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  2. Minimum mean square error - Wikipedia

    en.wikipedia.org/wiki/Minimum_mean_square_error

    The final values are + = + and ^ + = ^ +. Examples. Example 1. We shall take a linear ... Notice, that the form of the estimator will remain unchanged, ...

  3. Hat notation - Wikipedia

    en.wikipedia.org/wiki/Hat_notation

    In statistics, a circumflex (ˆ), called a "hat", is used to denote an estimator or an estimated value. [1] For example, in the context of errors and residuals , the "hat" over the letter ε ^ {\displaystyle {\hat {\varepsilon }}} indicates an observable estimate (the residuals) of an unobservable quantity called ε {\displaystyle \varepsilon ...

  4. MM2 - Wikipedia

    en.wikipedia.org/wiki/MM2

    MM2 may refer to: MM2, a class of force fields; see force field (chemistry) MM2 (MMS), an interface utilized by the Multimedia Messaging Service standard; Mega Man 2, a 1988 video game for the NES; Mega Man II, a 1991 video game for the Game Boy; Midtown Madness 2, a 2000 video game for the PC; Motocross Madness 2, a 2000 video game for the PC

  5. Jackknife resampling - Wikipedia

    en.wikipedia.org/wiki/Jackknife_resampling

    Given a sample of size , a jackknife estimator can be built by aggregating the parameter estimates from each subsample of size () obtained by omitting one observation. [1] The jackknife technique was developed by Maurice Quenouille (1924–1973) from 1949 and refined in 1956.

  6. Minimax estimator - Wikipedia

    en.wikipedia.org/wiki/Minimax_estimator

    The risk is constant, but the ML estimator is actually not a Bayes estimator, so the Corollary of Theorem 1 does not apply. However, the ML estimator is the limit of the Bayes estimators with respect to the prior sequence π n ∼ N ( 0 , n σ 2 ) {\displaystyle \pi _{n}\sim N(0,n\sigma ^{2})\,\!} , and, hence, indeed minimax according to ...

  7. Estimation theory - Wikipedia

    en.wikipedia.org/wiki/Estimation_theory

    Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data.

  8. Expected value - Wikipedia

    en.wikipedia.org/wiki/Expected_value

    To empirically estimate the expected value of a random variable, one repeatedly measures observations of the variable and computes the arithmetic mean of the results. If the expected value exists, this procedure estimates the true expected value in an unbiased manner and has the property of minimizing the sum of the squares of the residuals ...

  9. Chvorinov's rule - Wikipedia

    en.wikipedia.org/wiki/Chvorinov's_rule

    The S.I. unit for the mold constant B is seconds per metre squared (s/m 2). [4] According to Askeland, the constant n is usually 2, however Degarmo claims it is between 1.5 and 2.