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BiweightLocation—Wolfram Language Documentation

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• See Also
  • Median
  • Mean
  • TrimmedMean
  • WinsorizedMean
  • SpatialMedian
  • CentralFeature
  • BiweightMidvariance
• Related Guides
  • Descriptive Statistics
  • Date & Time
  • Robust Descriptive Statistics

BiweightLocation[list]

gives the value of the biweight location estimator of the elements in list.

BiweightLocation[list,c]

gives the value of the biweight location estimator with scaling parameter c.

• BiweightLocation is a robust location estimator.
• BiweightLocation is given by a weighted mean of the elements. Elements farther from the center have lower weights.
• The width scale of the weight function is controlled by a parameter c. Larger c indicates more data values are included in the computation of the statistic, and vice versa.
• For the list {x₁,x₂,…,x_n}, the value of the biweight location estimator is given by , where and is Median[{x₁-x^*,x₂-x^*,…,x_n-x^*}]. The value x^* of the estimator is computed iteratively, with the initial value chosen automatically by default.
• BiweightLocation[list] is equivalent to BiweightLocation[list,6].
• BiweightLocation[{{x₁,y₁,…},{x₂,y₂,…},…}] gives {BiweightLocation[{x₁,x₂,…}],BiweightLocation[{y₁,y₂,…}],…}.
• BiweightLocation allows c to be any positive real number.
• The following options can be given:
• The setting Method{"InitialPoint"x₀} allows for a custom initial value .

Same inputs with different precisions:

Biweight location with different scaling parameters:

Biweight location for a matrix gives columnwise estimate:

Biweight location of a large array:

Find a biweight location of a TimeSeries:

The biweight location depends only on the values:

Biweight location works with data involving quantities:

Compute biweight location of dates:

Compute biweight location of times:

List of times with different time zone specifications:

The value of BiweightLocation is computed iteratively. Limit the number of iterations attempted in the computation:

Adjust the starting value in the computation of BiweightLocation:

Limit the number of iterations with a better starting value:

Obtain a robust estimate of location when outliers are present:

Extreme values have a large influence on the Mean:

Consider data from a Gaussian mixture distribution:

Estimate the center with Mean:

The sample mean estimator has a large spread for non-Gaussian data. The standard deviation of the estimator is:

Estimate the center with BiweightLocation:

Use bootstrapping to assess the spread of the biweight location estimator:

Simulate a trajectory with heavy-tailed measurement noise:

The underlying signal and simulated path with noise:

Smooth the trajectory using a moving BiweightLocation:

Increasing the block size gives a smoother trajectory:

Compute the biweight location of a sample:

Values outside of the interval have no effect on the statistic. Here is the value of biweight location and is the median absolute deviation with respect to . is a scaling parameter with default value equal to 6:

The shape of the weight function w(x) being used in computing biweight location:

Multiply the smallest and the largest values in the sample by 2 and compute the biweight location again:

For normally distributed samples, BiweightLocation and Mean are nearly the same:

For non-normally distributed samples such as data from CauchyDistribution, BiweightLocation gives a better estimate of the center location than Mean:

BiweightLocation approaches Mean for large values of c:

Variation of biweight location around the mean of univariate data depending on the scaling factor c:

Variation of biweight location around the mean of bivariate data depending on the scaling factor c:

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