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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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 number is...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...

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Related Experiment Video

Updated: Jun 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Detecting influential observations by cluster analysis and Monte Carlo cross-validation.

Xihui Bian1, Wensheng Cai, Xueguang Shao

  • 1College of Chemistry, Nankai University, Tianjin, 300071, PR China.

The Analyst
|September 11, 2010
PubMed
Summary

A novel method identifies influential observations in Partial Least Squares (PLS) models using Monte Carlo cross-validation (MCCV) and Principal Component Analysis (PCA). This approach effectively detects outliers impacting model performance in spectral data analysis.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Last Updated: Jun 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Chemometrics
  • Data Science
  • Spectroscopy

Background:

  • Identifying influential observations is crucial for robust model development in industrial and laboratory settings.
  • Existing methods for outlier detection can be challenging and computationally intensive.
  • High-performance models rely on accurate data preprocessing and validation.

Purpose of the Study:

  • To develop a new, intuitive, and veracious method for detecting influential observations in Partial Least Squares (PLS) modeling.
  • To improve the reliability and accuracy of PLS models by identifying and addressing data points that disproportionately affect model outcomes.
  • To provide a robust approach for data quality assessment in quantitative spectral modeling.

Main Methods:

  • A novel approach based on the effect of observations on PLS models.
  • Utilizing Monte Carlo cross-validation (MCCV) to generate numerous PLS models.
  • Applying Principal Component Analysis (PCA) to the regression coefficients of the generated PLS models to cluster samples.
  • Identifying influential observations based on their frequency within different sample clusters in the principal component space.

Main Results:

  • The proposed method effectively clusters PLS models based on the presence of influential observations.
  • Influential observations are recognized by their distinct frequency patterns across different sample groups.
  • Demonstrated efficacy in quantitatively modeling Near-Infrared (NIR) and Raman spectra across three distinct examples.
  • The method provides an intuitive and veracious means of identifying problematic data points.

Conclusions:

  • The developed method offers an intuitive and veracious approach to detecting influential observations in PLS modeling.
  • This technique enhances the reliability of quantitative spectral analysis by identifying outliers.
  • The integration of MCCV and PCA provides a powerful tool for robust data modeling in chemometrics and related fields.