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14 votes
3 answers
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Concentration bounds for sums of random variables of permutations

I'm trying to find theorems regarding random variables derived from sampling permutations, specifically concentration bounds. As an example, let $X_i$ be the $\{0,1\}$-random variable that represents ...
Joe Bebel's user avatar
  • 539
9 votes
1 answer
886 views

Concentration of sum of powers of normals

Let $Z_1,Z_2,\ldots,Z_n$ be i.i.d. copies of a random variable $Z$ distributed as $\frac{1}{\sqrt{2}}X+i\frac{1}{\sqrt{2}}Y$ with $X$ and $Y$ independent standard Normal random variables i.e.~$X\sim\...
mohi's user avatar
  • 859
7 votes
1 answer
466 views

Martingale version of Bernstein-type inequality for (slightly) heavy-tailed distributions?

It is known that for sub-exponentially distributed martingale difference sequence, the following Bernstein-type inequality holds: $$ ℙ\left(\left| \sum_{i=1}^N a_i X_i \right| \ge t \right) \le 2\...
Nikolayevich's user avatar
7 votes
2 answers
606 views

Uniform Concentration Bounds on Weighted Sum of i.i.d. Bernoulli Random Variables

Let $\delta_1,...,\delta_n$ be $n$ independent identically distributed Bernoulli random variables with $\mathbb{P}(\delta_1=1)=p$. We consider a set $\Omega = \{\mathbf{a}:=(a_1,...,a_n)~|~a_i\in [0,c/...
tourzhao's user avatar
7 votes
0 answers
759 views

Product of two random Gaussian matrices - orthant probability

Let $X \in \mathbb{R}^{m \times n}$ and $Y \in \mathbb{R}^{n \times k} $ be two independent Gaussian random matrices, i.e., with entries independently sampled from $\mathcal{N}(0,1)$ (a normal ...
Daniel Soudry's user avatar
6 votes
3 answers
447 views

Isoperimetric inequality for $\epsilon$-expansion of a set only along a certain subspace

Let $\gamma_n$ be the standard gaussian distribution on $\mathbb R^n$. Let $V$ be a $k$-dimensional subspace of $\mathbb R^n$. Finally let $A$ be any (nonempty) Borel subset of $A$ with $\gamma_n(A) = ...
dohmatob's user avatar
  • 6,853
5 votes
3 answers
5k views

Distribution of the individual coordinates of a uniform random vector on a high-dimensional sphere

Let $X=(X_1,\ldots,X_n)$ be a random vector uniformly distributed on the $n$-dimensional sphere of radius $R > 0$. Intuitively, i think that for large $p$ every coordinate $X_i$ is normally ...
dohmatob's user avatar
  • 6,853
5 votes
2 answers
684 views

Asymptotic Expansion of Distribution in Central Limit Theorem for Non-Identically Distributed Random Variables

My question is related to the following theorem (e.g. Section XVI.4 of Feller's 1971 book): Let $Z_i$ $(i=1,\cdots,n)$ be independent and identically distributed random variables with mean zero, ...
jmscarlett's user avatar
5 votes
1 answer
765 views

Measure concentration for weakly dependent random variables

For an application quite alien to probability theory, I'd like to have a kind of measure concentration estimate, in the following spirit. Suppose that to every $1\le i,j\le n$ there corresponds a zero-...
Seva's user avatar
  • 23k
5 votes
1 answer
225 views

Anti-concentration of Gaussian when conditioning on event

Let $v$ be a given vector with $\|v\|_{\Sigma^{-1}} \leq 1$, where $\Sigma$ is a positive semi-definite matrix and $\|v\|_{\Sigma^{-1}} = \sqrt{v^\top\Sigma v}$. Meanwhile, let $u$ be a random vector ...
Minkov's user avatar
  • 1,127
5 votes
1 answer
1k views

Explicit constant for Carbery–Wright inequality

The Carbery–Wright inequality is a seminal result about the anti-concentration of polynomials of Gaussian random variables. See e.g. Meka, Nguyen, and Vu - Anti-concentration for polynomials of ...
user134977's user avatar
5 votes
0 answers
711 views

