StatsModels Python Module

A StatsModels Python Module is a statistical data analysis system that is a Python module.

• Context:
  • It can include:
    • Linear Regression Modeling Systems.
    • Generalized Linear Modeling Systems.
    • Discrete Choice Modeling Systems.
    • Robust Linear Modeling Systems.
    • Time Series Analysis Systems, such as statsmodels.tsa.
    • Nonparametric Estimation Systems.
• Example(s):
  • StatsModels v0.9.0 (~2018-08-14) [1].
  • StatsModels v0.6.1 (~2014-12-02).
  • StatsModels v0.5.0 (~2013-08-04).
  • …
• Counter-Example(s):
  • scikit-learn.
• See: numpy, matplotlib.

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References

2018

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Statsmodels Retrieved:2018-6-11.
  • Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. It complements SciPy's stats module. Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries.

2018b

• https://github.com/statsmodels/statsmodels
  • QUOTE: Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

2016

• https://github.com/statsmodels/statsmodels/
  • Main Features
    • Linear regression models:
      • Ordinary least squares.
      • Generalized least squares.
      • Weighted least squares.
      • Least squares with autoregressive errors.
      • Quantile regression.
    • Mixed Linear Model with mixed effects and variance components
    • GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
    • GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
    • Discrete models:
      • Logit and Probit
      • Multinomial logit (MNLogit).
      • Poisson regression.
      • Negative Binomial regression.
    • RLM: Robust linear models with support for several M-estimators.
    • Time Series Analysis: models for time series analysis
      • Complete StateSpace modeling framework
      • Seasonal ARIMA and ARIMAX models.
      • VARMA and VARMAX models.
      • Dynamic Factor models.
      • Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
      • Univariate time series analysis: AR, ARIMA.
      • Vector autoregressive models, VAR and structural VAR
      • Hypothesis tests for time series: unit root, cointegration and others
      • Descriptive statistics and process models for time series analysis
    • Survival analysis:
      • Proportional hazards regression (Cox models)
      • Survivor function estimation (Kaplan-Meier)
      • Cumulative incidence function estimation.
    • Nonparametric statistics: (Univariate) kernel density estimators.
    • Datasets: Datasets used for examples and in testing
    • Statistics: a wide range of statistical tests
      • diagnostics and specification tests
      • goodness-of-fit and normality tests.
      • functions for multiple testing
      • various additional statistical tests
    • Imputation with MICE and regression on order statistic
    • Mediation analysis
    • Principal Component Analysis with missing data
    • I/O
      • Tools for reading Stata .dta files into numpy arrays.
      • Table output to ascii, latex, and html
    • Miscellaneous models
    • Sandbox: statsmodels contains a sandbox folder with code in various stages of developement and testing which is not considered "production ready". This covers among others
      • Generalized method of moments (GMM) estimators
      • Kernel regression
      • Various extensions to scipy.stats.distributions
      • Panel data models
      • Information theoretic measures

2015

• http://statsmodels.sourceforge.net/
  • QUOTE: Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. Researchers across fields may find that statsmodels fully meets their needs for statistical computing and data analysis in Python. Features include:
    • Linear regression models.
    • Generalized linear models.
    • Discrete choice models.
    • Robust linear models.
    • Many models and functions for time series analysis
    • Nonparametric estimators.
    • A collection of datasets for examples
    • A wide range of ..statistical test]]s.
    • Input-output tools for producing tables in a number of formats (Text, LaTex, HTML) and for reading Stata files into NumPy and Pandas.
    • Plotting functions.
    • Extensive unit tests to ensure correctness of results
  • Statsmodels runs on Python 2.6 through 3.4.

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