This lecture introduces the notion of moment of a random variable.
Table of contents
The
-th
moment of a random variable is the expected value
of its
-th
power.
Definition
Let
be a random variable. Let
.
If the expected
value
exists
and is finite, then
is said to possess a finite
-th
moment and
is called
the
-th
moment
of
.
If
is not well-defined, then we say that
does not possess the
-th
moment.
The following example shows how to compute a moment of a discrete random variable.
Example
Let
be a discrete random
variable having
support
and
probability mass
function
The
third moment of
can be computed as
follows:
The
-th
central moment of a random variable
is the expected value of the
-th
power of the deviation of
from its expected value.
Definition
Let
be a random variable. Let
.
If the expected
value
exists
and is finite, then
is said to possess a finite
-th
central moment and
is called
the
-th
central moment of
.
The next example shows how to compute the central moment of a discrete random variable.
Example
Let
be a discrete random variable having
support
and
probability mass
function
The
expected value of
is
The
third central moment of
can be computed as
follows:
The following subsections contain more details about moments.
A generalization of the concept of moment to random vectors is introduced in the lecture entitled Cross-moments.
The moments of a random variable can be easily computed by using either its moment generating function, if it exists, or its characteristic function (see the lectures entitled Moment generating function and Characteristic function).
Please cite as:
Taboga, Marco (2021). "Moments of a random variable", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix. https://www.statlect.com/fundamentals-of-probability/moments.
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