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The logrank test, or log-rank test, is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate to use when the data are right skewed and censored (technically, the censoring must be non-informative).
Bowker's test of symmetry; Categorical distribution, general model; Chi-squared test; Cochran–Armitage test for trend; Cochran–Mantel–Haenszel statistics; Correspondence analysis; Cronbach's alpha; Diagnostic odds ratio; G-test; Generalized estimating equations; Generalized linear models; Krichevsky–Trofimov estimator; Kuder ...
Exponential/Log, Power Series, Sigmoidal, Hyperbolic, Yield Density, Linear, Polynomial, Dose Response, Pharmacology, Equilibrium and Inhibition. Levenberg-Marquardt fitting algorithm. A range of statistical calculations can also be applied to the data from within the spreadsheet. Example statistics include: [further explanation needed] F-test ...
The corresponding log partial likelihood is =: = (:), where we have written = using the indexing introduced above in a more general way, as :. Crucially, the effect of the covariates can be estimated without the need to specify the hazard function λ 0 ( t ) {\displaystyle \lambda _{0}(t)} over time.
In statistics, ranking is the data transformation in which numerical or ordinal values are replaced by their rank when the data are sorted.. For example, if the numerical data 3.4, 5.1, 2.6, 7.3 are observed, the ranks of these data items would be 2, 3, 1 and 4 respectively.
In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations where the fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known.
Together with rank statistics, order statistics are among the most fundamental tools in non-parametric statistics and inference. Important special cases of the order statistics are the minimum and maximum value of a sample, and (with some qualifications discussed below) the sample median and other sample quantiles.
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