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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic 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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric 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...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

MOMENT-BASED METHOD FOR RANDOM EFFECTS SELECTION IN LINEAR MIXED MODELS.

Mihye Ahn1, Hao Helen Zhang, Wenbin Lu

  • 1Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, U.S.A.

Statistica Sinica
|October 30, 2012
PubMed
Summary
This summary is machine-generated.

We introduce a new framework for selecting random effects in linear mixed models, improving parameter estimation efficiency. This robust method offers automatic selection without distributional assumptions, enhancing statistical modeling capabilities.

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Selecting random effects is crucial but difficult in linear mixed models.
  • Existing methods often require specific distributional assumptions.

Purpose of the Study:

  • To propose a robust and unified framework for automatic random effects selection.
  • To estimate covariance components and improve model parameter efficiency.
  • To extend the framework for simultaneous fixed effects selection.

Main Methods:

  • Developed a moment-based loss function for covariance matrix estimation.
  • Applied hard thresholding and a novel sandwich-type soft-thresholding penalty for sparse estimation.
  • Established asymptotic properties for consistency in selection and estimation.
  • Proposed optimization strategies for computational challenges.

Main Results:

  • The new procedure is distribution-free for random effects and errors.
  • Demonstrated consistency in both random effects selection and variance component estimation.
  • Showcased promising performance in selecting both random and fixed effects.
  • Successfully applied to the Amsterdam Growth and Health study data.

Conclusions:

  • The proposed framework provides a robust and unified approach to random effects selection.
  • It enhances the efficiency of estimating model parameters by improving selection accuracy.
  • The method's distribution-free nature and ability to select fixed effects offer broad applicability.