17 нояб. 2020 г. · You can use ransac which stands for RANSAC (RANdom SAmple Consensus), that essentially tries to provide a robust estimate of the parameter.

6 февр. 2019 г. · I am having trouble running a robust regression model with Statsmodel in python. The following OLS model works: model_name = sm.ols(formula="depenent ~ var1 * ...

21 окт. 2017 г. · I used python Statsmodels module and they used Stata and we share the same set of data. For Ordinary Least Squares regression, we got the same answers. But ...

22 авг. 2021 г. · I am running a logistic regression with statsmodel and I am trying to add robustness to my model, similar to STATA's robust command, ...

16 сент. 2015 г. · I was testing some basic category regression using Stats model: I build up a deterministic model Y = X + Z where X can takes 3 values (a, b or c) and Z only 2 ...

6 авг. 2017 г. · Weights are internally used to implement the reweighted least squares fitting method. If the weights have the interpretation of variance weights ...

7 мар. 2016 г. · I was wondering if in general there is a way to perform a more robust fit using Python, or even if this can be done using polyfit.

14 февр. 2022 г. · I am trying to predict out of sample data with statsmodels robust linear regression. I am having difficulties doing so with the predict function.

22 окт. 2018 г. · I have data and simply want to fit a robust curve using my model equation: y = a * e^(-b*z) This cookbook is my reference: click Below is my attempt.

20 янв. 2020 г. · I'm trying to run a regression in python where the standard errors are clustered as well as robust to heteroskedascity and autocorrelation (HAC).