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Variance ignoring NaNs


y = nanvar(X)
y = nanvar(X,1)
y = nanvar(X,W)
y = nanvar(X,W,DIM)



Financial times series object.


Weight vector.


Dimension along which the operation is conducted.


nanvar for financial times series objects is based on the Statistics and Machine Learning Toolbox™ function nanvar. See nanvar in the Statistics and Machine Learning Toolbox documentation.

y = nanvar(X) returns the sample variance of the values in a financial time series object X, treating NaNs as missing values. y is the variance of the non-NaN elements of each series in X.

nanvar normalizes y by N1 if N > 1, where N is the sample size of the non-NaN elements. This is an unbiased estimator of the variance of the population from which X is drawn, as long as X consists of independent, identically distributed samples, and data are missing at random. For N = 1, y is normalized by N.

y = nanvar(X,1) normalizes by N and produces the second moment of the sample about its mean. nanvar(X, 0) is the same as nanvar(X).

y = nanvar(X,W) computes the variance using the weight vector W. The length of W must equal the length of the dimension over which nanvar operates, and its non-NaN elements must be nonnegative. Elements of X corresponding to NaN elements of Ware ignored.

y = nanvar(X,W,DIM) takes the variance along dimension DIM of X.


To compute nanvar:

f = fints((today:today+1)', [4 -2 1; 9  5 7])
f.series1(1) = nan;
f.series3(2) = nan;

nvar = nanvar(f)
nvar =
         0   24.5000         0

Related Examples

See Also

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Introduced before R2006a

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