Statsmodels Examples Python Robust Models 1 Py At Main Github

Leo Migdal

-Dec 4, 2025, 11:39 AM

statsmodels examples python robust models 1 py at main github

There was an error while loading. Please reload this page. This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page SARIMAX: Frequently Asked Questions (FAQ)

State space modeling: Local Linear Trends Fixed / constrained parameters in state space models State space modeling: Local Linear Trends State space models: concentrating out the scale You’re running a regression on your sales data, and a few extreme values are throwing off your predictions. Maybe it’s a single huge order, or data entry errors, or legitimate edge cases you can’t just delete.

Standard linear regression treats every point equally, which means those outliers pull your coefficients in the wrong direction. Robust Linear Models in statsmodels give you a better option. Ordinary least squares regression gives outliers disproportionate influence because errors are squared. An outlier with twice the typical error contributes four times as much to the loss function. Robust Linear Models use iteratively reweighted least squares with M-estimators that downweight outliers instead of amplifying their impact. Think of it this way: OLS assumes all your data points are equally trustworthy.

RLM asks “how much should I trust each observation?” and adjusts accordingly. Points that look like outliers get lower weights, so they influence the final model less. The math behind this involves M-estimators, which minimize a function of residuals that grows more slowly than squared errors. Peter Huber introduced M-estimation for regression in 1964, and it remains the foundation for most robust regression methods today. Here’s a simple example using statsmodels: statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

The documentation for the latest release is at The documentation for the development version is at Recent improvements are highlighted in the release notes https://www.statsmodels.org/stable/release/ This wiki page assembles a collection "official" and user-contributed examples, tutorials and recipes for statsmodels. A set of notebook examples are provided as part of the official Statsmodels documentation.

If you have an interesting example, or if you can write a quick tutorial describing one of statsmodels' features, please consider posting it here. We would be delighted! Feel free to post your example file in any of the common formats (e.g. .py, .rst, .html) and to use any hosting service you like. One very slick, free, and convenient alternative is to: www.dropbox.com/scl/fo/mylhfjbpl2zlc5z5m4prq/h?dl=0&rlkey=li52chs6rcl6lejspde6n0oqf

State space modeling: Local Linear Trends © 2009–2012 Statsmodels Developers© 2006–2008 Scipy Developers© 2006 Jonathan E. TaylorLicensed under the 3-clause BSD License. http://www.statsmodels.org/stable/examples/index.html This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository.

We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page State space modeling: Local Linear Trends Fixed / constrained parameters in state space models TVP-VAR, MCMC, and sparse simulation smoothing There was an error while loading. Please reload this page.

In the world of data analysis and statistical modeling, Linear Regression (specifically Ordinary Least Squares or OLS) is a fundamental tool. It’s widely used for understanding relationships between variables and making predictions. However, OLS has a significant vulnerability: it’s highly sensitive to outliers. Outliers—data points that deviate significantly from other observations—can disproportionately influence OLS regression results, leading to biased coefficients and misleading conclusions. This is where Robust Linear Models (RLM) come into play, offering a more resilient approach. In this post, we’ll explore how to leverage Python’s powerful Statsmodels library to perform robust regression, ensuring your models are less susceptible to anomalous data.

OLS works by minimizing the sum of the squared residuals (the differences between observed and predicted values). Squaring these differences means that large errors, often caused by outliers, have a much greater impact on the model’s parameters than smaller errors. An outlier can pull the regression line towards itself, distorting the slope and intercept, and misrepresenting the true underlying relationship in the majority of the data. Robust regression methods aim to fit a model that is less affected by outliers. Instead of strictly minimizing the sum of squared residuals, they often employ different objective functions that downweight or even ignore the influence of extreme observations. This results in parameter estimates that are more representative of the bulk of the data, providing a more reliable understanding of the relationships between variables.

Statsmodels is a fantastic Python library that provides classes and functions for estimating many different statistical models, as well as for conducting statistical tests and statistical data exploration. It’s built on top of NumPy and SciPy, integrating seamlessly into your data science workflow. For robust linear models, Statsmodels offers the RLM class, which implements various M-estimators.

Statsmodels Examples Python Robust Models 1 Py At Main Github

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