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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. 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.

  4. Inverse probability - Wikipedia

    en.wikipedia.org/wiki/Inverse_probability

    The inverse probability problem (in the 18th and 19th centuries) was the problem of estimating a parameter from experimental data in the experimental sciences, especially astronomy and biology. A simple example would be the problem of estimating the position of a star in the sky (at a certain time on a certain date) for purposes of navigation ...

  5. Propensity score matching - Wikipedia

    en.wikipedia.org/wiki/Propensity_score_matching

    Propensity scores are used to reduce confounding by equating groups based on these covariates. Suppose that we have a binary treatment indicator Z, a response variable r, and background observed covariates X. The propensity score is defined as the conditional probability of treatment given background variables:

  6. List of statistics articles - Wikipedia

    en.wikipedia.org/wiki/List_of_statistics_articles

    Explained sum of squares; ... Inverse distance weighting; Inverse distribution; ... Propensity score matching; Proper linear model;

  7. 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 ().

  8. How To Turn Empathy Into Compassion For Better Relationships ...

    www.aol.com/turn-empathy-compassion-better...

    Compassion and empathy sound like synonyms, but they're two different skill sets. Here's how and why to hone both qualities, according to psychologists.

  9. Propensity probability - Wikipedia

    en.wikipedia.org/wiki/Propensity_probability

    The propensity theory of probability is a probability interpretation in which the probability is thought of as a physical propensity, disposition, or tendency of a given type of situation to yield an outcome of a certain kind, or to yield a long-run relative frequency of such an outcome.