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Related Concept Videos

Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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...
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...
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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Basics of Multivariate Analysis in Neuroimaging Data
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Published on: July 24, 2010

Classification and error estimation for discrete data.

Ulisses M Braga-Neto1

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77845, USA.

Current Genomics
|May 4, 2010
PubMed
Summary
This summary is machine-generated.

This study reviews discrete classification and error estimation for genomic data. It focuses on methods suitable for small sample sizes, crucial for reliable genomic signal processing and gene expression analysis.

Keywords:
Genomicsclassificationcoefficient of determination.discrete histogram ruleensemble methodserror estimationleave-one-outresubstitutionsampling distribution

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Area of Science:

  • Genomic Signal Processing
  • Bioinformatics
  • Machine Learning

Background:

  • Discrete classification is vital in genomics, particularly for gene expression data analysis and regulatory network inference.
  • Evaluating classifier performance via error estimation is essential but challenging with limited genomic datasets.
  • Small sample sizes are prevalent in genomic applications, complicating classifier design and error estimation.

Purpose of the Study:

  • To provide a comprehensive review of discrete classification and error estimation methodologies.
  • To specifically address the challenges posed by small sample data in genomics.
  • To analyze the performance and asymptotic behavior of these methods in genomic contexts.

Main Methods:

  • Review of existing literature on discrete classification techniques.
  • Analysis of error estimation strategies for discrete classifiers.
  • Focus on performance evaluation in small sample size scenarios.
  • Examination of asymptotic properties of classification and error estimation methods.

Main Results:

  • Identified key methodologies for discrete classification in genomics.
  • Highlighted the impact of small sample sizes on classifier performance and error estimation reliability.
  • Discussed the asymptotic behavior of methods under various genomic data conditions.

Conclusions:

  • Effective discrete classification and error estimation are critical for genomic applications.
  • Specialized methods are needed to handle the small sample data prevalent in genomics.
  • Understanding asymptotic behavior aids in selecting robust classification and error estimation techniques for genomic signal processing.