Concentration inequality for max component of a multivariate Gaussian in the general case

I am looking to bound the variance of the maximum component of a vector distributed multivariate Gaussian in the general case where the Gaussian distribution has arbitrary mean and full covariance ...
ted's user avatar
  • 283
4 votes
1 answer
681 views

Tail bound for product of normal distribution

Let $U, V$ be two standard normal random variables with covariance $cov(U,V) = \beta \in [0,1)$. Let $W = UV$ be the product of two RV's, and $W_1, W_2, \ldots, W_n$ be n i.i.d copies of $W$, what's ...
Wuchen's user avatar
  • 515
4 votes
1 answer
349 views

Variance of maximum of mixture of gaussians

Let $\{X_i\}$ be an iid collection of standard normal $(N(0,1))$ random variables . Let $X = (X_1,\ldots,X_n)$, and consider a function of the form $f(X) = \max(A\cdot X)$, where $A$ is some symmetric,...
arjun's user avatar
  • 941
4 votes
2 answers
343 views

Concentration of $k$-th pairwise distance of random points in a unit square

For $1\leq i \leq n$, let $X_i\sim \text{Uniform}(0,1)$, $Y_i \sim \text{Uniform}(0,1)$ be $n$ points chosen uniformly in the unit square. Denote the $k$-th smallest pairwise distances across the $n$ ...
AspiringMat's user avatar
4 votes
1 answer
229 views

Product of estimates of mean values - Concentration of measure inequality

Let $X_{1},...,X_{d} \in \{-1,1\}^d$ be random variables, with $E[X_j]=\mu_j$. Having $n$ i.i.d. samples $x^{(i)}_1,x^{(i)}_2,....,x^{(i)}_d$, $i=1,...,n $, let $\hat{\mu}_{j}=\frac{1}{n}\sum^{n}_{i=1}...
user_Lee's user avatar
  • 107
4 votes
0 answers
162 views

Concentration Inequality for Score Functions of Exponential Familty

Let $p$ be the density of a continuous one-parameter exponential family distribution on $\mathbb{R}$. We assume that $$p(x) = c(x)\cdot \exp\bigl [ x \cdot \theta - b(\theta ) \bigr ], $$ where $\...
Steve's user avatar
  • 1,127
3 votes
1 answer
178 views

Tail probability of random projection

Suppose $v\in R^n$ is a constant unit vector. $P_l$ is a random projection matrix to an $l$ dimensional subspace of $R^n$ which is uniformly sampled from $G(l,R^n)$ which is the collection of all $l$-...
neverevernever's user avatar
3 votes
2 answers
731 views

Concentration inequality for sum of iid random variables that involve KL distance

Conider $X \in \mathbb{R}^d$ and $Y \in \{0,1\}$, and a joint distribution $p_{XY}(x,y)$, and a set of $N$ i.i.d. samples $\{(X_i,Y_i)\}_{i=1}^{N}$. Define $p_{X0} = p_{XY}(x,0)$ and $p_{X1} = p_{XY}(...
Jeff's user avatar
  • 482
3 votes
1 answer
527 views

Wasserstein-type concentration inequalities for empirical measures on polish spaces

Let $(\mathcal{X},d)$ be a Polish (metric) space and let $\{X_n\}_{n=1}^{\infty}$ be a sequence of i.i.d. $\mathcal{X}$-valued random elements defined on a common complete (standard) probability space ...
ABIM's user avatar
  • 5,405
3 votes
2 answers
589 views

Measure concentration for law of large numbers

The classical law of large numbers states that $$\frac1k\sum_{i=1}^k X_i \rightarrow \mathbb{E} X_1$$ for i.i.d. $X_1, X_2, \ldots$ with finite $L^1$ norm. I was wondering whether is it possible to ...
John Wong's user avatar
  • 773
3 votes
1 answer
460 views

Derive concentration bound for the derivative

It that true to conclude that if a random $f(z)$ is a sub-Gaussian random variable for a constant value of z, its derivative $f'(z)|_{z=k}$ with respect to variable $z$ is also sub-Gaussian? In ...
Amirreza Shaban's user avatar
3 votes
0 answers
334 views

