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The saddlepoint approximation method, initially proposed by Daniels (1954) [1] is a specific example of the mathematical saddlepoint technique applied to statistics, in particular to the distribution of the sum of independent random variables.
According to this definition, E[X] exists and is finite if and only if E[X +] and E[X −] are both finite. Due to the formula |X| = X + + X −, this is the case if and only if E|X| is finite, and this is equivalent to the absolute convergence conditions in the definitions above. As such, the present considerations do not define finite ...
In probability theory, Rice's formula counts the average number of times an ergodic stationary process X(t) per unit time crosses a fixed level u. [1] Adler and Taylor describe the result as "one of the most important results in the applications of smooth stochastic processes." [2] The formula is often used in engineering. [3]
The sample mean is the average of the values of a variable in a sample, which is the sum of those values divided by the number of values. Using mathematical notation, if a sample of N observations on variable X is taken from the population, the sample mean is:
This can be done using the method of moments, e.g., the sample mean and the sample standard deviation. The sample mean is an estimate of μ 1 ' and the sample standard deviation is an estimate of μ 2 1/2. The following is an efficient method, known as the "Koay inversion technique". [14] for solving the estimating equations, based on the ...
Hence, the above formula can be used to estimate the noise variance in an MRI image from background data. [7] [8] The Rayleigh distribution was also employed in the field of nutrition for linking dietary nutrient levels and human and animal responses. In this way, the parameter σ may be used to calculate nutrient response relationship. [9]
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The Rice rule is often reported with the factor of 2 outside the cube root, () /, and may be considered a different rule. The key difference from Scott's rule is that this rule does not assume the data is normally distributed and the bin width only depends on the number of samples, not on any properties of the data.