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Showing 1-30 of 297 Thesis
A Robust Framework for Bayesian Nonparametric Model Misspecification Detection...
In modern statistical practice, Bayesian nonparametric (BNP) methods offer flexible alternatives to parametric models, yet practical deployment is hindered by m...
Assessing Bayesian Forecasting for Ride-Hail Demand in San Francisco ...
This study addresses the challenge of accurately forecasting ride-hail demand in San Francisco amid spatial-temporal variability, network effects, and dynamic p...
Comparative Analysis of Bayesian vs. Frequentist Methods in Small-Sample Inference...
In small-sample inference, statistical practitioners face a persistent tension between Bayesian and Frequentist methodologies regarding reliability, interpretab...
Forecasting Electricity Demand via Graph Neural Networks and Uncertainty Quantificat...
Forecasting electricity demand is challenged by nonlinear temporal dynamics and spatial dependencies across a power network, which conventional time-series mode...
Estimating Small-Sample Robustness in Time Series Forecasts with Bootstrap...
This study addresses the challenge of assessing forecast robustness in small-sample time series settings, where traditional asymptotic properties of bootstrap m...
Robust Bayesian Inference for Small-Sample Clinical Trials ...
Small-sample clinical trials frequently yield imprecise and biased inferences when conventional frequentist methods are applied, compromising decision-making in...
A Bayesian Framework for Robust Small-Sample Inference under Model Uncertainty...
In many applied statistical settings, inference from small samples is severely compromised by model misspecification and the absence of a universal likelihood m...
Evaluating Inventory Forecasting Under Demand Shocks in Automotive Parts Suppliers...
The automotive parts supply chain faces recurrent demand shocks driven by macroeconomic volatility, geopolitical disturbances, and disruptions in production net...
Comparative Analysis of Bayesian vs. Frequentist Interval Estimation Methods in Prac...
This study examines the practical performance and implications of Bayesian and Frequentist interval estimation approaches across diverse applied settings, addre...
Bayesian Deep Learning for Real-Time Anomaly Detection in ICT Networks...
This study addresses the growing challenge of ensuring real-time cyber-physical security and reliable performance in modern ICT networks by integrating Bayesian...
Assessment of Bayesian Model Averaging in Small-Sample Economic Forecasting with Rea...
This study addresses the challenge of reliable economic forecasting in small-sample environments by evaluating Bayesian Model Averaging (BMA) as a robust tool f...
Robust Bayesian Hierarchical Models for Small-Area Estimation ...
In many countries, reliable estimates of socio-economic indicators at small geographic levels are essential for targeted policy design, yet conventional area-le...
A Probabilistic Framework for Nonparametric Evolutionary Model Selection Systems...
This study addresses the challenge of selecting increasingly complex models in nonparametric settings where traditional criteria misalign with evolving data str...
Assessing Credit Risk Modeling in a Regional Rural Bank Portfolio...
This study investigates credit risk modeling within the loan portfolio of a regional rural bank, addressing the persistent gap between theoretical credit risk f...
Comparative Analysis of Bayesian vs. Frequentist Inference in Small Samples...
In many applied research settings, small-sample scenarios pose significant challenges to statistical inference, often leading to biased conclusions under classi...
Adaptive Bayesian Spatio-Temporal Models for IoT Sensor Networks...
The proliferation of Internet of Things (IoT) sensor networks across smart cities and industrial environments generates high-velocity, spatially structured data...
Estimating Causal Effects in Observational Data via Double ML Methods...
Estimating causal effects from observational data remains a central challenge in empirical research, where treatment assignment is not randomized and confoundin...
Bayesian Multivariate Time Series for Climate Event Forecasting...
Climate variability and extreme weather events pose escalating risks to socio-economic systems, demanding robust predictive tools that can accommodate interdepe...
A Probabilistic Framework for Robust Cross-Validation under Model Misspecification...
The reliability of cross-validation as a model evaluation tool deteriorates when candidate models are misspecified, leading to optimistic or pessimistic estimat...
Bayesian Forecasting for Retail Inventory in a Chain Store Network...
This study investigates the performance and reliability of Bayesian forecasting for retail inventory management within a national chain store network operating ...
Comparative Analysis of Bayesian and Frequentist Interventions in Survival Data...
In survival analysis, researchers face the challenge of selecting inferential frameworks that balance prior information incorporation with data-driven evidence,...
Federated Learning for Real-Time Anomaly Detection in Industrial IoT Data...
Industrial Internet of Things (IIoT) environments generate continuous, high-velocity data streams from heterogeneous sensors and actuators, creating significant...
Assessing the Impact of Climate Variables on Crop Yield Variability in Agricultural ...
Climate variability significantly influences agricultural productivity, with fluctuations in temperature, rainfall, humidity, and solar radiation directly affec...
Development and Assessment of a Robust Nonparametric Regression Method...
In contemporary statistical analysis, nonparametric regression techniques have gained prominence due to their flexibility in modeling complex, nonlinear relatio...
A Bayesian Hierarchical Framework for Modeling Multi-Source Public Health Data...
The increasing availability of diverse public health data sources, including electronic health records, survey data, national health registries, and environment...
Evaluating the Impact of Customer Segmentation on Retail Sales Forecasting Accuracy...
With retail markets becoming increasingly competitive and customer-driven, accurate sales forecasting remains a critical component of effective inventory manage...
Comparative Analysis of Machine Learning Models for Predicting Urban Traffic Congest...
Urban traffic congestion remains a persistent challenge for city planners and transportation authorities worldwide, leading to increased travel times, environme...
Machine Learning-Based Forecasting of Cybersecurity Threats in IoT Networks...
The rapid proliferation of Internet of Things (IoT) devices coupled with their integration into critical infrastructure has heightened the vulnerability of IoT ...
Analyzing the Impact of Weather Variables on Agricultural Yield Variability...
The variability in agricultural yields due to fluctuating weather conditions poses significant challenges to food security and sustainable farming practices in ...
Development and assessment of a robust Bayesian hierarchical model for small-area he...
Effective analysis of small-area health data is critical for local health policy formulation and resource allocation, yet challenges persist due to data sparsit...
