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Showing 1-30 of 312 projects
Forecasting and Uncertainty Quantification for Renewable Energy Production Using Bay...
This study presents a comprehensive framework for forecasting renewable energy production and quantifying associated uncertainty through Bayesian spatio-tempora...
Forecasting and Uncertainty Quantification for Renewable Energy Output Using Probabi...
Forecasting renewable energy output with quantified uncertainties is critical for reliable grid integration, efficient resource planning, and resilient energy s...
Impact of Time Series Forecasting Methods on Electricity Demand Prediction in a Smar...
This study evaluates the performance of contemporary time series forecasting methods for predicting electricity demand within a smart grid environment, focusing...
Estimating Long-Run Forecast Uncertainty in Climate-Adjusted Regression Models Using...
This study develops and implements a robust statistical framework to quantify long-run forecast uncertainty in climate-adjusted regression models, explicitly ad...
Impact of Weather Extremes on Agricultural Yield: A Spatiotemporal Statistical Analy...
Weather extremes driven by climate variability pose substantial risks to agricultural productivity, necessitating robust quantitative frameworks that capture sp...
Topic: Bayesian Hierarchical Modeling for Small-Area Estimation in Public Health Sur...
Bayesian hierarchical modeling provides a robust framework for estimating small-area health indicators by borrowing strength across related regions and time poi...
Efficient Estimation of Spatial-Temporal Extremes in Climate Data Using Bayesian Hie...
Efficient estimation of spatial-temporal extremes in climate data is essential for robust risk assessment and climate adaptation planning. This study develops a...
Estimating the Impact of Climate Variables on Crop Yield Using Hierarchical Bayesian...
This study develops a hierarchical Bayesian framework to quantify how climate variables influence crop yield while explicitly modeling spatial and temporal depe...
Evaluating Time-Varying Causal Effects in Observational Data Using Synthetic Control...
Evaluating time-varying causal effects in observational data presents a persistent challenge due to confounding, non-stationarity, and high dimensionality of co...
Assessing the Efficacy of Bootstrap Methods for Small-Sample Inference in Non-Normal...
Bootstrap methods have become central to statistical inference in settings where traditional parametric assumptions may fail, yet their performance under small-...
1) Bayesian hierarchical modeling for spatio-temporal crime incidence in urban distr...
This study develops a comprehensive statistical framework integrating Bayesian hierarchical modeling, state-space forecasting, robust high-dimensional technique...
Forecasting stock return volatility using high-frequency data and robust machine lea...
Forecasting stock return volatility remains a central challenge for asset pricing, risk management, and trading strategies, especially in the era of high-freque...
Assessing the Impact of Seasonal Adjustment Methods on Time Series Forecasting Accur...
This study investigates how seasonal adjustment methods influence the accuracy of time series forecasts for key economic indicators by applying robust statistic...
Robust Nonparametric Estimation of Extreme Value Distributions for Financial Risk As...
In financial risk management, accurately estimating the tail behavior of asset returns is crucial for quantifying extreme losses and for informing capital alloc...
Impact of Internet Search Trends on Predicting Localized Economic Indicators Using T...
This study investigates the predictive power of internet search trends for localized economic indicators using time series analysis and Bayesian Structural Time...
Robust Bayesian Hierarchical Models for Small-Sample Multivariate Time Series and Th...
This study develops and evaluates robust Bayesian hierarchical models tailored for small-sample, multivariate time series data and demonstrates their applicabil...
Assessing the Impact of Sampling Methods on Estimation Precision in Finite Populatio...
This study investigates how sampling methods influence estimation precision in finite population inference when data are subject to different missing data mecha...
Assessing the Impact of Climate Variability on Crop Yield Variability Using Bayesian...
This study investigates how climate variability influences crop yield variability by integrating Bayesian hierarchical modeling with time-varying coefficient me...
An Analysis of Seasonal Adjustment and Forecasting of Agricultural Yield Using Time ...
This study presents a comprehensive analysis of seasonal adjustment and forecasting of agricultural yield through the integration of time series methodologies a...
Robust Time Series Forecasting with Outlier-Resistant Methods for Economic Indicator...
This study develops and evaluates robust time series forecasting methods designed to withstand outliers and structural breaks in economic indicators, addressing...
Cryptographic Key Generation Reliability Estimation Using Bayesian Inference on Real...
Cryptographic key generation reliability is critical for ensuring secure communications in modern digital ecosystems, yet real-world entropy sources exhibit non...
Development of a Robust Time Series Forecasting Framework for Economic Indicators Us...
This study presents a robust time series forecasting framework for economic indicators by integrating state-space modeling with Bayesian inference to enhance pr...
Analyzing the Impact of Socioeconomic Factors on Household Income Distribution Using...
This study explores the intricate relationship between socioeconomic factors and household income distribution through the application of advanced statistical m...
Predictive Modeling of Student Performance Using Machine Learning Techniques...
This study explores the application of machine learning algorithms to predict student academic performance, aiming to enhance educational strategies and student...
Analyzing the Impact of Socioeconomic Factors on Educational Attainment Using Multiv...
This study investigates the influence of various socioeconomic factors on educational attainment among students in selected regions using advanced multivariate ...
Analyzing the Impact of Socioeconomic Factors on Educational Achievement Using Multi...
This study investigates the influence of socioeconomic factors on students' educational achievement through the application of advanced multivariate statistical...
Analyzing the Impact of Socioeconomic Factors on Graduation Rates Using Multivariate...
This study seeks to elucidate the influence of socioeconomic factors on graduation rates among students through the application of multivariate regression techn...
Analysis of Factors Influencing Student Academic Performance Using Multivariate Stat...
This study employs multivariate statistical techniques to analyze the numerous factors that influence student academic performance, aiming to identify key varia...
Analyzing the Impact of Socioeconomic Factors on Academic Performance Using Advanced...
This study examines the influence of socioeconomic factors on students' academic performance utilizing advanced statistical modeling techniques to uncover nuanc...
Analyzing the Impact of Socioeconomic Factors on Health Outcomes Using Multivariate ...
This study employs multivariate statistical techniques to analyze the influence of socioeconomic factors on health outcomes across diverse populations, aiming t...
