Search research articles

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

Related Concept Videos

Association Areas of the Cortex

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

Factorial Design

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...

You might also read

Related Articles

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

Sort by

Same author

[Gut-brain axis and Parkinson's disease: research progress on the gut-brain interaction].

Sheng li xue bao : [Acta physiologica Sinica]·2026

Same author

Transient Potential Profiling for Rapid Calcium Ion Quantification: Eliminating Conditioning Time in Solid-Contact Ion-Selective Electrodes.

Biosensors·2026

Same author

Fragmentation property identity card-assisted reliable structural annotation of calycosin metabolites in vivo: A strategy for isomer discrimination using mass spectrometry.

Journal of chromatography. A·2026

Same author

Non-surgical treatment of severe trachea injury due to tracheal intubation-a case report.

AME case reports·2026

Same author

Maternal vitamin A deficiency programs offspring visceral hypersensitivity through RARβ-COX-2/PGE2 signaling.

Biochimica et biophysica acta. Molecular and cell biology of lipids·2026

Same author

Analysis of Quality Differences in <i>Rhizoma Musae</i> From Different Origins Based on Rhizosphere Soil Bacterial Diversity and Metabolomics.

Food science & nutrition·2026

Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026

Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026

Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026

Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026

Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026

Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 3, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Localized Functional Principal Component Analysis.

Kehui Chen¹, Jing Lei¹

¹University of Pittsburgh and Carnegie Mellon University.

Journal of the American Statistical Association

|January 26, 2016

Summary

This summary is machine-generated.

Localized functional principal component analysis (LFPCA) identifies orthogonal basis functions with localized support. This novel method improves data variability explanation and reveals features missed by standard functional principal component analysis (FPCA).

Keywords:

convex optimization deflation domain selection functional principal component analysis interpretability orthogonality

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Jan 3, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Statistics
Data Analysis
Functional Data Analysis

Background:

Functional principal component analysis (FPCA) is a standard method for dimensionality reduction in functional data.
Traditional FPCA may not effectively capture localized patterns or provide interpretable basis functions when variability is concentrated in specific regions.
There is a need for methods that can identify spatially or temporally localized patterns in functional data.

Purpose of the Study:

To introduce Localized Functional Principal Component Analysis (LFPCA) for identifying orthogonal basis functions with localized support.
To develop an efficient and globally optimal algorithm for LFPCA using a novel Deflated Fantope Localization method.
To demonstrate the performance of LFPCA in recovering true eigenfunctions and improving estimation accuracy compared to standard FPCA.

Main Methods:

Formulation of LFPCA as a convex optimization problem.
Implementation using an efficient algorithm to achieve global optimum.
Validation through simulations and analysis of country mortality data.

Main Results:

LFPCA converges to FPCA under appropriate parameter choices.
Cross-validation tuning parameters enable near-perfect recovery of localized eigenfunctions.
Significant improvement in estimation accuracy for data with localized eigenfunctions.
LFPCA reveals interpretable orthogonal basis functions even when original eigenfunctions are not localized.
Analysis of country mortality data uncovered novel insights not detectable by standard FPCA.

Conclusions:

LFPCA is an effective method for analyzing functional data with localized variability.
The proposed Deflated Fantope Localization method provides an efficient approach to LFPCA.
LFPCA enhances interpretability and accuracy in functional data analysis, offering advantages over standard FPCA.