Tail bound on trace norm / nuclear norm / Schatten-1 norm of Rademacher matrix

Let $0 < r \leq d$ integers. Let $X$, $Y$ be $d \times r$ matrices of independent Rademacher variables, that is, $X,Y \in \mathbb{R}^{d \times r}$ with entries $\pm1$ with probability $1/2$. I am ...
arriopolis's user avatar
3 votes
0 answers
186 views

Anti-concentration for sum of t-wise independent uniform variables

Let $X_{1},\ldots,X_{n}$ be i.i.d. random variables, each variable is uniform over the set of integers $\{ 0,\ldots,D-1 \}$. Let $S = \sum_{i=1}^{n}X_{i}$. By ``small ball probability'', we have that ...
Daniel86's user avatar
  • 225
2 votes
1 answer
847 views

Concentration inequality for quadratic form of Gaussian variables with non-idempotent matrix

Given $y \sim N(0,\sigma^2 I)$, and $M$ that is a symmetric matrix (not necessarily idempotent) what is the distribution of ${y^T M y}$? is there a high probability bound on $|{y^T M y}|$? Most ...
Enigman's user avatar
  • 123
2 votes
1 answer
302 views

Concentration on discrete probability estimator

Let $t>1$ and $X_1,..., X_t$ a set of real random variables from a discrete distribution, whose pmf is $p(x)$, supported on the points $1,...,k$. Let $N_t(x) = \sum_{i = 1}^t \mathbb{1}_{X_i =\, x}....
Apprentice's user avatar
2 votes
1 answer
271 views

How to compute bounding coefficients for McDiarmid's inequality?

I am trying to understand the proof in Sec. A2 of Gretton et al.. To make the question self-contained, I summarize below the key ingredients. At the end of the post, I state my question. Given a ...
Francesco Solera's user avatar
2 votes
1 answer
886 views

Asymptotic behavior of a ratio of sums of iid random variables

Let $X_i$ and $Y_i$ be distributed identically to $X$ and $Y$, respectively. Assume both $X$ and $Y$ take strictly positive values. Consider the random variable $R_n \doteq \frac{\sum_{i=1}^n X_i}{\...
Patrick Sanan's user avatar
2 votes
1 answer
136 views

Concentration bound for a increasingly weighted sum of bernoulli random variables

Given $x_1,x_2,\ldots,x_n$ i.i.d. bernoulli random variables with $P(x_i=1)=\frac1n$. Given a constant $c=1+\frac{1}{m}, m\geq n$. Is there an explicit theorem that can derive a concentration argument ...
Betty's user avatar
  • 25
2 votes
1 answer
635 views

Azuma's Inequality when the conditions hold with high probability?

In Azuma's Inequality, is the statement true when $|X_k - X_{k-1}| < c_k$ almost surely rather than with probability 1? If not, is there another result which gives strong concentration when the ...
Patt Geffrey's user avatar
2 votes
0 answers
83 views

Concentration inequalities for sets

Assume that we have a random set $B$ which is constructed by selecting elements from $U = \{ X_1, \dots, X_n \}$ where $X_i$ are independent samples from Gaussians with means $\mu_i$ and variances $\...
bolzano's user avatar
  • 143
1 vote
1 answer
115 views

How does Chernoff-Hoeffding bound with limited independence reduce to the usual generic CH bound with complete independence

As the title might suggest, I am referring to this paper https://www.cs.umd.edu/~srin/PDF/ch-bounds.pdf , titled : Chernoff-Hoeffding Bounds for Application with Limited Independence. The theorem in ...
some1fromhell's user avatar
1 vote
1 answer
137 views

Approximate $\log \mathbb E_P[\exp(th(x)]$ for a function $h$ which is lipschitz and has finite moments of order 1 and 2 w.r.t $P$

Let $P$ be a probability measure on a space $\mathcal X$ and $h: \mathcal X \rightarrow \mathbb R$ is measurable function with finite moments of order 1 and 2. I'm interested in approximating the ...
dohmatob's user avatar
  • 6,853
1 vote
2 answers
111 views

