P-splines with an $\ell_{1}$ penalty for repeated measures

Repeated measures with zeros.

Consider repeated measures data with many zeros. For the case with one grouping factor and one repeated measure, we examine several models, assuming that the nonzero data are roughly lognormal. One of the simplest approaches is to model the zeros as left-censored observations from the lognormal distribution. A random effect is assumed for subjects. The censored model makes a strong assumption a...

Robust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models

We start the paper by pointing the potential important role of the prior distribution of the roughness penalty parameter in the resulting smoothness of Bayesian P-splines models (Ruppert et al. 2003 ; Lang and Brezger 2004). The recommended specification for that distribution yields models that can lack flexibility in specific circumstances. In such instances, these are shown to correspond to a...

Repeated Measures

Repeated measures refer to the observations taken on the same subject (plant, animal, person, etc). There is usually a variation among the subjects, which is modeled by random effects. Here is an example. A completely randomized design with a single factor (with fertilizer or without). Each treatment is applied to three plants and the root length of each plant is measured on five occasions (wee...

Repeated measures.

A repeated-measures design is one in which multiple, or repeated, measurements are made on each experimental unit. The experimental unit could be a person or an animal, and repeated measurements might be taken serially in time, such as in weekly systolic blood pressures or monthly weights. The repeated assessments might be measured under different experimental conditions. Repeated measurements ...

Bayesian P-Splines