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  2. Point estimation - Wikipedia

    en.wikipedia.org/wiki/Point_estimation

    In this case, it is denoted by L(θ). The principle of maximum likelihood consists of choosing an estimate within the admissible range of θ, that maximizes the likelihood. This estimator is called the maximum likelihood estimate (MLE) of θ. In order to obtain the MLE of θ, we use the equation dlogL(θ)/dθ i =0, i = 1, 2, …, k. If θ is a ...

  3. Basis of estimate - Wikipedia

    en.wikipedia.org/wiki/Basis_of_estimate

    Basis of estimate (BOE) is a tool used in the field of project management by which members of the project team, usually estimators, project managers, or cost analysts, calculate the total cost of the project.

  4. Three-point estimation - Wikipedia

    en.wikipedia.org/wiki/Three-point_estimation

    For example, a triangular distribution might be used, depending on the application. In three-point estimation, three figures are produced initially for every distribution that is required, based on prior experience or best-guesses: a = the best-case estimate; m = the most likely estimate; b = the worst-case estimate

  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. Least squares - Wikipedia

    en.wikipedia.org/wiki/Least_squares

    The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...

  7. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    The above equations are efficient to use if the mean of the x and y variables (¯ ¯) are known. If the means are not known at the time of calculation, it may be more efficient to use the expanded version of the α ^ and β ^ {\displaystyle {\widehat {\alpha }}{\text{ and }}{\widehat {\beta }}} equations.

  8. A priori estimate - Wikipedia

    en.wikipedia.org/wiki/A_priori_estimate

    A priori is Latin for "from before" and refers to the fact that the estimate for the solution is derived before the solution is known to exist. One reason for their importance is that if one can prove an a priori estimate for solutions of a differential equation, then it is often possible to prove that solutions exist using the continuity ...

  9. Maximum spacing estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_spacing_estimation

    This is the case that maximizes the geometric mean of such spacings, so solving for the parameters that maximize the geometric mean would achieve the “best” fit as defined this way. Ranneby (1984) justified the method by demonstrating that it is an estimator of the Kullback–Leibler divergence , similar to maximum likelihood estimation ...