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Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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Margin of Error01:27

Margin of Error

The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...

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

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Published on: October 11, 2018

Correcting the optimal resampling-based error rate by estimating the error rate of wrapper algorithms.

Christoph Bernau1, Thomas Augustin, Anne-Laure Boulesteix

  • 1Department for Medical Informatics, Biometry and Epidemiology, Marchioninistr. 15, D-81377, Munich, Germany.

Biometrics
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to correct over-optimistic prediction errors in high-dimensional classification tasks by addressing tuning bias. The approach offers competitive estimates with lower computational cost compared to existing methods like internal cross-validation (ICV).

Keywords:
ClassificationHigh-dimensional dataMethod selection biasRepeated subsamplingTuning bias

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Learning

Background:

  • High-dimensional binary classification, common in microarray analysis, often suffers from tuning bias.
  • Reporting performance based solely on the best tuning parameter leads to over-optimistic error predictions.
  • Existing methods like internal cross-validation (ICV) aim to correct this bias but can be computationally intensive.

Purpose of the Study:

  • To develop a novel, computationally efficient method for correcting tuning bias in high-dimensional classification.
  • To provide a smooth, statistically robust alternative to ICV with guaranteed error bounds.
  • To extend bias correction principles to address method selection bias.

Main Methods:

  • A subsampling-based estimator is developed, decomposing the unconditional error rate.
  • The proposed method estimates the error rate of wrapper algorithms, akin to ICV.
  • It functions as a weighted mean of errors across different tuning parameter values.

Main Results:

  • The new method provides competitive error rate estimates compared to ICV on microarray and simulated data.
  • It offers intuitive bounds for the corrected error, unlike standard ICV.
  • The approach demonstrates significantly lower computational cost than ICV.

Conclusions:

  • The proposed bias correction method effectively reduces over-optimistic predictions in high-dimensional classification.
  • It presents a computationally efficient and statistically sound alternative to ICV.
  • The study highlights the potential of bias correction for both tuning and method selection biases.