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Related Concept Videos

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.
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

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An R-Based Landscape Validation of a Competing Risk Model
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Estimating misclassification error: a closer look at cross-validation based methods.

Songthip Ounpraseuth1, Shelly Y Lensing, Horace J Spencer

  • 1Department of Biostatistics, University of Arkansas for Medical Sciences, 4301 W. Markham St. Slot 781, Little Rock, AR 72205, USA. STOunpraseuth@uams.edu

BMC Research Notes
|November 30, 2012
PubMed
Summary
This summary is machine-generated.

K-fold cross-validation (CV) is recommended over bootstrap cross-validation (BCV) for estimating classifier error. BCV exhibits substantial negative bias, outweighing its reduced variance, making k-fold CV a more reliable choice for generalization error estimation.

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Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Bioinformatics

Background:

  • Traditional cross-validation (CV) methods use sampling without replacement.
  • Bootstrap methods offer reduced variation alternatives for estimating classifier error.
  • Monte Carlo (MC) simulation studies compare CV methods for estimating misclassification error.

Purpose of the Study:

  • Compare a new bootstrap cross-validation (BCV) method to k-fold CV.
  • Evaluate their performance in estimating classifier error.
  • Investigate bias and variance characteristics in error estimation.

Main Methods:

  • Conducted a Monte Carlo simulation study.
  • Compared k-fold CV and a novel BCV method.
  • Validated findings with a real-world breast cancer gene-expression dataset.

Main Results:

  • K-fold CV showed modest positive bias, while BCV exhibited substantial negative bias.
  • BCV's extreme negative bias was confirmed in simulations and a real dataset.
  • CV exercises for estimating conditional error can have problematic design flaws.

Conclusions:

  • Recommend k-fold CV over BCV for estimating a classifier's generalization error.
  • BCV's significant negative bias is unacceptable despite reduced variance.
  • Using CV to estimate fixed misclassification error conditional on training data is problematic.