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In statistics, the **method of moments** is a method of estimation of population parameters.

It starts by expressing the population moments (i.e., the expected values of powers of the random variable under consideration) as functions of the parameters of interest. Those expressions are then set equal to the sample moments. The number of such equations is the same as the number of parameters to be estimated. Those equations are then solved for the parameters of interest. The solutions are estimates of those parameters.

The method of moments was introduced by Pafnuty Chebyshev in 1887 in the proof of the central limit theorem. The idea of matching empirical moments of a distribution to the population moments dates back at least to Pearson.^{[ citation needed ]}

Suppose that the problem is to estimate unknown parameters characterizing the distribution of the random variable .^{ [1] } Suppose the first moments of the true distribution (the "population moments") can be expressed as functions of the s:

Suppose a sample of size is drawn, resulting in the values . For , let

be the *j*-th sample moment, an estimate of . The method of moments estimator for denoted by is defined as the solution (if there is one) to the equations:^{[ citation needed ]}

The method of moments is fairly simple and yields consistent estimators (under very weak assumptions), though these estimators are often biased.

It is an alternative to the method of maximum likelihood.

However, in some cases the likelihood equations may be intractable without computers, whereas the method-of-moments estimators can be computed much more quickly and easily. Due to easy computability, method-of-moments estimates may be used as the first approximation to the solutions of the likelihood equations, and successive improved approximations may then be found by the Newton–Raphson method. In this way the method of moments can assist in finding maximum likelihood estimates.

In some cases, infrequent with large samples but not so infrequent with small samples, the estimates given by the method of moments are outside of the parameter space (as shown in the example below); it does not make sense to rely on them then. That problem never arises in the method of maximum likelihood ^{[ citation needed ]}. Also, estimates by the method of moments are not necessarily sufficient statistics, i.e., they sometimes fail to take into account all relevant information in the sample.

When estimating other structural parameters (e.g., parameters of a utility function, instead of parameters of a known probability distribution), appropriate probability distributions may not be known, and moment-based estimates may be preferred to maximum likelihood estimation.

An example application of the method of moments is to estimate polynomial probability density distributions. In this case, an approximate polynomial of order is defined on an interval . The method of moments then yields a system of equations, whose solution involves the inversion of a Hankel matrix.^{ [2] }

Consider the uniform distribution on the interval , . If then we have

Solving these equations gives

Given a set of samples we can use the sample moments and in these formulae in order to estimate and .

Note, however, that this method can produce inconsistent results in some cases. For example, the set of samples results in the estimate even though and so it is impossible for the set to have been drawn from in this case.

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In statistics, **maximum spacing estimation**, or **maximum product of spacing estimation (MPS)**, is a method for estimating the parameters of a univariate statistical model. The method requires maximization of the geometric mean of *spacings* in the data, which are the differences between the values of the cumulative distribution function at neighbouring data points.

In probability theory and directional statistics, a **wrapped Cauchy distribution** is a wrapped probability distribution that results from the "wrapping" of the Cauchy distribution around the unit circle. The Cauchy distribution is sometimes known as a Lorentzian distribution, and the wrapped Cauchy distribution may sometimes be referred to as a wrapped Lorentzian distribution.

In probability theory and statistics, the **Hermite distribution**, named after Charles Hermite, is a discrete probability distribution used to model *count data* with more than one parameter. This distribution is flexible in terms of its ability to allow a moderate over-dispersion in the data.

In statistics, the **variance function** is a smooth function which depicts the variance of a random quantity as a function of its mean. The variance function is a measure of heteroscedasticity and plays a large role in many settings of statistical modelling. It is a main ingredient in the generalized linear model framework and a tool used in non-parametric regression, semiparametric regression and functional data analysis. In parametric modeling, variance functions take on a parametric form and explicitly describe the relationship between the variance and the mean of a random quantity. In a non-parametric setting, the variance function is assumed to be a smooth function.

- ↑ Kimiko O. Bowman and L. R. Shenton, "Estimator: Method of Moments", pp 2092–2098,
*Encyclopedia of statistical sciences*, Wiley (1998). - ↑ J. Munkhammar, L. Mattsson, J. Rydén (2017) "Polynomial probability distribution estimation using the method of moments". PLoS ONE 12(4): e0174573. https://doi.org/10.1371/journal.pone.0174573

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