EstemPMM

EstemPMM-package

The EstemPMM package provides robust methods for estimating parameters of linear
models and time series models that are robust to non-Gaussian errors.

internal

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

Polynomial Maximization Method for Non-Gaussian Regression

EstemPMM-package function

<code>lm_pmm2</code> - Fit linear models using PMM2
<code>compare_with_ols</code> - Compare PMM2 with OLS

Linear Regression Functions

<code>ts_pmm2</code> - General function for fitting time series models using PMM2
<code>ar_pmm2</code> - Fit AR models
<code>ma_pmm2</code> - Fit MA models
<code>arma_pmm2</code> - Fit ARMA models
<code>arima_pmm2</code> - Fit ARIMA models
<code>compare_ts_methods</code> - Compare PMM2 with classical methods

Time Series Functions

<code>pmm2_inference</code> - Bootstrap inference for linear models
<code>ts_pmm2_inference</code> - Bootstrap inference for time series models

Statistical Inference

<code>pmm_skewness</code> - Compute skewness
<code>pmm_kurtosis</code> - Compute kurtosis
<code>compute_moments</code> - Compute moments and cumulants

Utilities

Maintainer: Serhii Zabolotnii <a href="/link/zabolotniua%40gmail.com?package=EstemPMM&version=0.1.1" data-mini-rdoc="EstemPMM::zabolotniua@gmail.com">zabolotniua@gmail.com</a> (<a href="https://orcid.org/0000-0003-0242-2234">ORCID</a>)

Author

EstemPMM: Polynomial Maximization Method for Robust Regression and Time Series — EstemPMM-package

<code>lm_pmm2</code> - Fit linear models using PMM2
<code>compare_with_ols</code> - Compare PMM2 with OLS

<code>ts_pmm2</code> - General function for fitting time series models using PMM2
<code>ar_pmm2</code> - Fit AR models
<code>ma_pmm2</code> - Fit MA models
<code>arma_pmm2</code> - Fit ARMA models
<code>arima_pmm2</code> - Fit ARIMA models
<code>compare_ts_methods</code> - Compare PMM2 with classical methods

<code>pmm2_inference</code> - Bootstrap inference for linear models
<code>ts_pmm2_inference</code> - Bootstrap inference for time series models

<code>pmm_skewness</code> - Compute skewness
<code>pmm_kurtosis</code> - Compute kurtosis
<code>compute_moments</code> - Compute moments and cumulants

Maintainer: Serhii Zabolotnii <a href='mailto:zabolotniua@gmail.com'>zabolotniua@gmail.com</a> (<a href='https://orcid.org/0000-0003-0242-2234'>ORCID</a>)

EstemPMM-package: EstemPMM: Polynomial Maximization Method for Robust Regression and Time Series

Description

Arguments

Linear Regression Functions

Time Series Functions

Statistical Inference

Utilities

Author

See Also