Jove
Visualize
Contact Us

Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

87
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
87

You might also read

Related Articles

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

Sort by
Same author

Charge Injection and Interfiber Electrical Conduction in Cable Bacteria.

ACS applied materials & interfaces·2026
Same author

Pullulan Coating Preserves High Conductivity in Cable Bacteria Wires.

ACS applied bio materials·2026
Same author

Precision Training Via Causal Machine Learning: Modeling Rating of Perceived Exertion in Professional Soccer Players.

International journal of sports physiology and performance·2025
Same author

Inactive "Ghost" Cells Do Not Affect Motility and Long-Range Electron Transport in Filamentous Cable Bacteria.

Environmental microbiology·2025
Same author

Road Disturbance Shifts Root Fungal Symbiont Types and Reduces the Connectivity of Plant-Fungal Co-Occurrence Networks in Mountains.

Molecular ecology·2025
Same author

A novel cable bacteria species with a distinct morphology and genomic potential.

Applied and environmental microbiology·2025
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: Jun 27, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.2K

Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring.

Thomas Decorte1, Steven Mortier2, Jonas J Lembrechts3

  • 1Department of Mathematics, University of Antwerp-imec, Middelheimlaan 1, 2000 Antwerp, Belgium.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

Large-scale environmental sensor data often has missing values. Spatial data recovery techniques, particularly matrix completion, effectively fill these gaps, enhancing data analysis for crucial monitoring efforts.

Keywords:
environmental monitoringimputationmissing datatime serieswireless sensor networks

More Related Videos

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.2K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.2K

Related Experiment Videos

Last Updated: Jun 27, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.2K
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.2K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.2K

Area of Science:

  • Environmental Science
  • Data Science
  • Sensor Networks

Background:

  • Sensor networks generate vast spatiotemporal datasets vital for diverse fields.
  • Missing data in these datasets, due to sensor issues, hinders analysis.
  • Reconstructing missing sensor data is challenging, especially considering spatial and temporal correlations.

Purpose of the Study:

  • To evaluate various data imputation methods for large-scale environmental monitoring datasets.
  • To compare the effectiveness of spatial versus temporal data recovery techniques.
  • To identify optimal methods for completing missing sensor readings in IoT networks.

Main Methods:

  • Applied and evaluated 12 imputation methods on a large-scale environmental dataset (temperature, soil moisture).
  • Included methods like Spline Interpolation, MissForest, MICE, MCMC, M-RNN, and BRITS.
  • Assessed performance across various missing data proportions (10-50%) and realistic scenarios.

Main Results:

  • Spatial data recovery techniques generally outperformed time-based imputation methods.
  • Matrix completion techniques demonstrated the highest performance in imputing missing values.
  • Effectiveness was evaluated under different missing data percentages and realistic conditions.

Conclusions:

  • Spatial imputation methods are superior for completing missing values in large-scale environmental sensor data.
  • Matrix completion offers a robust solution for data gaps in IoT environmental monitoring.
  • These findings maximize the utility of extensive environmental monitoring investments.