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

Residual Plots01:07

Residual Plots

5.0K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
5.0K
Significance Testing: Overview01:04

Significance Testing: Overview

3.8K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
3.8K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.0K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Fisher's Exact Test01:08

Fisher's Exact Test

791
Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
791

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

Accelerating item factor analysis on GPU with Python package xifa.

Behavior research methods·2023
Same author

Condomless Anal Sex Associated With Heterogeneous Profiles Of HIV Pre-Exposure Prophylaxis Use and Sexual Activities Among Men Who Have Sex With Men: A Latent Class Analysis Using Sex Diary Data on a Mobile App.

Journal of medical Internet research·2021
Same author

Mobile App (UPrEPU) to Monitor Adherence to Pre-exposure Prophylaxis in Men Who Have Sex With Men: Protocol for a User-Centered Approach to Mobile App Design and Development.

JMIR research protocols·2020
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026
Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026
Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026
Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026
Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

機械学習における特性の重要性に関する残留変位テスト

Po-Hsien Huang1

  • 1National Chengchi University, Taipei City, Taiwan.

The British journal of mathematical and statistical psychology
|August 30, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,機械学習 (ML) の仮説テストのための残留変位テスト (RPT) を導入します. RPT-Xは機能の有意性を効果的に評価し,さまざまなMLモデルで統計的正確性を維持します.

キーワード:
特徴の重要性機械学習パルムテーションテスト

さらに関連する動画

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

関連する実験動画

Last Updated: Sep 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

科学分野:

  • 心理学
  • コンピュータ科学
  • 統計について

背景:

  • 伝統的な心理学的研究は,仮説のテストのために線形モデルを大きく利用しています.
  • 機械学習 (ML) は,複雑で非線形変数の関係を探求するための高度な方法を提供します.
  • MLにおける現在の機能重要性ツールには,堅固な統計的推論能力が欠けている.

研究 の 目的:

  • 機械学習の枠組みの中で仮説テストのための統計的に健全な方法を開発する.
  • MLモデルにおける特徴の有意性を評価するためのツールとして,残留変位テスト (RPT) を導入する.
  • "ブラックボックス"MLアルゴリズムの解釈のための推論統計のギャップを埋める.

主な方法:

  • 余剰変異試験の2つのバリエーションを導入しました. Y (RPT-Y) のRPTとX (RPT-X) のRPTです.
  • RPT-Yは,他の特徴に条件付けられたラベル残留物である.
  • RPT-Xは,他の特徴に条件付けられた標的の特徴の残留値を変換します.
  • 様々なMLアルゴリズムで 総合的なシミュレーション研究を行いました

主要な成果:

  • RPT-Xは,名目レベル以下の安定した経験的タイプIエラー率を示した.
  • RPT-Xは回帰と分類の両方で適切な統計力を示しました.
  • この研究では,幅広いMLアルゴリズムでRPT-Xの性能を検証した.

結論:

  • 残留パルムテーション試験,特にRPT-Xは,MLにおける統計的推論のための有効なアプローチを提供します.
  • RPT-Xは仮説テストの貴重なツールであり,MLモデルの解釈性を高めます.
  • この発見は,RPT-Xの心理学研究および他のMLの応用におけるより広範な採用を支持する.