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  2. Point estimation - Wikipedia

    en.wikipedia.org/wiki/Point_estimation

    In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

  3. Three-point estimation - Wikipedia

    en.wikipedia.org/wiki/Three-point_estimation

    These values are used to calculate an E value for the estimate and a standard deviation (SD) as L-estimators, where: E = (a + 4m + b) / 6 SD = (b − a) / 6. E is a weighted average which takes into account both the most optimistic and most pessimistic estimates provided. SD measures the variability or uncertainty in the estimate.

  4. Estimation statistics - Wikipedia

    en.wikipedia.org/wiki/Estimation_statistics

    Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. [1]

  5. Estimating equations - Wikipedia

    en.wikipedia.org/wiki/Estimating_equations

    In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated.This can be thought of as a generalisation of many classical methods—the method of moments, least squares, and maximum likelihood—as well as some recent methods like M-estimators.

  6. Empirical Bayes method - Wikipedia

    en.wikipedia.org/wiki/Empirical_Bayes_method

    The resulting point estimate ⁡ is therefore like a weighted average of the sample mean ¯ and the prior mean =. This turns out to be a general feature of empirical Bayes; the point estimates for the prior (i.e. mean) will look like a weighted averages of the sample estimate and the prior estimate (likewise for estimates of the variance).

  7. Estimator - Wikipedia

    en.wikipedia.org/wiki/Estimator

    In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished. [1] For example, the sample mean is a commonly used estimator of the population mean. There are point and interval ...

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  9. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    Download QR code; Print/export ... the argument that maximizes the likelihood function serves as a point estimate for the ... is the sample mean of the observations. ...