What are the applications of higher order statistics?

In summary, higher order statistics refer to the third and fourth central moments of a distribution, which measure skewness and kurtosis respectively. These statistics are useful in determining the randomness and shape of a distribution, and can be tested through formal statistical tests or by plotting a histogram.
  • #1
fisico30
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Hello Forum,

I am not clear on what higher order statistics actually mean. I know that if a process is Gaussian, it is fully described by its mean and variance. The higher order statistics are zero or redundant...IF the process is not Gaussian, then the HOS are useful...

1) How do we determine if a process is Gaussian first of all?
2) What type of operations do we mean with higher order statistics?

thanks
fisico30
 
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  • #2
fisico30 said:
Hello Forum,

I am not clear on what higher order statistics actually mean. I know that if a process is Gaussian, it is fully described by its mean and variance. The higher order statistics are zero or redundant...IF the process is not Gaussian, then the HOS are useful...

1) How do we determine if a process is Gaussian first of all?
2) What type of operations do we mean with higher order statistics?

thanks
fisico30

Skewness, which is the third central moment determines how symetric a distribution is. The four central moment Kurtosis determines how tail heavy a distribution. If a distribution is more tail heavy then there is greater variance in estimating the mean. There are formal statistics test for testing how well a process fits a given random distribution. However, you can get some idea of what kind of distiribution might make a good fit just by plotting a histogram.
 

What are higher order statistics?

Higher order statistics refer to statistical measures that go beyond the traditional mean, median, and mode. These measures include skewness, kurtosis, and various types of moments.

Why are higher order statistics important?

Higher order statistics provide a more comprehensive understanding of a dataset by capturing additional information about its shape, asymmetry, and peakedness. They can also help identify outliers and underlying patterns in the data.

How are higher order statistics calculated?

The calculation of higher order statistics involves using mathematical formulas that take into account the values and frequencies of the data points. For example, skewness is calculated by dividing the third moment by the cube of the standard deviation.

What is the difference between skewness and kurtosis?

Skewness measures the asymmetry of a dataset, while kurtosis measures the peakedness of a dataset. A perfectly symmetrical dataset will have a skewness of 0, while a dataset with a higher kurtosis will have a more peaked distribution.

How can higher order statistics be used in practical applications?

Higher order statistics have many practical applications in various fields such as finance, economics, and engineering. They can be used to analyze financial data, model stock market trends, and improve the performance of machine learning algorithms.

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