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Robust Descriptive Statistics—Wolfram Language Documentation

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• Functions
  • BiweightLocation
  • BiweightMidvariance
  • CentralFeature
  • Commonest
  • EstimatedDistribution
  • FindDistributionParameters
  • InterquartileRange
  • Max
  • Median
  • MedianDeviation
  • Min
  • MinMax
  • Ordering
  • QnDispersion
  • Quantile
  • QuartileDeviation
  • Quartiles
  • QuartileSkewness
  • RankedMax
  • RankedMin
  • SnDispersion
  • Sort
  • SpatialMedian
  • TakeLargest
  • TakeSmallest
  • TrimmedMean
  • TrimmedVariance
  • WinsorizedMean
  • WinsorizedVariance
• Related Guides
  • Descriptive Statistics
  • Hypothesis Tests
  • Probability & Statistics
  • Statistical Moments & Generating Functions
  • Statistical Model Analysis
  • Data Transforms & Smoothing
  • Curve Fitting
  • Random Sampling
• Tech Notes
  • Basic Statistics
  • Descriptive Statistics
  • Ordering in Lists

Descriptive statistics with consistent performance against data from different distributions are considered robust, as they are less affected by outliers. These estimators are generally defined via order statistics or optimizing certain objective functions of data.

The Wolfram Language provides a variety of robust estimators for different applications, including location, dispersion and shape characterization. They are useful in outlier detection and parametric estimation.

Median  ▪  Commonest  ▪  TrimmedMean  ▪  WinsorizedMean  ▪  SpatialMedian  ▪  CentralFeature  ▪  BiweightLocation

TrimmedVariance  ▪  WinsorizedVariance  ▪  MedianDeviation  ▪  InterquartileRange  ▪  QuartileDeviation   ▪  QnDispersion   ▪  SnDispersion   ▪  BiweightMidvariance

QuartileSkewness  ▪  EstimatedDistribution  ▪  FindDistributionParameters

Min  ▪  Max  ▪  MinMax  ▪  Sort  ▪  Ordering  ▪  RankedMin  ▪  RankedMax  ▪  Quantile  ▪  Quartiles  ▪  TakeLargest  ▪  TakeSmallest

▪
• Basic Statistics
▪
• Descriptive Statistics
▪
• Ordering in Lists

▪
• Descriptive Statistics
▪
• Hypothesis Tests
▪
• Probability & Statistics
▪
• Statistical Moments & Generating Functions
▪
• Statistical Model Analysis
▪
• Data Transforms & Smoothing
▪
• Curve Fitting
▪
• Random Sampling

• Demonstrations related to Descriptive Statistics (Wolfram Demonstrations Project)

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