Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

600
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
600
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
Multiple Regression01:25

Multiple Regression

3.2K
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...
3.2K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

126
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
126

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Estimation for time-varying coefficient smoothed quantile regression.

Journal of applied statistics·2025
Same author

High-dimensional partially linear functional Cox models.

Biometrics·2025
Same author

Family of bivariate distributions on the unit square: theoretical properties and applications.

Journal of applied statistics·2024
Same author

A Generalized Measure of Cumulative Residual Entropy.

Entropy (Basel, Switzerland)·2022
Same author

Cumulative Residual <i>q</i>-Fisher Information and Jensen-Cumulative Residual <i>χ</i><sup>2</sup> Divergence Measures.

Entropy (Basel, Switzerland)·2022
Same author

Information Generating Function of Ranked Set Samples.

Entropy (Basel, Switzerland)·2021
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 10, 2025

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

3.4K

密度応答と機能的自動回帰的エラープロセスの変数係数付加モデル

Zixuan Han1, Tao Li2, Jinhong You2

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,自動相関でタイムシリーズのデータを正確に分析するための新しい統計モデルを導入します. 変数係数の加算モデルは,密度値の応答におけるシリアル依存を考慮することで推論を改善します.

キーワード:
密度応答機能的な自動回帰エラープロセスログ-量子密度変換変数係数

さらに関連する動画

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.3K

関連する実験動画

Last Updated: Sep 10, 2025

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

3.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.3K

科学分野:

  • 統計について
  • データサイエンス
  • タイムシリーズ分析

背景:

  • タイムシリーズのデータにおける自己相関は,偏った統計的推論につながる可能性があります.
  • 既存のモデルは,密度で評価された応答のシリアル依存性を十分に捉えることができないかもしれません.

研究 の 目的:

  • 密度で評価された応答のための新しい変数係数添加モデルを提案する.
  • シリアル依存を解決するために機能的な自動回帰 (FAR) エラープロセスを組み込む.
  • 連続的に依存するデータを分析するための堅固な推定手順を提供する.

主な方法:

  • 密度関数を線形空間にマッピングするためのログ量子密度変換.
  • 変数係数の初期推定のためのB-spline系列近似.
  • 機能的な自動回帰的エラープロセスを推定するためのスプリング・スムージング技術.
  • 推定誤差処理を調整することで添加物成分を精製する.

主要な成果:

  • 提案された方法は,密度値の応答における自己相関を効果的に考慮します.
  • 理論的性質は,収束率とアシンプトティックな振る舞いを含む.
  • シミュレーション研究と現実世界のデータアプリケーションは,この方法の有効性を示しています.

結論:

  • 機能的な自動回帰的エラープロセスで開発された変数係数の加算モデルは,タイムシリーズのデータに対する改善された統計的推論を提供します.
  • このアプローチは,様々な実用的なアプリケーションで,複雑で連続的に依存する密度値のデータを分析するための貴重なツールを提供します.