WeightedData

WeightedData[{x1,x2,},{w1,w2,}]

represents observations xi with weights wi.

WeightedData[{x1,x2,},fn]

represents observations xi with weighting function fn.

Details

  • WeightedData augments data with weights for each data point.
  • The data {x1,x2,} and weights {w1,w2,} should be lists of equal length.
  • The weight function fn is applied to the list {x1,x2,} and should return an explicit list of weights {w1,w2,}.
  • WeightedData can be used in statistics functions including:
  • Mean,Variance,descriptive statistics functions
    EmpiricalDistribution,nonparametric distribution estimation
    EstimatedDistribution,parametric distribution estimation
  • WeightedData[{x1,x2,}] gives data with equal weights.
  • Properties of WeightedData can be obtained by specifying WeightedData[]["property"].
  • A list of available properties can be obtained using WeightedData[]["Properties"].
  • WeightedData has the following properties:
  • "EmpiricalPDF"data values and estimated weights
    "InputData"unweighted input data values
    "MetaInformation"a list containing meta-information rules
    "Weights"a list containing the data weights

Examples

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Basic Examples  (1)

Create data with weights:

Compute a weighted Mean and StandardDeviation:

Scope  (10)

Create weighted univariate data:

Some weighted descriptive statistics:

Add weights to a set of multivariate values:

A set of weighted multivariate descriptive statistics:

Use a pure function to create weighted data values:

Weighted means and variances:

Visualize the impact of the various weighting schemes:

Fit nonparametric distributions to weighted data:

Fit parametric distributions to weighted data:

Compare the estimated and empirical distributions:

Extract the input data from a WeightedData object:

Compare the distributions of the unweighted and weighted data:

Obtain the weights from a WeightedData object:

Visually inspect the effect of the weights on the data values:

Compute a weighted mean from the empirical PDF:

The weighted mean can be computed directly using Mean:

Find the weighted average of an irregularly sampled TimeSeries:

Compare with the average of values:

Create weighted data involving quantities:

Some weighted descriptive statistics:

Applications  (2)

Create a weighted histogram:

Estimate confidence interval for maximum likelihood estimates of distribution parameters:

Apply fractional random weight bootstrap to estimate confidence interval, by repeating weighted estimation with weights sampled from a DirichletDistribution with unit parameters:

Generate one thousand estimates of the distribution parameters:

Visualize bootstrap estimates:

Fit joint Gaussian distribution to the bootstrapped parameters:

Properties & Relations  (2)

Descriptive statistics are based on the underlying EmpiricalDistribution:

Sample estimates are given when they differ from population estimates:

WeightedData works for TimeSeries objects:

For , the weights are proportional to :

Compare with binsampled time average:

Compute integration limits:

Introduced in 2012
 (9.0)