Jove
Visualize
Contact Us

Related Concept Videos

Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Regression Analysis01:11

Regression Analysis

6.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.5K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

3.2K
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...
3.2K
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
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...
6.6K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

180
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
180
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

130
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...
130

You might also read

Related Articles

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

Sort by
Same author

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.

Statistical methods in medical research·2026
Same author

Variable selection in functional linear Cox model.

Biometrics·2026
Same author

Advancing Objective Mobile Device Use Measurement in Children Ages 6-11 Through Built-In Device Sensors: A Proof-of-Concept Study.

Human behavior and emerging technologies·2025
Same author

Home-Based Digital Technologies to Support Aging-in-Place for Rural African American People With Alzheimer Disease and Their Care Partners: Protocol for a Mixed Methods Feasibility Study.

JMIR research protocols·2025
Same author

Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions.

Biometrical journal. Biometrische Zeitschrift·2024
Same author

Functional proportional hazards mixture cure model with applications in cancer mortality in NHANES and post ICU recovery.

Statistical methods in medical research·2023
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
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: Oct 20, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Variable selection in nonlinear function-on-scalar regression.

Rahul Ghosal1, Arnab Maity2

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Biometrics
|September 16, 2021
PubMed
Summary
This summary is machine-generated.

We introduce a novel variable selection method for nonlinear additive function-on-scalar regression (FOSR) models. This approach effectively identifies important predictors, accommodating nonlinear covariate effects and improving upon existing linear FOSR methods.

Keywords:
NHANESfunction-on-scalar regressionfunctional data analysisfunctional principal component analysisnonlinear regressionvariable selection

More Related Videos

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

3.5K
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.7K

Related Experiment Videos

Last Updated: Oct 20, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
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

3.5K
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.7K

Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Existing variable selection methods for function-on-scalar regression (FOSR) often assume linear effects of scalar predictors.
  • This linearity assumption can be overly restrictive when dealing with multiple continuous covariates.
  • There is a need for flexible methods that can capture nonlinear relationships in FOSR.

Purpose of the Study:

  • To develop a new, computationally efficient method for variable selection in nonlinear additive FOSR models.
  • To extend existing linear FOSR variable selection techniques to accommodate nonlinear covariate effects.
  • To provide a unified and flexible framework for variable selection in FOSR.

Main Methods:

  • Proposed a computationally efficient approach utilizing functional principal component scores of the functional response.
  • Extended the framework to handle nonlinear additive effects of scalar predictors within the FOSR model.
  • Evaluated the method through simulation studies and application to real-world accelerometer data.

Main Results:

  • The proposed method demonstrates advantages over existing variable selection techniques in FOSR, even when covariate effects are linear.
  • The method successfully identifies relevant variables and accommodates nonlinear relationships.
  • Application to NHANES accelerometer data reveals associations between physical activity patterns and participant characteristics.

Conclusions:

  • The developed method offers a flexible and unified approach for variable selection in nonlinear additive FOSR.
  • It effectively handles nonlinear covariate effects, outperforming existing methods in simulations.
  • The approach is valuable for analyzing complex functional data, such as physical activity patterns.