Epidemiology Analysis With GAUSS

Main Applications of GAUSS in Epidemiology

GAUSS offers a complete set of tools for data organization, analysis, and presentation. Survey data, cross-sectional data, and large panel data can easily be easily managed using a number of conditional data selection and sorting tools.

Whatever your area of research, GAUSS supports all your data analysis needs, large or small.

Field Example applications
Health Data Analysis
  • Event count models
  • Commuting choice models
  • Activity based models
  • Demand management and forecasting
  • Mode choice models
  • Free flow speed estimation
Public Health Modeling
  • Linear and nonlinear regression models
  • Classification
  • Clustering
  • Static and dynamic systems
Clinical Trials & Methods
  • Hierarchical models
  • Bayesian analysis
  • Decision analysis
  • Statistical prediction
  • Power testing

Time Series, Regression Models and Other Main Functions of GAUSS for Epidemiology

GAUSS is equipped to efficiently conduct statistical inference, correlation and regression analysis as well as categorical, multivariate, time-series and survival analysis.

Data cleaning, processing, and management

General Regression Analysis

Pre-built GAUSS functions can be used to efficiently and intuitively implement fundamental regression models including:

Epidemiology Time Series Analysis

With GAUSS time series analysis is made easy and efficient whether you're just getting started or developing new cutting edge methods. GAUSS time series capabilities include:

Discrete Choice Analysis

GAUSS provides a full suite of tools for analyzing qualitative choice models. GAUSS's discrete choice tools cover everything from binary and multinomial models to logistic regression.

  • Multinomial logit models
  • Logistic regression modeling
    • L2 and L1 regularized classifiers
    • L2 and L1-loss linear support vector machines (SVM)
  • Model selection and assessment tools
    • Full model and restricted model log-likelihoods
    • Chi-square statistics
    • Agresti’s G-squared statistic
    • McFadden’s pseudo-R-squared statistic
    • Madella’s pseudo-R-squared statistic
    • Akaike information criterion (AIC)
    • Bayesian information criterion (BIC)
    • Likelihood ratio statistics and accompanying probability values
    • Cragg and Uhler’s normed likelihood ratios
    • Count and adjusted count R-squared

Panel Data Analysis

  • Data aggregation and within-group statistics
  • Panel data unit root tests
    • Breitung and Das panel unit root test
    • Im, Pesaran, and Shin (IPS) panel unit root test
    • Levin-Lin-Chu (LLC) panel unit root test
    • Pesaran unit root test in the presence of cross-section dependence
    • Modified CADF and CIPS panel unit root tests
    • Bai and Ng PANIC panel unit root test
    • Harris and Tzavalis panel unit root test
    • Hadri panel data unit root tests
    • Panel unit root tests with structural breaks
    • Im, Lee, & Tieslau panel LM unit root test with level shifts
    • Lee and Tieslau panel LM unit root test with level and trend shifts
    • Nazlioglu & Karul panel stationarity test with gradual structural shifts
  • One-way individual effects
    • One-way fixed effects
    • One-way random effects
    • Least squares pooled ols
    • Least squares dummy variable (LSDV)
  • Cross dependence tests
    • Pesaran test for cross-dependence
    • Friedman test for cross-dependence
    • Frees test for cross-dependence
  • Causality tests
    • Granger causality
    • Toda & Yamamoto causality test
    • Single Fourier-frequency Granger causality test
    • Single Fourier-frequency Toda & Yamamoto causality test
    • Cumulative Fourier-frequency Granger causality test
    • Cumulate Fourier-frequency Today & Yamamoto test
    • Fischer testing for Granger causality in heterogeneous mixed panels
    • Zhnc and Zn test statistics for Granger non-causality in heterogeneous panels
    • Panel SUR Wald statistics
  • Model diagnostics and assessment tests
    • Hausman test for specification
    • Lagrange multiplier test for error components model

GAUSS Applications Designed for Epidemiology

Application Description

Time series MT

Includes comprehensive tools for time series data analysis including
  • MLE and state-space estimation
  • Unit root and cointegration testing
  • Model diagnostics and forecasting
  • Nonlinear time series models

Linear regression MT

Provides procedures for estimating single equations or systems of equations including:
  • Two-stage least squares.
  • Three-stage least squares.
  • Seemingly unrelated regression.

Discrete Choice

Provides an adaptable, efficient, and user-friendly environment for linear data classification including
  • Binary and count models.
  • Multinomial logit models.
  • Logistic regression.
  • Tools for model selection and assessment.

Descriptive Statistics MT

Provides basic statistics for the variables in GAUSS datasets. These statistics describe and test univariate and multivariate features of the data and provide information for further analysis.

Bayesian Estimation Tools

Provides a suite of pre-built tools for Bayesian estimation and analysis including:
  • Data generation.
  • Markov-chain Monte Carlo estimation (MCMC).
  • Full post-estimation graphing and reporting.
  • Maximum likelihood estimation (MLE) initialization.

Industries that use GAUSS Data Analysis Tools

Data analysts across a wide range of industries use GAUSS. GAUSS is found in

  • Universities
  • Government agencies
  • Non-governmental organizations
  • Nonprofit research organizations
  • Corporations

Icons of some organizations where GAUSS is used.

Benefits of GAUSS for Epidemiologists

GAUSS provides a fast and flexible environment for epidemiology. Whether you are performing ordinary least squares regressions or survey analysis, GAUSS provides tangible advantages including:

  • Over 1000 pre-built statistical and econometric functions.
  • Light-weight and efficient analytics engine designed to make the most of your hardware and provide optimized computation speed.
  • Intuitive matrix-based programming language for transparent and easy to understand programming.
  • Fully interactive environment for speeding up your workflow from exploring data to analyzing results.
  • Comprehensive documentation and examples.
  • Comprehensive data support including CSV, Excel HDF5, SAS, Stata, text delimited files.
  • Relational database support including MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Oracle, IBM DB2, HBase, Hive and MongoDB.

Compatibility of GAUSS with Other Software

GAUSS is built to seamlessly integrate into any analytics environment:

  • GAUSS is fully compatible with SAS, STATA, HDF5, CSV, and Excel datasets.
  • Efficiently connect powerful analytics to any internal or customer-facing data source, application, or interface with the GAUSS Engine.
  • Full technical support for assistance when migrating from and integrating with other software platforms.

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