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pro vyhledávání: '"Townes, F. William"'
Seasonal influenza forecasting is critical for public health and individual decision making. We investigate whether the inclusion of data about influenza activity in neighboring states can improve point predictions and distribution forecasting of inf
Externí odkaz:
http://arxiv.org/abs/2408.12722
Autor:
Rosengart, AnnaElaine L., Bidwell, Amanda L., Wolfe, Marlene K., Boehm, Alexandria B., Townes, F. William
Since the start of the coronavirus-19 pandemic, the use of wastewater-based epidemiology (WBE) for disease surveillance has increased throughout the world. Because wastewater measurements are affected by external factors, processing WBE data typicall
Externí odkaz:
http://arxiv.org/abs/2408.12012
Autor:
Townes, F. William
Mixed Poisson distributions provide a flexible approach to the analysis of count data with overdispersion, zero inflation, or heavy tails. Since the Poisson mean must be nonnegative, the mixing distribution is typically assumed to have nonnegative su
Externí odkaz:
http://arxiv.org/abs/2407.17614
Autor:
Townes, F. William
The class of subweibull distributions has recently been shown to generalize the important properties of subexponential and subgaussian random variables. We describe alternative characterizations of subweibull distributions and detail the conditions u
Externí odkaz:
http://arxiv.org/abs/2407.11386
Auxiliary data sources have become increasingly important in epidemiological surveillance, as they are often available at a finer spatial and temporal resolution, larger coverage, and lower latency than traditional surveillance signals. We describe t
Externí odkaz:
http://arxiv.org/abs/2309.16546
Gaussian processes are widely used for the analysis of spatial data due to their nonparametric flexibility and ability to quantify uncertainty, and recently developed scalable approximations have facilitated application to massive datasets. For multi
Externí odkaz:
http://arxiv.org/abs/2110.06122
High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often it is of interest to quantify and summarize changes in cell state that occur between experimental or biological conditions. Differential
Externí odkaz:
http://arxiv.org/abs/2102.06731
Autor:
Townes, F. William
Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis. Generalized linear models enable direct modeling of counts in a
Externí odkaz:
http://arxiv.org/abs/2001.04343
Autor:
Townes, F. William
Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data. We provide a detailed derivation of GLM-PCA with a focus on optimization. We also demonstrate how to incorporate covariates, and sugg
Externí odkaz:
http://arxiv.org/abs/1907.02647
Autor:
Marsh, David M., Townes, F. William, Cotter, Kerry M., Farroni, Kara, McCreary, Kathryn L., Petry, Rachael L., Tilghman, Joseph M.
Publikováno v:
Journal of Herpetology, 2019 Jun 01. 53(2), 96-103.
Externí odkaz:
https://www.jstor.org/stable/48687016