Search research articles

Related Concept Videos

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Parametric Survival Analysis: Weibull and Exponential Methods

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...

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Practical Consequences of the Bias in the Laplace Approximation to Marginal Likelihood for Hierarchical Models.

Entropy (Basel, Switzerland)·2025

Same author

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.

Movement ecology·2024

Same author

Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC.

Frontiers in ecology and evolution·2021

Same author

A unifying theory for two-dimensional spatial redistribution kernels with applications in population spread modelling.

Journal of the Royal Society, Interface·2020

Same author

Estimating the intensity of use by interacting predators and prey using camera traps.

The Journal of animal ecology·2019

Same author

Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference.

Ecology and evolution·2017

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Profile Likelihood for Hierarchical Models Using Data Doubling.

Subhash R Lele¹

¹Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada.

Entropy (Basel, Switzerland)

|September 28, 2023

Summary

This study introduces a novel computational method for profile likelihood inference in hierarchical models. The data doubling technique simplifies complex calculations, enabling accurate statistical analysis of functions of parameters.

Keywords:

Laplace approximation data cloning functions of parameters nuisance parameters parameterization invariance

More Related Videos

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

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

Published on: July 3, 2020

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Related Experiment Videos

Last Updated: Jul 15, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

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

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

Published on: July 3, 2020

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

Statistics
Computational Statistics
Statistical Modeling

Background:

Statistical inference often requires analyzing functions of numerous canonical parameters.
Frequentist inference typically relies on profile likelihood, which is computationally challenging for hierarchical models due to high-dimensional integration.
Existing methods struggle with the complexity of likelihood computation in hierarchical settings.

Purpose of the Study:

To develop a computationally efficient method for calculating profile likelihood for functions of parameters in general hierarchical models.
To provide a robust alternative to traditional methods that are hindered by integration challenges.

Main Methods:

The study proposes a novel computational approach using data doubling.
This method bypasses the need for direct high-dimensional integration of the likelihood function.
The technique is applicable to any specified function of the model parameters.

Main Results:

The data doubling method provides a simple and effective way to compute profile likelihood for hierarchical models.
Mathematical proofs confirm the method's validity under standard regularity conditions.
The approach ensures that the maximum likelihood estimator's distribution is non-singular, multivariate, and Gaussian.

Conclusions:

The developed computational method significantly simplifies profile likelihood inference for hierarchical models.
This advancement offers a practical tool for frequentist statistical analysis in complex modeling scenarios.
The data doubling technique enhances the tractability of statistical inference for functions of parameters in hierarchical structures.