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In statistics , the method of moments is a method of estimation of population parameters .
Suppose that the problem is to estimate
k
{\displaystyle k}
unknown parameters
θ
1
,
θ
2
,
…
,
θ
k
{\displaystyle \theta _{1},\theta _{2},\dots ,\theta _{k}}
describing the distribution
f
W
(
w
;
θ
)
{\displaystyle f_{W}(w;\theta )}
of the random variable
W
{\displaystyle W}
.[ 1] Suppose the first
k
{\displaystyle k}
moments of the true distribution (the "population moments") can be expressed as functions of the
θ
{\displaystyle \theta }
s:
μ
1
≡
E
[
W
]
=
g
1
(
θ
1
,
θ
2
,
…
,
θ
k
)
,
μ
2
≡
E
[
W
2
]
=
g
2
(
θ
1
,
θ
2
,
…
,
θ
k
)
,
⋮
μ
k
≡
E
[
W
k
]
=
g
k
(
θ
1
,
θ
2
,
…
,
θ
k
)
.
{\displaystyle {\begin{aligned}\mu _{1}&\equiv \operatorname {E} [W]=g_{1}(\theta _{1},\theta _{2},\ldots ,\theta _{k}),\\[4pt]\mu _{2}&\equiv \operatorname {E} [W^{2}]=g_{2}(\theta _{1},\theta _{2},\ldots ,\theta _{k}),\\&\,\,\,\vdots \\\mu _{k}&\equiv \operatorname {E} [W^{k}]=g_{k}(\theta _{1},\theta _{2},\ldots ,\theta _{k}).\end{aligned}}}
Suppose a sample of size
n
{\displaystyle n}
is drawn, and it leads to the values
w
1
,
…
,
w
n
{\displaystyle w_{1},\dots ,w_{n}}
. For
j
=
1
,
…
,
k
{\displaystyle j=1,\dots ,k}
, let
μ
^
j
=
1
n
∑
i
=
1
n
w
i
j
{\displaystyle {\widehat {\mu }}_{j}={\frac {1}{n}}\sum _{i=1}^{n}w_{i}^{j}}
be the j -th sample moment, an estimate of
μ
j
{\displaystyle \mu _{j}}
. The method of moments estimator for
θ
1
,
θ
2
,
…
,
θ
k
{\displaystyle \theta _{1},\theta _{2},\ldots ,\theta _{k}}
denoted by
θ
^
1
,
θ
^
2
,
…
,
θ
^
k
{\displaystyle {\widehat {\theta }}_{1},{\widehat {\theta }}_{2},\dots ,{\widehat {\theta }}_{k}}
is defined as the solution (if there is one) to the equations:[source? ]
μ
^
1
=
g
1
(
θ
^
1
,
θ
^
2
,
…
,
θ
^
k
)
,
μ
^
2
=
g
2
(
θ
^
1
,
θ
^
2
,
…
,
θ
^
k
)
,
⋮
μ
^
k
=
g
k
(
θ
^
1
,
θ
^
2
,
…
,
θ
^
k
)
.
{\displaystyle {\begin{aligned}{\widehat {\mu }}_{1}&=g_{1}({\widehat {\theta }}_{1},{\widehat {\theta }}_{2},\ldots ,{\widehat {\theta }}_{k}),\\[4pt]{\widehat {\mu }}_{2}&=g_{2}({\widehat {\theta }}_{1},{\widehat {\theta }}_{2},\ldots ,{\widehat {\theta }}_{k}),\\&\,\,\,\vdots \\{\widehat {\mu }}_{k}&=g_{k}({\widehat {\theta }}_{1},{\widehat {\theta }}_{2},\ldots ,{\widehat {\theta }}_{k}).\end{aligned}}}
The method of moments is simple and gets consistent estimators (under very weak assumptions). However, these estimators are often biased .
↑ K. O. Bowman and L. R. Shenton, "Estimator: Method of Moments", pp 2092–2098, Encyclopedia of statistical sciences , Wiley (1998).