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Chebyshev's inequality is more general, stating that a minimum of just 75% of values must lie within two standard deviations of the mean and 88.89% within three standard deviations for a broad range of different probability distributions. [1] [2]
The basis of the method is to have, or to find, a set of simultaneous equations involving both the sample data and the unknown model parameters which are to be solved in order to define the estimates of the parameters. [1] Various components of the equations are defined in terms of the set of observed data on which the estimates are to be based.
Suppose f(x,θ) is some function defined for θ ∈ Θ, and continuous in θ. Then for any fixed θ, the sequence {f(X 1,θ), f(X 2,θ), ...} will be a sequence of independent and identically distributed random variables, such that the sample mean of this sequence converges in probability to E[f(X,θ)]. This is the pointwise (in θ) convergence.
In other words, if X and Y are random variables that take different values with probability zero, then the expectation of X will equal the expectation of Y. If X = c {\displaystyle X=c} (a.s.) for some real number c , then E [ X ] = c . {\displaystyle \operatorname {E} [X]=c.}
A square with sides equal to the difference of each value from the mean is formed for each value. Arranging the squares into a rectangle with one side equal to the number of values, n, results in the other side being the distribution's variance, σ 2.
where s x 2 and s y 2 are the variances of the x and y variates respectively, m x and m y are the means of the x and y variates respectively and s xy is the covariance of x and y. Although the approximate variance estimator of the ratio given below is biased, if the sample size is large, the bias in this estimator is negligible.
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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]