Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

41
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...
41
Correlation01:09

Correlation

11.7K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.7K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

433
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...
433
Correlation of Experimental Data01:23

Correlation of Experimental Data

231
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
231
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

130
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
130
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

372
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
372

You might also read

Related Articles

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

Sort by
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

A GMM APPROACH FOR DEALING WITH MISSING DATA ON REGRESSORS.

The review of economics and statistics·2024
Same author

Missing dependent variables in fixed-effects models.

Journal of econometrics·2024
Same author

Interval censored regression with fixed effects.

Journal of applied econometrics (Chichester, England)·2023
Same author

Heterogenous Macromolecular Contributions to Early Mouse Embryo Development: (in vitro culture/mouse embryos/abnormal development/growth factors/inductors).

Development, growth & differentiation·2023
Same author

Examination of universal purchase programs as a driver of vaccine uptake among US States, 1995-2014.

Vaccine·2018
Same journal

Double/debiased machine learning for logistic partially linear model.

The econometrics journal·2024
Same journal

Using a Satisficing Model of Experimenter Decision-Making to Guide Finite-Sample Inference for Compromised Experiments.

The econometrics journal·2021
Same journal

Model averaging estimation for high-dimensional covariance matrices with a network structure.

The econometrics journal·2021
Same journal

Peer effects in bedtime decisions among adolescents: a social network model with sampled data.

The econometrics journal·2019
Same journal

My friend far, far away: a random field approach to exponential random graph models.

The econometrics journal·2019
Same journal

An instrumental variable random-coefficients model for binary outcomes.

The econometrics journal·2015
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 4, 2025

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

3.4K

Partial effects in non-linear panel data models with correlated random effects.

Jason Abrevaya1, Yu-Chin Hsu2

  • 1Department of Economics, The University of Texas at Austin, Austin, TX 78712, USA.

The Econometrics Journal
|February 9, 2024
PubMed
Summary
This summary is machine-generated.

This study clarifies partial effects in nonlinear panel data models, crucial for empirical research. Understanding how unobserved heterogeneity and covariate values impact these effects is key to accurate interpretation.

Keywords:
C33Non-linear panel data modelscorrelated random effectspartial effects

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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

Published on: September 17, 2019

6.3K

Related Experiment Videos

Last Updated: Jul 4, 2025

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

3.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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

Published on: September 17, 2019

6.3K

Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Estimating and interpreting partial effects in nonlinear panel data models presents challenges due to nonlinearity and heterogeneity.
  • Existing literature offers various approaches, but a systematic characterization is needed for empirical researchers.

Purpose of the Study:

  • To systematically characterize various partial effects in nonlinear panel data models.
  • To introduce new concepts for partial effects and clarify existing ones.
  • To demonstrate the quantitative differences between various partial effects using a panel probit model.

Main Methods:

  • Developing a systematic framework for characterizing partial effects in nonlinear panel data models.
  • Distinguishing interpretations based on the treatment of unobserved heterogeneity (fixed vs. covariate-dependent).
  • Differentiating interpretations based on the averaging approach (specific covariate values vs. average over values).

Main Results:

  • The interpretation of partial effects critically depends on assumptions about unobserved heterogeneity and the averaging strategy.
  • The study introduces novel partial-effects concepts alongside existing ones.
  • A panel probit example illustrates that different partial effects can yield substantially different quantitative results.

Conclusions:

  • A clear understanding of partial effects in nonlinear panel data models is essential for robust empirical research.
  • Researchers must carefully consider the treatment of unobserved heterogeneity and the choice of averaging to correctly interpret partial effects.
  • The proposed characterization and new concepts enhance the toolkit for analyzing complex panel data structures.