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Related Concept Videos

Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
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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...
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Sampling materials are classified into three main types: solid, liquid, and gas.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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.
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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

Bai-chuan Deng1, Yong-huan Yun, Yi-zeng Liang

  • 1Department of Chemistry, University of Bergen, Bergen N-5007, Norway.

The Analyst
|August 2, 2014
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Summary
This summary is machine-generated.

A new variable selection algorithm, Variable Iterative Space Shrinkage Approach (VISSA), statistically evaluates performance at each step. VISSA demonstrates superior prediction ability for Near-Infrared (NIR) data calibration compared to existing methods.

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Area of Science:

  • Chemometrics
  • Data Science
  • Machine Learning

Background:

  • Variable selection is crucial for building robust chemometric models.
  • Existing methods often lack rigorous statistical evaluation of variable subspaces during optimization.
  • Model Population Analysis (MPA) provides a framework for systematic variable space exploration.

Purpose of the Study:

  • To introduce a novel optimization algorithm, Variable Iterative Space Shrinkage Approach (VISSA), for enhanced variable selection.
  • To statistically evaluate variable space performance iteratively within the optimization process.
  • To improve prediction accuracy in chemometric modeling, specifically for Near-Infrared (NIR) data calibration.

Main Methods:

  • Development of the Variable Iterative Space Shrinkage Approach (VISSA) based on Model Population Analysis (MPA).
  • Introduction of Weighted Binary Matrix Sampling (WBMS) for generating representative variable subspaces.
  • Implementation of a two-rule optimization strategy: iterative shrinkage and performance improvement of the variable space.

Main Results:

  • VISSA demonstrated superior prediction ability compared to established methods like CARS, MCUVE, and IRIV for NIR data calibration.
  • The algorithm's core strategy ensures that each new variable space outperforms the previous one, a key differentiator.
  • VISSA is user-friendly, requiring minimal, insensitive parameters and featuring automatic termination.

Conclusions:

  • VISSA offers a statistically rigorous and effective approach to variable selection in chemometrics.
  • The algorithm's performance advantages make it a valuable tool for NIR data analysis and model optimization.
  • Freely available Matlab code facilitates the adoption and further development of VISSA.