Nonparametric Analysis of Temporal Trend When Fitting Parametric Models to Extreme­Value Data

A Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data

ÐWe present a simple paradigm for fitting models, parametric and nonparametric, to noisy data, which resolves some of the problems associated with classical MSE algorithms. This is done by considering each point on the model as a possible source for each data point. The paradigm can be used to solve problems which are ill-posed in the classical MSE approach, such as fitting a segment (as oppose...

Power Transformations When Fitting Theoretical Models to Data

A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data

We o er a simple paradigm for tting models, parametric and non-parametric, to noisy data, which resolves some of the problems associated with classic MSE algorithms. This is done by considering each point on the model as a possible source for each data point. The paradigm also allows to solve problems which are not de ned in the classical MSE approach, such as tting a segment (as opposed to a l...

Fitting Models to Grouped Data

Fitting parametric random effects models in very large data sets with application to VHA national data

BACKGROUND With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such a...