Related Experiment Video
Updated: May 14, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
Published on: June 3, 2009
Automatic smoothing parameter selection in GAMLSS with an application to centile estimation
Robert A Rigby1, Dimitrios M Stasinopoulos2
1STORM FLSC, London Metropolitan University, London, UK.
Abstract:
A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each distribution parameter to estimate its smoothing parameters. This provides a fast method for estimating multiple smoothing parameters. The method is applied to centile estimation where all four parameters of a distribution for the response variable are modelled as smooth functions of a transformed explanatory variable x This allows smooth modelling of the location, scale, skewness and kurtosis parameters of the response variable distribution as functions of x.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...
Regression Toward the Mean
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...