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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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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.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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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Stratified Sampling Method01:16

Stratified 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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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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. 
In analytical chemistry, the choice of...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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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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Subsemble: an ensemble method for combining subset-specific algorithm fits.

Stephanie Sapp1, Mark J van der Laan2, John Canny3

  • 1Department of Statistics, University of California at Berkeley, Berkeley, CA, USA.

Journal of Applied Statistics
|April 30, 2014
PubMed
Summary

Subsemble is a new subset ensemble method that improves prediction performance, especially for small to moderate datasets. This general prediction tool offers a theoretical performance guarantee and practical integration into machine learning libraries.

Keywords:
big datacross-validationensemble methodsmachine learningprediction

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Area of Science:

  • Machine Learning
  • Statistical Prediction
  • Data Science

Background:

  • Ensemble methods are increasingly vital for analyzing massive datasets.
  • Existing methods often focus on large datasets, leaving gaps for smaller ones.

Purpose of the Study:

  • To introduce Subsemble, a versatile subset ensemble prediction method.
  • To demonstrate Subsemble's applicability across datasets of all sizes (small, moderate, large).
  • To provide theoretical performance guarantees and practical implementation strategies.

Main Methods:

  • Subsemble partitions the full dataset into observational subsets.
  • A specified underlying algorithm is trained on each subset.
  • V-fold cross-validation is employed to combine subset-specific fits into a final prediction function.

Main Results:

  • Subsemble offers a theoretical performance guarantee (oracle result).
  • Simulations show Subsemble outperforms single full-dataset fits on small to moderate datasets.
  • Subsemble can be integrated into SuperLearner libraries for comparative performance evaluation.

Conclusions:

  • Subsemble is a beneficial and generalizable prediction tool.
  • It enhances prediction performance, particularly for smaller and moderately sized datasets.
  • The method provides a practical approach for robust ensemble modeling.