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

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

931
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
931
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

726
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
726
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

766
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
766
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

948
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
948
Elasticity01:12

Elasticity

5.0K
Elasticity is the ability of an object to withstand the effects of distortion and to return to its original size and shape once the forces causing deformation are removed. When an elastic material deforms under the action of an external force, it experiences internal resistance to the deformation. However, if no external force is applied, it returns to its original state.
The elasticity of an object can be described by a stress-strain curve, which represents the relationship between stress...
5.0K
Elasticity in Concrete01:20

Elasticity in Concrete

372
Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear...
372

You might also read

Related Articles

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

Sort by
Same author

Reliable quantification of renal function from frozen blood samples.

medRxiv : the preprint server for health sciences·2026
Same author

CD70 drives cSCC growth by linking DNA damage response, inflammation, and tumor-stromal signaling.

Cell death & disease·2026
Same author

Evaluation of Dronabinol to Decrease Opioid Use for Cancer-Induced Bone Pain.

The oncologist·2026
Same author

Community health worker intervention to reduce worker exposure to volatile organic compounds in small business auto and beauty shops in a marginalized community: A cluster randomized controlled trial.

PloS one·2026
Same author

Development and usability of a mobile ecological momentary assessment platform for dietary surveillance in the U.S.

The international journal of behavioral nutrition and physical activity·2026
Same author

Center and Geographic Variability in Acceptance of the First Donor Heart by Race.

Circulation. Heart failure·2026
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

Related Experiment Video

Updated: Feb 13, 2026

Regular Care and Maintenance of a Zebrafish Danio rerio Laboratory: An Introduction
11:47

Regular Care and Maintenance of a Zebrafish Danio rerio Laboratory: An Introduction

Published on: November 18, 2012

92.1K

Regularized continuous-time Markov Model via elastic net.

Shuang Huang1, Chengcheng Hu1, Melanie L Bell1

  • 1Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, U.S.A.

Biometrics
|March 14, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new regularized continuous-time Markov model using elastic net penalties to handle complex disease state transitions in panel data. The method efficiently selects variables and estimates parameters, improving stability for longitudinal health analyses.

Keywords:
Continuous-time Markov modelElastic net penaltyPanel dataRegularization

More Related Videos

The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton
08:50

The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton

Published on: March 10, 2023

1.2K
Autonomously Bioluminescent Mammalian Cells for Continuous and Real-time Monitoring of Cytotoxicity
04:47

Autonomously Bioluminescent Mammalian Cells for Continuous and Real-time Monitoring of Cytotoxicity

Published on: October 28, 2013

10.5K

Related Experiment Videos

Last Updated: Feb 13, 2026

Regular Care and Maintenance of a Zebrafish Danio rerio Laboratory: An Introduction
11:47

Regular Care and Maintenance of a Zebrafish Danio rerio Laboratory: An Introduction

Published on: November 18, 2012

92.1K
The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton
08:50

The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton

Published on: March 10, 2023

1.2K
Autonomously Bioluminescent Mammalian Cells for Continuous and Real-time Monitoring of Cytotoxicity
04:47

Autonomously Bioluminescent Mammalian Cells for Continuous and Real-time Monitoring of Cytotoxicity

Published on: October 28, 2013

10.5K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Continuous-time Markov models analyze disease state transitions in longitudinal panel data.
  • Estimating covariate effects in these models can lead to parameter explosion and overfitting.
  • Existing methods struggle with stability when dealing with numerous parameters.

Purpose of the Study:

  • To propose a novel regularized continuous-time Markov model for analyzing longitudinal transitions.
  • To address challenges of parameter estimation and variable selection in complex models.
  • To develop a stable and efficient method for analyzing disease progression.

Main Methods:

  • Developed a regularized continuous-time Markov model incorporating an elastic net penalty.
  • Derived an efficient, automatic, and data-driven coordinate descent algorithm for optimization.
  • Extended the model to handle scenarios with exactly known death times.

Main Results:

  • The elastic net penalty enables simultaneous variable selection and parameter estimation.
  • The coordinate descent algorithm efficiently solves the penalized optimization problem.
  • The proposed method demonstrated superior performance in simulation studies and real-world data.

Conclusions:

  • The regularized continuous-time Markov model offers a robust solution for analyzing complex longitudinal state transitions.
  • The elastic net penalty and coordinate descent algorithm effectively manage high-dimensional parameter spaces.
  • This approach enhances the analysis of disease progression and survival data.