# Documentation

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# nanvar

Variance ignoring `NaN`s

## Syntax

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

## Arguments

 `X` Financial times series object. `W` Weight vector. `DIM` Dimension along which the operation is conducted.

## Description

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

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

`nanvar` normalizes `y` by `N``1` 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 `W`are ignored.

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

## Examples

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 ```

## See Also

### Topics

#### Introduced before R2006a

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