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A simple, easy-to-understand, and statistically powerful method of matching known as Coarsened Exact Matching or CEM. [6] Matching has been promoted by Donald Rubin. [4] It was prominently criticized in economics by Robert LaLonde (1986), [7] who compared estimates of treatment effects from an experiment to comparable estimates produced with ...
In fact, it can be shown that the unconditional analysis of matched pair data results in an estimate of the odds ratio which is the square of the correct, conditional one. [ 2 ] In addition to tests based on logistic regression, several other tests existed before conditional logistic regression for matched data as shown in related tests .
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
Radius matching: all matches within a particular radius are used -- and reused between treatment units. Kernel matching: same as radius matching, except control observations are weighted as a function of the distance between the treatment observation's propensity score and control match propensity score. One example is the Epanechnikov kernel ...
Cross-validation, [2] [3] [4] sometimes called rotation estimation [5] [6] [7] or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.
Convergent Cross Mapping (CCM) leverages a corollary to the Generalized Takens Theorem [2] that it should be possible to cross predict or cross map between variables observed from the same system. Suppose that in some dynamical system involving variables X {\displaystyle X} and Y {\displaystyle Y} , X {\displaystyle X} causes Y {\displaystyle Y} .
Repeated measures analysis of variance (rANOVA) is a commonly used statistical approach to repeated measure designs. [3] With such designs, the repeated-measure factor (the qualitative independent variable) is the within-subjects factor, while the dependent quantitative variable on which each participant is measured is the dependent variable.
The participants on one side of the matching (the hospitals) may have a numerical capacity, specifying the number of doctors they are willing to hire. The total number of participants on one side might not equal the total capacity to which they are to be matched on the other side. The resulting matching might not match all of the participants.