Upper bound of expected maximum of weighted sub-gaussian r.v.s
$begingroup$
Let $X_1, X_2, ldots$ be an infinite sequence of sub-Gaussian random variables which are not necessarily independent.
My question is how to prove
begin{eqnarray}
mathbb{E}max_i frac{|X_i|}{sqrt{1+log i}} leq C K,
end{eqnarray}
where $K=max_i |X_i|_{psi_2}$. Note that $|cdot|_{psi_2}$ is the Orlicz norm for sub-Gaussian random variable.
Here is my thought that confuses me.... Consider the finite case with $ileq N$, we have
begin{eqnarray}
mathbb{E}max_{ileq N} frac{|X_i|}{sqrt{1+log i}} &=& int_0^infty mathbb{P}left(max_{ileq N} frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& int_0^infty sum_{i=1}^Nmathbb{P}left( frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& sum_{i=1}^N frac{2}{sqrt{1+log i}} int_0^infty e^{-cs^2/K^2}ds \
&=& Ksqrt{frac{pi}{c}} sum_{i=1}^N frac{1}{sqrt{1+log i}}
end{eqnarray}
where the first inequality holds by a simple union bound and the second inequality holds by sub-Gaussianity of $X_i$ (i.e. we have $mathbb{P}{|X_i|geq t} leq 2 e^{-ct^2/|X_i|_{psi_2}^2}$ and $c$ is an absolute constant) and a simple trick of change-of-variable (i.e. let $s := tsqrt{1+log i}$).
However, the problem of my proof above is that the sum $sum_{i=1}^N frac{1}{sqrt{1+log i}}toinfty$ as $Ntoinfty$. Intuitively, I think the inequalities I used here are not very sharp. But what is the right inequality to use in this case???
This question comes from Exercise 2.5.10 of Prof. Roman Vershynin's book titled as "High-Dimensional Probability". The electric version of this book is downloadable from his personal webpage.
probability probability-distributions order-statistics
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
$endgroup$
add a comment |
$begingroup$
Let $X_1, X_2, ldots$ be an infinite sequence of sub-Gaussian random variables which are not necessarily independent.
My question is how to prove
begin{eqnarray}
mathbb{E}max_i frac{|X_i|}{sqrt{1+log i}} leq C K,
end{eqnarray}
where $K=max_i |X_i|_{psi_2}$. Note that $|cdot|_{psi_2}$ is the Orlicz norm for sub-Gaussian random variable.
Here is my thought that confuses me.... Consider the finite case with $ileq N$, we have
begin{eqnarray}
mathbb{E}max_{ileq N} frac{|X_i|}{sqrt{1+log i}} &=& int_0^infty mathbb{P}left(max_{ileq N} frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& int_0^infty sum_{i=1}^Nmathbb{P}left( frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& sum_{i=1}^N frac{2}{sqrt{1+log i}} int_0^infty e^{-cs^2/K^2}ds \
&=& Ksqrt{frac{pi}{c}} sum_{i=1}^N frac{1}{sqrt{1+log i}}
end{eqnarray}
where the first inequality holds by a simple union bound and the second inequality holds by sub-Gaussianity of $X_i$ (i.e. we have $mathbb{P}{|X_i|geq t} leq 2 e^{-ct^2/|X_i|_{psi_2}^2}$ and $c$ is an absolute constant) and a simple trick of change-of-variable (i.e. let $s := tsqrt{1+log i}$).
However, the problem of my proof above is that the sum $sum_{i=1}^N frac{1}{sqrt{1+log i}}toinfty$ as $Ntoinfty$. Intuitively, I think the inequalities I used here are not very sharp. But what is the right inequality to use in this case???
This question comes from Exercise 2.5.10 of Prof. Roman Vershynin's book titled as "High-Dimensional Probability". The electric version of this book is downloadable from his personal webpage.
probability probability-distributions order-statistics
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
$endgroup$
add a comment |
$begingroup$
Let $X_1, X_2, ldots$ be an infinite sequence of sub-Gaussian random variables which are not necessarily independent.
