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Related Experiment Video

Updated: May 14, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)

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.

Statistical Methods in Medical Research

|February 5, 2013

Summary

This summary is machine-generated.

A new method automatically selects smoothing parameters for generalized additive models (GAMLSS) using P-splines and local maximum likelihood. This approach efficiently estimates multiple parameters for applications like centile estimation.

Keywords:

Box–Cox power exponential distribution Box–Cox t distribution P-splines lambda–mu–sigma method quantile estimation

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Last Updated: May 14, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

Statistical modeling
Computational statistics

Background:

Generalized Additive Models for Location, Scale and Shape (GAMLSS) are flexible for modeling complex distributions.
Automatic selection of smoothing parameters is crucial for GAMLSS model performance and interpretability.

Purpose of the Study:

To introduce a novel, fast method for automatic smoothing parameter selection in GAMLSS.
To apply this method to centile estimation, modeling all distribution parameters as smooth functions.

Main Methods:

Utilizes a P-spline representation for smoothing terms, treating them as random effects.
Employs internal (local) maximum likelihood estimation on the predictor scale for parameter estimation.
Applies the method to model all four parameters (location, scale, skewness, kurtosis) of a response distribution.

Main Results:

The proposed method offers a computationally efficient way to estimate multiple smoothing parameters simultaneously.
Demonstrates successful application in centile estimation, enabling smooth modeling of distribution characteristics.
Facilitates the modeling of location, scale, skewness, and kurtosis as smooth functions of an explanatory variable.

Conclusions:

The P-spline based local maximum likelihood approach provides an effective and fast solution for smoothing parameter selection in GAMLSS.
This method enhances the flexibility and applicability of GAMLSS for detailed distributional analysis, including centile estimation.