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This article mainly introduces the Python mathematical modeling library StatsModels statistical regression example analysis, the article is very detailed, has a certain reference value, interested friends must read it!

1. About StatsModels

Statsmodels (http://www.statsmodels.org) is a Python library for fitting a variety of statistical models, performing statistical tests, and data exploration and visualization.

2. Documentation

The latest version of the document is located at:

Https://www.statsmodels.org/stable/

3. Main functions

1. Linear regression model:

Ordinary least square method

Generalized least square method

Weighted least square method

Least square method with autoregressive error

Quantile regression

Recursive least square method

two。 Mixed linear model with mixed effect and variance component

3.glm: a Generalized Linear Model exponential Family Distribution that supports all one Parameter

4. Bayesian mixed glm of binomial and poisson

5.gee: generalized estimation equation for unidirectional clustering or Longitudinal data

6. Discrete model:

Logit and probit

Polynomial logit (mnlogit)

Poisson and Generalized Poisson regression

Negative binomial regression

Zero expansion counting model

7. Rlm: a robust linear model that supports multiple m estimates.

8. Time series analysis: time series analysis model

A complete state space modeling framework

Seasonal arima and arimax models

Varma and Varmax model

Dynamic factor model

Unobserved component model

Markov switching model (MSAR), also known as hidden Markov model (HMM)

Univariate time series analysis: ar,arima

Vector autoregressive model, var and structural var

Vector error correction model, vecm

Exponential smoothing, Holt Winters

Hypothesis testing of time series: unit root, cointegration, etc.

Descriptive Statistics and process Model of time Series Analysis

9. Survival analysis:

Proportional hazard regression (cox model)

Survivor function estimation (kaplan-meier)

Cumulative correlation function estimation

10. Multivariable:

Principal component Analysis of missing data

Rotation factor analysis

Manova

Canonical correlation

11. Nonparametric statistics: univariate and multivariate kernel density estimation

twelve。 Datasets: datasets for examples and tests

13. Statistics: extensive statistical testing

Diagnosis and specification testing

Goodness-of-fit and normality test

Multiple test function

Various additional statistical tests

14. Mouse interpolation, Sequential Statistical regression and Gaussian interpolation

15. Intermediary analysis

16. Graphics include drawing functions for visualizing analysis data and model results

17. Input / output

A tool for reading stata.dta files, but pandas has a newer version

Table output to ascii, latex, and html

18. Sandboxing: statsmodels contains a sandboxed folder containing development and testing that are not considered "production ready".

Generalized method of moment (gmm) estimator

Nuclear regression

Various extensions of scipy.stats.distributions

Panel data model

Information theory measure

4. Obtain and install

Pip3 install-- upgrade statsmodel-I https://pypi.tsinghua.edu.cn/simple

The above is all the contents of the article "sample Analysis of StatsModels Statistical regression of Mathematical Modeling Library in Python". Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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