My question is how to prove
begin{eqnarray}
mathbb{E}max_i frac{|X_i|}{sqrt{1+log i}} leq C K,
end{eqnarray}
where $K=max_i |X_i|_{psi_2}$. Note that $|cdot|_{psi_2}$ is the Orlicz norm for sub-Gaussian random variable.
Here is my thought that confuses me.... Consider the finite case with $ileq N$, we have
begin{eqnarray}
mathbb{E}max_{ileq N} frac{|X_i|}{sqrt{1+log i}} &=& int_0^infty mathbb{P}left(max_{ileq N} frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& int_0^infty sum_{i=1}^Nmathbb{P}left( frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& sum_{i=1}^N frac{2}{sqrt{1+log i}} int_0^infty e^{-cs^2/K^2}ds \
&=& Ksqrt{frac{pi}{c}} sum_{i=1}^N frac{1}{sqrt{1+log i}}
end{eqnarray}
where the first inequality holds by a simple union bound and the second inequality holds by sub-Gaussianity of $X_i$ (i.e. we have $mathbb{P}{|X_i|geq t} leq 2 e^{-ct^2/|X_i|_{psi_2}^2}$ and $c$ is an absolute constant) and a simple trick of change-of-variable (i.e. let $s := tsqrt{1+log i}$).
However, the problem of my proof above is that the sum $sum_{i=1}^N frac{1}{sqrt{1+log i}}toinfty$ as $Ntoinfty$. Intuitively, I think the inequalities I used here are not very sharp. But what is the right inequality to use in this case???
This question comes from Exercise 2.5.10 of Prof. Roman Vershynin's book titled as "High-Dimensional Probability". The electric version of this book is downloadable from his personal webpage.
probability probability-distributions order-statistics
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
$endgroup$
Let $X_1, X_2, ldots$ be an infinite sequence of sub-Gaussian random variables which are not necessarily independent.
My question is how to prove
begin{eqnarray}
mathbb{E}max_i frac{|X_i|}{sqrt{1+log i}} leq C K,
end{eqnarray}
where $K=max_i |X_i|_{psi_2}$. Note that $|cdot|_{psi_2}$ is the Orlicz norm for sub-Gaussian random variable.
Here is my thought that confuses me.... Consider the finite case with $ileq N$, we have
begin{eqnarray}
mathbb{E}max_{ileq N} frac{|X_i|}{sqrt{1+log i}} &=& int_0^infty mathbb{P}left(max_{ileq N} frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& int_0^infty sum_{i=1}^Nmathbb{P}left( frac{|X_i|}{sqrt{1+log i}} > t right) dt \
&leq& sum_{i=1}^N frac{2}{sqrt{1+log i}} int_0^infty e^{-cs^2/K^2}ds \
&=& Ksqrt{frac{pi}{c}} sum_{i=1}^N frac{1}{sqrt{1+log i}}
end{eqnarray}
where the first inequality holds by a simple union bound and the second inequality holds by sub-Gaussianity of $X_i$ (i.e. we have $mathbb{P}{|X_i|geq t} leq 2 e^{-ct^2/|X_i|_{psi_2}^2}$ and $c$ is an absolute constant) and a simple trick of change-of-variable (i.e. let $s := tsqrt{1+log i}$).
However, the problem of my proof above is that the sum $sum_{i=1}^N frac{1}{sqrt{1+log i}}toinfty$ as $Ntoinfty$. Intuitively, I think the inequalities I used here are not very sharp. But what is the right inequality to use in this case???
This question comes from Exercise 2.5.10 of Prof. Roman Vershynin's book titled as "High-Dimensional Probability". The electric version of this book is downloadable from his personal webpage.
probability probability-distributions order-statistics
probability probability-distributions order-statistics
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
asked Dec 14 '18 at 10:14
W. LinW. Lin
63
63
add a comment |
add a comment |
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