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  2. Gower's distance - Wikipedia

    en.wikipedia.org/wiki/Gower's_distance

    In statistics, Gower's distance between two mixed-type objects is a similarity measure that can handle different types of data within the same dataset and is particularly useful in cluster analysis or other multivariate statistical techniques. Data can be binary, ordinal, or continuous variables.

  3. Mode (statistics) - Wikipedia

    en.wikipedia.org/wiki/Mode_(statistics)

    In statistics, the mode is the value that appears most often in a set of data values. [1] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x=argmax x i P(X = x i)). In other words, it is the value that is most likely to be sampled.

  4. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    Probability density function (pdf) or probability density: function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample.

  5. Isotonic regression - Wikipedia

    en.wikipedia.org/wiki/Isotonic_regression

    A benefit of isotonic regression is that it is not constrained by any functional form, such as the linearity imposed by linear regression, as long as the function is monotonic increasing. Another application is nonmetric multidimensional scaling , [ 1 ] where a low-dimensional embedding for data points is sought such that order of distances ...

  6. Maximum likelihood estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_likelihood_estimation

    In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data.This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable.

  7. Segmented regression - Wikipedia

    en.wikipedia.org/wiki/Segmented_regression

    Yr = A 1.x + K 1 for x < BP (breakpoint) Yr = A 2.x + K 2 for x > BP (breakpoint) where: Yr is the expected (predicted) value of y for a certain value of x; A 1 and A 2 are regression coefficients (indicating the slope of the line segments); K 1 and K 2 are regression constants (indicating the intercept at the y-axis).

  8. Quantile function - Wikipedia

    en.wikipedia.org/wiki/Quantile_function

    The derivative of the quantile function, namely the quantile density function, is yet another way of prescribing a probability distribution. It is the reciprocal of the pdf composed with the quantile function. Consider a statistical application where a user needs to know key percentage points of a given distribution.

  9. Probability mass function - Wikipedia

    en.wikipedia.org/wiki/Probability_mass_function

    The graph of a probability mass function. All the values of this function must be non-negative and sum up to 1. In probability and statistics, a probability mass function (sometimes called probability function or frequency function [1]) is a function that gives the probability that a discrete random variable is exactly equal to some value. [2]