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  2. Grubbs's test - Wikipedia

    en.wikipedia.org/wiki/Grubbs's_test

    In statistics, Grubbs's test or the Grubbs test (named after Frank E. Grubbs, who published the test in 1950 [1]), also known as the maximum normalized residual test or extreme studentized deviate test, is a test used to detect outliers in a univariate data set assumed to come from a normally distributed population.

  3. Score test - Wikipedia

    en.wikipedia.org/wiki/Score_test

    In many situations, the score statistic reduces to another commonly used statistic. [11] In linear regression, the Lagrange multiplier test can be expressed as a function of the F-test. [12] When the data follows a normal distribution, the score statistic is the same as the t statistic. [clarification needed]

  4. Scoring algorithm - Wikipedia

    en.wikipedia.org/wiki/Scoring_algorithm

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

  5. Propensity score matching - Wikipedia

    en.wikipedia.org/wiki/Propensity_score_matching

    Any score that is 'finer' than the propensity score is a balancing score (i.e.: () = (()) for some function ). The propensity score is the coarsest balancing score function, as it takes a (possibly) multidimensional object ( X i ) and transforms it into one dimension (although others, obviously, also exist), while b ( X ) = X {\displaystyle b(X ...

  6. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    A hinge function is defined by a variable and a knot, so to add a new basis function, MARS must search over all combinations of the following: 1) existing terms (called parent terms in this context) 2) all variables (to select one for the new basis function) 3) all values of each variable (for the knot of the new hinge function).

  7. Bayesian information criterion - Wikipedia

    en.wikipedia.org/wiki/Bayesian_information_criterion

    ^ = the maximized value of the likelihood function of the model , i.e. ^ = (^,), where {^} are the parameter values that maximize the likelihood function and is the observed data; n {\displaystyle n} = the number of data points in x {\displaystyle x} , the number of observations , or equivalently, the sample size;

  8. Calinski–Harabasz index - Wikipedia

    en.wikipedia.org/wiki/Calinski–Harabasz_index

    Given a data set of n points: {x 1, ..., x n}, and the assignment of these points to k clusters: {C 1, ..., C k}, the Calinski–Harabasz (CH) Index is defined as the ratio of the between-cluster separation (BCSS) to the within-cluster dispersion (WCSS), normalized by their number of degrees of freedom:

  9. List of numerical-analysis software - Wikipedia

    en.wikipedia.org/wiki/List_of_numerical-analysis...

    MATLAB is a widely used proprietary software for performing numerical computations. [1] [2] [3] It comes with its own programming language, in which numerical algorithms can be implemented. GNU MCSim a simulation and numerical integration package, with fast Monte Carlo and Markov chain Monte Carlo capabilities.