pmm2_inference

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

pmm2_inference function

<dl><dt>object</dt>
<dd>object of class PMM2fit</dd>
<dt>formula</dt>
<dd>the same formula that was used initially</dd>
<dt>data</dt>
<dd>data frame that was used initially</dd>
<dt>B</dt>
<dd>number of bootstrap replications</dd>
<dt>seed</dt>
<dd>(optional) for reproducibility</dd>
<dt>parallel</dt>
<dd>logical, whether to use parallel computing</dd>
<dt>cores</dt>
<dd>number of cores to use for parallel computing, defaults to auto-detect</dd></dl>

Arguments

Bootstrap inference for PMM2 fit — pmm2_inference

<dl>

<dt>object</dt>
<dd>object of class PMM2fit</dd>


<dt>formula</dt>
<dd>the same formula that was used initially</dd>


<dt>data</dt>
<dd>data frame that was used initially</dd>


<dt>B</dt>
<dd>number of bootstrap replications</dd>


<dt>seed</dt>
<dd>(optional) for reproducibility</dd>


<dt>parallel</dt>
<dd>logical, whether to use parallel computing</dd>


<dt>cores</dt>
<dd>number of cores to use for parallel computing, defaults to auto-detect</dd>

</dl>

pmm2_inference: Bootstrap inference for PMM2 fit

Description

Usage

Value

Arguments