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Full-Text Articles in Statistical Models
Bayesian Statistical Modeling Of Spatially Resolved Transcriptomics Data, Xi Jiang
Bayesian Statistical Modeling Of Spatially Resolved Transcriptomics Data, Xi Jiang
Statistical Science Theses and Dissertations
Spatially resolved transcriptomics (SRT) quantifies expression levels at different spatial locations, providing a new and powerful tool to investigate novel biological insights. As experimental technologies enhance both in capacity and efficiency, there arises a growing demand for the development of analytical methodologies.
One question in SRT data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process, which could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types …
Forecasting Covid-19 With Temporal Hierarchies And Ensemble Methods, Li Shandross
Forecasting Covid-19 With Temporal Hierarchies And Ensemble Methods, Li Shandross
Masters Theses
Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights.
In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into …