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  2. Inverse probability weighting - Wikipedia

    en.wikipedia.org/wiki/Inverse_probability_weighting

    Inverse probability weighting is a statistical technique for estimating quantities related to a population other than the one from which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. [ 1 ]

  3. 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.

  4. 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 a target population.

  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.

  7. Marginal structural model - Wikipedia

    en.wikipedia.org/wiki/Marginal_structural_model

    Marginal structural models are a class of statistical models used for causal inference in epidemiology. [1] [2] Such models handle the issue of time-dependent confounding in evaluation of the efficacy of interventions by inverse probability weighting for receipt of treatment, they allow us to estimate the average causal effects.

  8. 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 .

  9. Divergence-from-randomness model - Wikipedia

    en.wikipedia.org/wiki/Divergence-from-randomness...

    The columns to the right show the observed and Poisson probabilities. P obs,elite(Kt) is the observed probability over all documents. P Poisson, all, lambda(Kt) is the Poisson probability, where lambda(t,c)=nL(t,c)/N D(c)=0.20 is the Poisson parameter. The table illustrates how the observed probability is different from the Poisson probability.