Jove
Visualize
Contact Us
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 Concept Videos

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...
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...
Friedman Two-way Analysis of Variance by Ranks01:21

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 from...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Multiple Regression01:25

Multiple Regression

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...

You might also read

Related Articles

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

Sort by
Same author

Statistical analysis of disease onset during lifespan with left truncation.

Biometrics·2026
Same author

Gradient boosting-based discrete failure time model for selecting time-varying effects and interactions.

Lifetime data analysis·2026
Same author

Atezolizumab for Alveolar Soft Part Sarcoma: A Clinical Trial Update.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Regression for Left-Truncated and Right-Censored Data: A Semiparametric Sieve Likelihood Approach.

Statistics in medicine·2026
Same author

Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet.

Lifetime data analysis·2026
Same author

Pulmonary artery sarcoma with mediastinal metastasis: a case report.

Frontiers in oncology·2026

Related Experiment Video

Updated: May 11, 2026

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

Variable selection in monotone single-index models via the adaptive LASSO.

Jared C Foster1, Jeremy M G Taylor, Bin Nan

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, U.S.A.

Statistics in Medicine
|May 8, 2013
PubMed
Summary

This study introduces a new adaptive LASSO method for variable selection in monotone single-index models. The approach improves estimation accuracy and performance, especially when monotonicity is assumed.

Keywords:
adaptive LASSOisotonic regressionkernel estimatorsingle-index modelsvariable 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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: May 11, 2026

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

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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Single-index models are valuable for analyzing high-dimensional data by reducing dimensionality.
  • The assumption of monotonicity in the functional component enhances model interpretability and inference.
  • Variable selection is crucial for identifying relevant predictors in complex models.

Purpose of the Study:

  • To develop and evaluate an adaptive LASSO penalized least squares method for variable selection in monotone single-index models.
  • To estimate both the index parameter and the unknown monotone function for continuous outcomes.
  • To assess the performance of the proposed methods through simulation studies and real-world data application.

Main Methods:

  • An adaptive LASSO penalized least squares approach is proposed for parameter estimation.
  • Monotone function estimation is achieved using the pooled adjacent violators algorithm (PAVA) and kernel regression.
  • An iterative estimation process approximates the unknown function linearly, enabling the use of standard LASSO algorithms like coordinate descent.

Main Results:

  • Simulation studies demonstrate that the proposed methods perform well across various scenarios.
  • The assumption of monotonicity significantly improves model performance when applicable.
  • The methods are successfully applied to data from a randomized clinical trial in intensive care.

Conclusions:

  • The adaptive LASSO approach provides an effective tool for variable selection in monotone single-index models.
  • Incorporating the monotonicity assumption enhances the statistical power and precision of the analysis.
  • The methodology is robust and applicable to real-world clinical trial data analysis.