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Articles 31 - 36 of 36
Full-Text Articles in Other Statistics and Probability
Examining Stigma In Rural Mental Health Care Settings: A Mixed Methods Approach, Lainie Krumenacker
Examining Stigma In Rural Mental Health Care Settings: A Mixed Methods Approach, Lainie Krumenacker
Murray State Theses and Dissertations
More than half of Americans will be diagnosed with a mental illness in their lifetime (CDC, 2021), yet stigma towards mental health affects both patients and providers. Although programs exist to address stigma, improve cultural competency among providers, and educate families on the importance of support, facilities are often limited on programs they provide due to allocation of resources and funds. Without a shift in treatment and programing, stigma will continue to impact patient care and outcome.
This study explored stigma among mental health providers in rural communities, while exploring potential differences in treatment among patients due to race. Mental …
Fact Sheet, Yale University Office Of Institutional Research
Fact Sheet, Yale University Office Of Institutional Research
Fact Sheets
No abstract provided.
Disentangling Cyclic Causality: An Instance-Based Framework For Causal Discovery, Chase A. Yakaboski
Disentangling Cyclic Causality: An Instance-Based Framework For Causal Discovery, Chase A. Yakaboski
Dartmouth College Ph.D Dissertations
Correlation does not imply causation" is one of the fundamental principles taught in science, emphasizing that associations between variables do not necessarily indicate causality. Yet, over the past three decades, extensive research has begun to challenge this perspective by developing sophisticated methods to differentiate causal from correlative relationships. This research suggests that correlations often involve a blend of confounded and causal interactions, which, given certain assumptions, can be disentangled to uncover actionable insights and deepen our understanding of physical, biological, and societal systems.
Accurately discovering causal relationships from data amidst cyclic dynamics remains a challenging open problem in causality research. …
Imputation Strategies For Different Categories Of Missing Data, Karthik Chalumuri
Imputation Strategies For Different Categories Of Missing Data, Karthik Chalumuri
Honors Theses and Capstones
Addressing missing data in research is crucial for ensuring the reliability and validity of study findings, yet it remains a significant challenge. This study investigates the impact of missing data on research outcomes and explores the underutilization of existing tools for managing missingness, potentially leading to gaps in critical information with tangible implications for decision-making processes (Dziura et al.).
Focusing on the different categories of missing data—Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR)—this research examines various imputation strategies tailored to each category. Specifically, we compare the efficacy of several model-based imputation methods, …
To Mean Or Not To Mean: An Investigation Of Regression To The Mean, Hunter Ellis
To Mean Or Not To Mean: An Investigation Of Regression To The Mean, Hunter Ellis
Williams Honors College, Honors Research Projects
Regression to the mean is a statistical phenomenon that can hide important characteristics of what is truly happening in a research study. Caused by statistical randomness, regression to the mean occurs when extreme values, high or low, are followed by less extreme values. To correctly deal with it, one must understand what it is and how to distinguish its effect on conclusions made from the data. This paper provides examples of regression to the mean in both a medical and academic performance study and explains simple identifiers one can observe. Those are then followed up by the introduction of the …
Last Passage Time And Excursion Theory For Solvable Diffusions With Applications In Mathematical Finance, Yaode Sui
Theses and Dissertations (Comprehensive)
In this dissertation, we investigate the properties of last passage times and excursion theory in one-dimensional solvable diffusions, emphasizing their applications in financial modeling, particularly in option pricing. We derive closed-form formulas for the marginal distribution of last passage times and their joint distribution with process values, including the maximum and minimum of the process value. The focus is on time-homogeneous diffusions with various boundaries and imposed killing. Employing spectral expansion theory, we derive explicit formulas for distributions of last passage times in common processes such as Drifted Brownian Motion (BM), Squared Bessel (SQB), Ornstein-Uhlenbeck (OU), and Cox-Ingersoll-Ross (CIR) models. …