Generalized Linear Models

Monday, September 16, 2019 - 2:30pm - 3:30pm
Runze Li (The Pennsylvania State University)
We develop a new estimation and valid inference method for low-dimensional regression coefficients in high-dimensional generalized linear models. The proposed estimator is computed by solving a score function. We recursively conduct model selection to reduce the dimensionality from high to a moderate scale and construct the score equation based on the selected variables. The proposed confidence interval (CI) achieves valid coverage without assuming consistency of the model selection
Tuesday, April 24, 2018 - 3:30pm - 4:00pm
Rebecca Willett (University of Wisconsin, Madison)
Consider observing a collection of discrete events within a network that reflects how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-exciting point processes, in which the likelihood of future events depends on the past events.
Subscribe to RSS - Generalized Linear Models