Related Experiment Video
Updated: Jun 7, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Marginal longitudinal semiparametric regression via penalized splines
M Al Kadiri1, R J Carroll, M P Wand
1Centre for Statistical and Survey Methodology, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, New South Wales, Australia.
Statistics & Probability Letters
|November 2, 2010
Summary
This study introduces a simple penalized spline method for marginal longitudinal nonparametric regression. This approach, implemented using Gibbs sampling and BUGS software, offers an efficient estimation technique.
Area of Science:
- Statistics
- Biostatistics
- Econometrics
Background:
- Marginal longitudinal nonparametric regression is complex.
- Existing efficient estimation methods are often elaborate.
- A simpler, effective approach is needed.
Purpose of the Study:
- To present a straightforward penalized spline method for marginal longitudinal nonparametric regression.
- To demonstrate the utility of Gibbs sampling and BUGS software for implementation.
- To illustrate applications in nonparametric and additive regression models.
Main Methods:
- Penalized splines for nonparametric regression.
- Gibbs sampling for Bayesian inference.
- BUGS software for computational implementation.
Main Results:
- A simple and effective penalized spline approach for marginal longitudinal nonparametric regression.
- Efficient estimation is achievable with this method.
- Successful implementation using Gibbs sampling and BUGS.
Conclusions:
- Penalized splines offer a practical solution for marginal longitudinal nonparametric regression.
- Gibbs sampling and BUGS facilitate efficient implementation.
- The method is applicable to various regression models.
Related Concept Videos
01:14Parametric Survival Analysis: Weibull and Exponential Methods
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
01:09Truncation in Survival Analysis
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:20Calibration Curves: Linear Least Squares
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
For data that follow a straight line, the standard method for fitting is the linear...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:23Quadratic Models
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...