Abstract: Sparse generalized linear model is useful in many fields. In the research, the researchers will learn sparse generalized linear model using different algorithms. The paper determines the ...

In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...

Abstract: Identification of causal relationships of neural activity is one of the most important problems in neuroscience and neural engineering. We show that a novel deep learning approach using a ...

This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...

First off, awesome job so far with deploying MATLAB/Python Code for 1-d SPM. I was reviewing your website for generalized linear model, but there seems to be limited documentation on it, except for an ...