Marcinkiewicz–Zygmund inequality

Theorem ^[1][2] If ${\displaystyle \textstyle X_{i}}$ , ${\displaystyle \textstyle i=1,\ldots ,n}$ , are independent random variables such that ${\displaystyle \textstyle E\left(X_{i}\right)=0}$ and ${\displaystyle \textstyle E\left(\left\vert X_{i}\right\vert ^{p}\right)<+\infty }$ , ${\displaystyle \textstyle 1\leq p<+\infty }$ , then

${\displaystyle A_{p}E\left(\left(\sum _{i=1}^{n}\left\vert X_{i}\right\vert ^{2}\right)_{}^{p/2}\right)\leq E\left(\left\vert \sum _{i=1}^{n}X_{i}\right\vert ^{p}\right)\leq B_{p}E\left(\left(\sum _{i=1}^{n}\left\vert X_{i}\right\vert ^{2}\right)_{}^{p/2}\right)}$

where ${\displaystyle \textstyle A_{p}}$ and ${\displaystyle \textstyle B_{p}}$ are positive constants, which depend only on ${\displaystyle \textstyle p}$ and not on the underlying distribution of the random variables involved.

In the case ${\displaystyle \textstyle p=2}$ , the inequality holds with ${\displaystyle \textstyle A_{2}=B_{2}=1}$ , and it reduces to the rule for the sum of variances of independent random variables with zero mean, known from elementary statistics: If ${\displaystyle \textstyle E\left(X_{i}\right)=0}$ and ${\displaystyle \textstyle E\left(\left\vert X_{i}\right\vert ^{2}\right)<+\infty }$ , then

${\displaystyle \mathrm {Var} \left(\sum _{i=1}^{n}X_{i}\right)=E\left(\left\vert \sum _{i=1}^{n}X_{i}\right\vert ^{2}\right)=\sum _{i=1}^{n}\sum _{j=1}^{n}E\left(X_{i}{\overline {X}}_{j}\right)=\sum _{i=1}^{n}E\left(\left\vert X_{i}\right\vert ^{2}\right)=\sum _{i=1}^{n}\mathrm {Var} \left(X_{i}\right).}$

Several similar moment inequalities are known as Khintchine inequality and Rosenthal inequalities, and there are also extensions to more general symmetric statistics of independent random variables.^[3]