Publications by authors named "Michael J Wurm"
Article Synopsis
- Regularization techniques like lasso and elastic net help improve regression models by enhancing coefficient estimation and prediction accuracy while also aiding in variable selection.
- The authors propose a coordinate descent algorithm to fit a range of ordinal regression models with an elastic net penalty, addressing gaps in current software for regularized regression.
- They introduce a new model class called ELMO, which encompasses models like multinomial and ordinal logistic regression, and provide an R package that implements this algorithm.
This paper introduces a new algorithm to estimate and perform inferences on a recently proposed and developed semiparametric generalized linear model (glm). Rather than selecting a particular parametric exponential family model, such as the Poisson distribution, this semiparametric glm assumes that the response is drawn from the more general exponential tilt family. The regression coefficients and unspecified reference distribution are estimated by maximizing a semiparametric likelihood.
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