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  2. Seemingly unrelated regressions - Wikipedia

    en.wikipedia.org/wiki/Seemingly_unrelated...

    In Stata, SUR can be estimated using the sureg and suest commands. [15] [16] [17] In Limdep, SUR can be estimated using the sure command [18] In Python, SUR can be estimated using the command SUR in the “linearmodels” package. [19] In gretl, SUR can be estimated using the system command.

  3. Hosmer–Lemeshow test - Wikipedia

    en.wikipedia.org/wiki/Hosmer–Lemeshow_test

    The Hosmer–Lemeshow test is a statistical test for goodness of fit and calibration for logistic regression models. It is used frequently in risk prediction models. The test assesses whether or not the observed event rates match expected event rates in subgroups of the model population.

  4. Bayesian vector autoregression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_vector_autoregression

    This type model can be estimated with Eviews, Stata, Python [8] or R [9] Statistical Packages. Recent research has shown that Bayesian vector autoregression is an appropriate tool for modelling large data sets. [10]

  5. Multiple correspondence analysis - Wikipedia

    en.wikipedia.org/wiki/Multiple_correspondence...

    This is the aim of multiple factor analysis which balances the different issues (i.e. the different groups of variables) within a global analysis and provides, beyond the classical results of factorial analysis (mainly graphics of individuals and of categories), several results (indicators and graphics) specific of the group structure.

  6. Youden's J statistic - Wikipedia

    en.wikipedia.org/wiki/Youden's_J_statistic

    Youden's index is often used in conjunction with receiver operating characteristic (ROC) analysis. [4] The index is defined for all points of an ROC curve, and the maximum value of the index may be used as a criterion for selecting the optimum cut-off point when a diagnostic test gives a numeric rather than a dichotomous result.

  7. ArviZ - Wikipedia

    en.wikipedia.org/wiki/ArviZ

    ArviZ (/ ˈ ɑː r v ɪ z / AR-vees) is a Python package for exploratory analysis of Bayesian models. [2] [3] [4] [5] It is specifically designed to work with the ...

  8. Error correction model - Wikipedia

    en.wikipedia.org/wiki/Error_correction_model

    The choice of dependent variable in the first stage influences test results, i.e. we need weak exogeneity for as determined by Granger causality One can potentially have a small sample bias The cointegration test on α {\displaystyle \alpha } does not follow a standard distribution

  9. Durbin–Watson statistic - Wikipedia

    en.wikipedia.org/wiki/Durbin–Watson_statistic

    Minitab: the option to report the statistic in the Session window can be found under the "Options" box under Regression and via the "Results" box under General Regression. Python: a durbin_watson function is included in the statsmodels package (statsmodels.stats.stattools.durbin_watson), but statistical tables for critical values are not ...