# mkurtosis

Syntax

mkurtosis(X, window, [biased=true], [minPeriods])

Arguments

biased is a Boolean value indicating whether the result is biased. The default value is true, meaning the bias is not corrected.

Please see Moving Functions (m-functions) for the parameters and windowing logic.

Details

Calculate the moving kurtosis of X in a sliding window.

Examples

```\$ m=matrix(1 9 3 100 3 2 1 -100 9 10000, 1 2 3 4 5 6 7 8 9 100);
\$ m.mkurtosis(8);
```

#0

#1

3.989653641279048

1.761904761904762

3.989840910744778

1.761904761904762

6.140237905908072

6.101712240467206

```\$ m.rename!(date(2020.04.06)+1..10, `col1`col2)
\$ m.setIndexedMatrix!()
\$ mkurtosis(m, 8d)
```

label

col1

col2

2020.04.07

2020.04.08

2020.04.09

1.5

1.5

2020.04.10

2.3195

1.64

2020.04.11

3.2251

1.7

2020.04.12

4.163

1.7314

2020.04.13

5.1141

1.75

2020.04.14

3.9897

1.7619

2020.04.15

3.9898

1.7619

2020.04.16

6.1402

6.1017

```\$ mkurtosis(m, 1w)
```

label

col1

col2

2020.04.07

2020.04.08

2020.04.09

1.5

1.5

2020.04.10

2.3195

1.64

2020.04.11

3.2251

1.7

2020.04.12

4.163

1.7314

2020.04.13

5.1141

1.75

2020.04.14

3.4937

1.75

2020.04.15

3.4937

1.75

2020.04.16

5.1645

5.145

The default case of kurtosis in DolphinDB is biased (biased = true), while in pandas and Excel it is unbiased estimation, and the kurtosis value 3 of the normal distribution is subtracted.

The following example illustrates the equivalent conversion between the two when using a sliding window:

```python
\$ m = [[1111,2], [323,9], [43,12], [51,32], [6,400]]
\$ df = pandas.DataFrame(m)
\$ y = df.rolling(4).kurt()

dolphindb
\$ m=matrix(1111 323 43 51 6, 2 9 12 32 400)
\$ m.mkurtosis(4, false)-3
```

#0

#1

2.504252

2.366838

3.675552

3.941262

Related function: kurtosis