Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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

376
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...
376
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

60
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
60
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

190
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
190
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

199
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...
199
Linear time-invariant Systems01:23

Linear time-invariant Systems

209
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
209
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

167
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
167

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Distinct effects of GES variants on ceftazidime-avibactam and imipenem-relebactam resistance in carbapenem-resistant <i>Pseudomonas aeruginosa</i>: a ten-year retrospective study.

Emerging microbes & infections·2026
Same author

Effects of silicone-based formulations on laser-induced microscopic lesions: results from a randomized trial using an in vivo human wound healing model.

Scientific reports·2026
Same author

Full-color 3D visualization with Janus metafiber.

Nature communications·2026
Same author

Stepwise KPC mutational accumulation confers high-level ceftazidime-avibactam resistance in ST11 carbapenem-resistant Klebsiella pneumoniae.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2026
Same author

Associations of single and mixed air pollution exposure with lung function and incident pulmonary fibrosis: The mediation effect of low-grade inflammation.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Succession Dominates Alpha Male Replacement in Despotic Rhesus Monkeys: Insights from a Long-Term Study in the Taihang Mountains, Henan Province, China.

Animals : an open access journal from MDPI·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
查看所有相关文章

相关实验视频

Updated: May 31, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180&#176; Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.6K

对于时间序列模型的强大的两步波形基推理.

Stéphane Guerrier1, Roberto Molinari2, Maria-Pia Victoria-Feser1

  • 1University of Geneva, Geneva, Switzerland.

Journal of the American Statistical Association
|January 23, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了潜在时间序列模型的强大的两步估计框架,解决了异常值和计算复杂性等挑战. 这种新方法增强了各种科学和经济领域的数据分析.

关键词:
波束时刻的一般化方法大规模时间序列.基于尺度的差异分析.信号处理 信号处理国家空间模型.波段差异的波段差异.

更多相关视频

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.1K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.0K

相关实验视频

Last Updated: May 31, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180&#176; Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.6K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.1K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.0K

科学领域:

  • 统计 统计 统计 统计
  • 时间序列分析时间序列分析
  • 信号处理 信号处理

背景情况:

  • 隐性时间序列模型,包括自回归移动平均 (ARMA) 模型,在生物学,生态学,工程学和经济学中至关重要.
  • 分析这些模型的挑战包括数据异常值,大型数据集的高计算成本和复杂的模型选择.
  • 现有的方法往往无法同时解决这些问题.

研究的目的:

  • 提出一个总体框架,用于对潜伏时间序列模型进行可靠的两步估计.
  • 共同应对异常值,计算复杂性和模型选择等挑战.
  • 为分析复杂时间序列数据提供实用和高效的方法.

主要方法:

  • 开发一个有边界的影响M估计器波波幅变异处理异常值.
  • 确定用于推断的波形变量估计器的联合异面正常性的条件.
  • 应用波束时刻 (GMWM) 的通用方法,以进行可靠的两步估计.

主要成果:

  • 拟议的强大的两步估计框架有效地处理异常值,并减少计算复杂性.
  • 强大的估计器的异面性质是使用GMWM框架来得出的.
  • 模拟研究表明,强大的GMWM估计器具有良好的有限样本性能.

结论:

  • 开发的框架为潜在时间序列分析中常见的挑战提供了同时解决方案.
  • 强大的GMWM估计器实际上是相关的,并在模拟中表现良好.
  • 这种方法提高了时间序列分析在各种科学领域的可靠性和效率.