AIC,PMM2fit-method

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

AIC,PMM2fit-method function

<dl><dt>object</dt>
<dd>PMM2fit object</dd>
<dt>...</dt>
<dd>Additional arguments (not used)</dd>
<dt>k</dt>
<dd>Penalty per parameter to be used; default is 2</dd></dl>

Arguments

Calculate AIC for PMM2fit object — AIC,PMM2fit-method

<dl>

<dt>object</dt>
<dd>PMM2fit object</dd>


<dt>...</dt>
<dd>Additional arguments (not used)</dd>


<dt>k</dt>
<dd>Penalty per parameter to be used; default is 2</dd>

</dl>

AIC,PMM2fit-method: Calculate AIC for PMM2fit object

Description

Usage

Value

Arguments