What is PoissonOffSetGMLE in the StatsModels library?
This recipe explains what is PoissonOffSetGMLE in the StatsModels library
Recipe Objective - What is PoissonOffSetGMLE in the StatsModels library?
PoissonOffSetGMLE is a Maximum Likelihood Estimation of the Poisson Model. This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset. All methods and results are common, except for the definition of the negative log-likelihood method. Gradients and Hessian matrices, and all the resulting statistics, are based on numerical differentiation.
It belongs to the class statsmodels.miscmodels.count.PoissonOffsetGMLE(endog, exog=None, offset=None, missing='none', **kwds)
Access YOLO OCR Character Recognition Project with Source Code
For more related projects -
https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
https://www.dezyre.com/projects/data-science-projects/neural-network-projects
Table of Contents
Attributes:
endog_names
Names of endogenous variables.
exog_names
Names of exogenous variables.
Methods:
expandparams(params)
expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, ...])
Fit method for likelihood based models
from_formula(formula, data[, subset, drop_cols])
Create a Model from a formula and dataframe.
hessian(params)
Hessian of log-likelihood evaluated at params
information(params)
Fisher information matrix of model.
predict(params[, exog])
After a model has been fit predict returns the fitted values.
predict_distribution(exog)
return frozen scipy.stats distribution with mu at estimated prediction
score(params)
Gradient of log-likelihood evaluated at params
What Users are saying..
Savvy Sahai
As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More