in terrestrial and oceanic environments. Recently, there has been an increased effort to identify the possible sources of plastic to understand how they affect vulnerable beaches. This issue is of particular concern in the Gulf of Mexico due to the presence of oil, natural gas, and plastic production. In this thesis, we expand upon existing Bayesian plastic attribution models and develop a rigorous statistical framework to map observed beached microplastics to their sources. Within this framework, we combine Lagrangian backtracking simulations of floating particles using nurdle beaching …

Estimating Financial And Environmental Risk: Some New Developments And Comparison Study., Fnu Kamronnaher

All Dissertations

This dissertation delves into the concept of risk, specifically focusing on two prominent categories: financial risk and environmental risk.

Financial risk is the probability of unfavorable outcomes of an investment while environmental risk refers to the possible harm to the environment resulting from extreme weather events. More specifically, risk is the high (low) quantiles of the distribution of variables of interest. \\ To quantify and assess uncertainties in financial and environmental risk, robust and reliable methodologies are needed. The aim of this dissertation is to develop some risk assessment methods and compare them with the widely used methodologies in both …

Metrics For Comparison Of Complex Networks, Clarissa Reyes

Open Access Theses & Dissertations

Heuristic network statistics are used as a preliminary approach to identify change across networks. In networks where there is known node correspondence (KNC), conventional network comparison methods include taking a norm of the difference matrix, or calculating dissimilarity measures like DeltaCon and cut distance. Since different KNC measures provide varying insight to the network comparison problem, we propose employing Rank Score Characteristic Functions (RSCFs) and the rank-score process as a method for reaching a consensus when ranking quantified change across multiple pairs of networks â?? which is particularly useful for ranking change across subpopulations or subgraphs. Additionally, we propose a …

Integrating Machine Learning Methods For Medical Diagnosis, Jazmin Quezada

Open Access Theses & Dissertations

Abstract:The rapid advancement of machine learning techniques has revolutionized the field of medical diagnosis by offering powerful tools to analyze complex data sets and make accurate predictions. In this proposed method, we present a novel approach that integrates machine learning and optimization models to enhance the accuracy of medical diagnoses. Our method focuses on fine-tuning and optimizing the parameters of machine learning algorithms commonly used in medical diagnosis, such as logistic regression, support vector machines, and neural networks. By employing optimization techniques, we systematically explore the parameter space of these algorithms to discover the most optimal configurations. Moreover, by representing …

Stochastic Optimal Control Of Conditional Mckean-Vlasov Equations With Jump And Markovian Switching, Charles Samuel Conly Sharp

Theses and Dissertations

This thesis obtains a number of results in stochastic optimal control for conditional McKean-Vlasov equations with jump and Markovian switching. First, we prove the uniqueness of the solutions and derive a relevant version of Itô's formula. We provide the dynamic programming principle and prove the associated verification theorem. A stochastic maximum principle is established. Further, we derive the relationship between dynamic programming and the stochastic maximum principle. Additionally, we utilize our stochastic maximum principle result for a mean-variance portfolio selection problem.

Comparative Analysis Of Teacher Effects Parameters In Models Used For Assessing School Effectiveness: Value-Added Models & Persistence, Merlin J. Kamgue

Graduate Theses and Dissertations

Longitudinal measures for students have become increasingly popular to estimate the effects of individual teachers and schools. Value-added models are one of the approaches using longitudinal data to evaluate teachers and schools. In the value-added model (VAM) literature, many statistical approaches have been developed and used to estimate teacher or school effects on student learning. This study opted to use a Bayesian multivariate model for evaluating teacher effects. The generalized persistence models can handle longitudinal data, not vertically scaled, allowing for a below-par teacher’s effects correlation across test administrations. This study first generated longitudinal students’ test score data and used …

Implementation Of Hierarchical And K-Means Clustering Techniques On The Trend And Seasonality Components Of Temperature Profile Data, Emmanuel Ogedegbe

Electronic Theses and Dissertations

In this study, time series decomposition techniques are used in conjunction with Kmeans clustering and Hierarchical clustering, two well-known clustering algorithms, to climate data. Their implementation and comparisons are then examined. The main objective is to identify similar climate trends and group geographical areas with similar environmental conditions. Climate data from specific places are collected and analyzed as part of the project. The time series is then split into trend, seasonality, and residual components. In order to categorize growing regions according to their climatic inclinations, the deconstructed time series are then submitted to K-means clustering and Hierarchical clustering with dynamic …

Dual Barriers: Examining Digital Access And Travel Burdens To Hospital Maternity Care Access In The United States, 2020, Peiyin Hung Ph.D., Marion Granger, Nansi Boghossian, Jiani Yu, Sayward Harrison, Jihong Liu Sc.D., Berry A. Campbell, Bo Cai Ph.D., Chen Liang, Xiaoming Li Ph.D.

Faculty Publications

Policy Points The White House Blueprint for Addressing the Maternal Health Crisis report released in June 2022 highlighted the need to enhance equitable access to maternity care. Nationwide hospital maternity unit closures have worsened the maternal health crisis in underserved communities, leaving many birthing people with few options and with long travel times to reach essential care. Ensuring equitable access to maternity care requires addressing travel burdens to care and inadequate digital access. Our findings reveal socioeconomically disadvantaged communities in the United States face dual barriers to maternity care access, as communities located farthest away from care facilities had the …