LASSO type penalized spline regression for binary data.

ABSTRACT:

Background

Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be used for smoothing by considering the knot coefficients from a regression spline as random effects. The resulting models are called semiparametric mixed models (SPMMs). Allowing the random knot coefficients to follow a normal distribution with mean zero and a constant variance is equivalent to using a penalized spline with a ridge regression type penalty. We introduce the least absolute shrinkage and selection operator (LASSO) type penalty in the SPMM setting by considering the coefficients at the knots to follow a Laplace double exponential distribution with mean zero.

Methods

We adopt a Bayesian approach and use the Markov Chain Monte Carlo (MCMC) algorithm for mod

SUBMITTER: Mullah MAS 

PROVIDER: S-EPMC8070328 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature