Asymptotic Statistics
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Asymptotic statistics is a branch of statistics that deals with the analysis of statistical models as the sample size approaches infinity. It provides a way to make inferences about a population based on a finite sample, using mathematical techniques such as asymptotic theory.

In essence, asymptotic statistics allows us to:

* Make inferences about a population parameter (e.g., mean, variance) based on a sample of data
* Estimate the uncertainty associated with these inferences
* Develop statistical tests and confidence intervals for hypothesis testing

Asymptotic statistics is widely used in various fields, including engineering, economics, finance, and computer science.

Example Use Case: Partial Directed Coherence (PDC)
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The asympPDC package on GitHub provides a MATLAB and Octave implementation of asymptotic statistics for PDC and directed transfer function estimation. It can be used to analyze the connectivity between variables in a system.

# Install asympPDC package
git clone https://github.com/asymppdc/asympPDC.git

# Load asympPDC package
addpath asympPDC

# Estimate PDC and directed transfer function
[coeffs, stats] = pdc(data);

Note: This is a brief example to illustrate the concept. For more information on asymptotic statistics and its applications, please refer to the asympPDC package documentation or relevant literature in the field.