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  2. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    It is known women are under-represented considerably in a random sample of software engineers, which would be important when adjusting for other variables such as years employed and current level of seniority. Suppose only 20% of software engineers are women, i.e., males are 4 times as frequent as females.

  3. Bootstrapping (statistics) - Wikipedia

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

    In particular, the bootstrap is useful when there is no analytical form or an asymptotic theory (e.g., an applicable central limit theorem) to help estimate the distribution of the statistics of interest. This is because bootstrap methods can apply to most random quantities, e.g., the ratio of variance and mean.

  4. Overlap coefficient - Wikipedia

    en.wikipedia.org/wiki/Overlap_coefficient

    The overlap coefficient, [note 1] or Szymkiewicz–Simpson coefficient, [citation needed] [3] [4] [5] is a similarity measure that measures the overlap between two finite sets.It is related to the Jaccard index and is defined as the size of the intersection divided by the size of the smaller of two sets:

  5. Bhattacharyya distance - Wikipedia

    en.wikipedia.org/wiki/Bhattacharyya_distance

    In statistics, the Bhattacharyya distance is a quantity which represents a notion of similarity between two probability distributions. [1] It is closely related to the Bhattacharyya coefficient, which is a measure of the amount of overlap between two statistical samples or populations.

  6. Inverse probability weighting - Wikipedia

    en.wikipedia.org/wiki/Inverse_probability_weighting

    These applications codified the theory of other statistics and estimators such as marginal structural models, the standardized mortality ratio, and the EM algorithm for coarsened or aggregate data. Inverse probability weighting is also used to account for missing data when subjects with missing data cannot be included in the primary analysis. [4]

  7. Importance sampling - Wikipedia

    en.wikipedia.org/wiki/Importance_sampling

    However, the simulation outputs are weighted to correct for the use of the biased distribution, and this ensures that the new importance sampling estimator is unbiased. The weight is given by the likelihood ratio, that is, the Radon–Nikodym derivative of the true underlying distribution with respect to the biased simulation distribution.

  8. Pre- and post-test probability - Wikipedia

    en.wikipedia.org/wiki/Pre-_and_post-test_probability

    For example, the risk of developing breast cancer at an age younger than 40 years in women in the United Kingdom can be estimated at 2%. [9] Also, studies on Ashkenazi Jews has indicated that a mutation in BRCA1 confers a relative risk of 21.6 of developing breast cancer in women under 40 years of age, and a mutation in BRCA2 confers a relative ...

  9. Weighted sum model - Wikipedia

    en.wikipedia.org/wiki/Weighted_Sum_Model

    In decision theory, the weighted sum model (WSM), [1] [2] also called weighted linear combination (WLC) [3] or simple additive weighting (SAW), [4] is the best known and simplest multi-criteria decision analysis (MCDA) / multi-criteria decision making method for evaluating a number of alternatives in terms of a number of decision criteria.