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  2. 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]

  3. Horvitz–Thompson estimator - Wikipedia

    en.wikipedia.org/wiki/Horvitz–Thompson_estimator

    In statistics, the Horvitz–Thompson estimator, named after Daniel G. Horvitz and Donovan J. Thompson, [1] is a method for estimating the total [2] and mean of a pseudo-population in a stratified sample by applying inverse probability weighting to account for the difference in the sampling distribution between the collected data and the target population.

  4. Inverse probability - Wikipedia

    en.wikipedia.org/wiki/Inverse_probability

    Given the data, one must estimate the true position (probably by averaging). This problem would now be considered one of inferential statistics. The terms "direct probability" and "inverse probability" were in use until the middle part of the 20th century, when the terms "likelihood function" and "posterior distribution" became prevalent.

  5. Inverse-variance weighting - Wikipedia

    en.wikipedia.org/wiki/Inverse-variance_weighting

    For normally distributed random variables inverse-variance weighted averages can also be derived as the maximum likelihood estimate for the true value. Furthermore, from a Bayesian perspective the posterior distribution for the true value given normally distributed observations and a flat prior is a normal distribution with the inverse-variance weighted average as a mean and variance ().

  6. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    inverse-variance weighting, also known as analytic weights, [24] is when each element is assigned a weight that is the inverse of its (known) variance. [25] [9]: 187 When all elements have the same expectancy, using such weights for calculating weighted averages has the least variance among all weighted averages. In the common formulation ...

  7. Bootstrapping (statistics) - Wikipedia

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

    Monographs on Statistics and Applied Probability. Vol. 57. Boca Raton, US: Chapman & Hall. software. Davison AC, Hinley DV (1997). Bootstrap Methods and their Application. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge: Cambridge University Press. ISBN 9780511802843. software. Mooney CZ, Duval RD (1993).

  8. Propensity score matching - Wikipedia

    en.wikipedia.org/wiki/Propensity_score_matching

    SPSS: A dialog box for Propensity Score Matching is available from the IBM SPSS Statistics menu (Data/Propensity Score Matching), and allows the user to set the match tolerance, randomize case order when drawing samples, prioritize exact matches, sample with or without replacement, set a random seed, and maximize performance by increasing ...

  9. Inverse distribution - Wikipedia

    en.wikipedia.org/wiki/Inverse_distribution

    In probability theory and statistics, an inverse distribution is the distribution of the reciprocal of a random variable. Inverse distributions arise in particular in the Bayesian context of prior distributions and posterior distributions for scale parameters .