pmm_kurtosis

Implements the Polynomial Maximization Method ('PMM') for parameter estimation
in linear and time series models when error distributions deviate from normality.
The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary
least squares ('OLS') when errors exhibit significant skewness. Includes methods
for linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference.
Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) <doi:10.1007/978-3-319-77179-3_75>,
Zabolotnii, Tkachenko, and Warsza (2022) <doi:10.1007/978-3-031-03502-9_37>, and
Zabolotnii, Tkachenko, and Warsza (2023) <doi:10.1007/978-3-031-25844-2_21>.

Serhii Zabolotnii

EstemPMM

Polynomial Maximization Method for Non-Gaussian Regression

pmm_kurtosis function

<dl><dt>x</dt>
<dd>numeric vector</dd>
<dt>excess</dt>
<dd>logical, whether to return excess kurtosis (kurtosis - 3)</dd></dl>

Arguments

Calculate kurtosis from data — pmm_kurtosis

<dl>

<dt>x</dt>
<dd>numeric vector</dd>


<dt>excess</dt>
<dd>logical, whether to return excess kurtosis (kurtosis - 3)</dd>

</dl>

pmm_kurtosis: Calculate kurtosis from data

Description

Usage

Value

Arguments