Concentration bound for sum of indicators of maximum value of k combinations

Let $X_1, \dots, X_n$ be i.i.d. random variables distributed as $\mathrm{Exp}(\lambda)$ for some $\lambda > 0$ and let $t > 0$. For every combination $J$ of $k$ of these variables, we define $...
bolzano's user avatar
  • 143
1 vote
1 answer
3k views

Tail bound regime for Binomial distribution in concentration paper

In paper 'Concentration Inequalities and Martingale Inequalities:A Survey' gives the following inequality: My question is whether the inequality holds in regime $\lambda$ being $o(\sqrt n)$ (say $\...
VS.'s user avatar
  • 1,826
1 vote
1 answer
467 views

Sharp tail bounds for the maximum of an iid sample of a random variable supported on $[0, 1]$

Let $X_1,\ldots,X_n$ be an iid sample from a distribution supported on $[0, 1]$. Question What are some sharp concentration inequalities (i.e tail bounds) empirical statistic defined by $Z_n := \max(...
dohmatob's user avatar
  • 6,853
1 vote
1 answer
249 views

On concentration of a sum random variable

Take a random variable defined as $$r=u_{11}v_{1}v_{1}+u_{12}v_{1}v_{2}+\dots+u_{n,n-1}v_{n}v_{n-1}+u_{nn}v_{n}v_{n}$$ where $v_{i}$ are independent uniform random variables from $\{0,\dots,b\}$, $u_{...
Turbo's user avatar
  • 13.9k
1 vote
1 answer
308 views

$L_1$ norm concentration of an empirical distribution

Suppose we have one random variable $X$, whose sample space is $\mathbb{X}=\{x_1,x_2,\dots,x_m\}$, and the size of the sample space is $m$. We have $N$ i.i.d. samples from this distribution, and $x_i$ ...
white's user avatar
  • 23
1 vote
1 answer
118 views

What is the order of the left tail of a mixture of non-central chi-square?

Let $\mu\sim N(0,1)$, $Z\sim N(\mu,1)$. Then $Z$ can be viewed as a mixture of Gaussians. It can also be viewed as a Gaussian but there is a prior for the mean. Let $X\sim\exp(\lambda)$ where the ...
neverevernever's user avatar
1 vote
1 answer
122 views

Variance bound of a functional

$X_1,\ldots,X_n$ are i.i.d standard normal random variables. $a_1,\ldots, a_n$ are constants with $a_i \in [\kappa_1, \kappa_2]$ for all $i$ and $\kappa_1>0$. $\hat c_n$ is given as the solution ...
Gourab Mukherjee's user avatar
1 vote
0 answers
68 views

(Anti-)concentration of gap between largest and second largest component of multivariate random gaussian vector

Let $n$ be a large positive integer and let $Y=(Y_1,\ldots,Y_n)$ be a zero-centered random $n$-dmensional real vector with covariance matrix $\Sigma$, an $n$-by-$n$ positive definite matrix with ...
dohmatob's user avatar
  • 6,853
1 vote
0 answers
376 views

Anti-concentration bounds for folded normal and inverse of gaussian variables

Are there any easy to use bounds on sums of the following kind : $$ \sum_{i = 1}^{i = N} |a_i| \geq P \\ a_i \sim \mathcal{N}(0, 1) \\ $$ and also for sums of the form : $$ \sum_{i = 1}^{i = M} \...
Govind Gopakumar's user avatar
0 votes
1 answer
808 views

Concentration of $\ell_2$ norm of a vector sampled from a distribution

Let $X=(X_1,\ldots,X_n)$, where $X_i \sim P_{p_i}(0,\frac{1}{\lambda})$ are iid, $P_{p_i}$ is sub gaussian distribution for $i^\text{th}$ element, and 0 and $1/\lambda$ are mean and variance. I'm ...
newbie's user avatar
  • 61
0 votes
1 answer
231 views

Concentration inequalities for random sampling without replacement

Let a population $C$ consist of $N$ values $c_1, c_2, \cdots, c_N$, with $c_i\in \{0,1\}$. Let $X_1, X_2, \cdots, X_n$ denote a random sample without replacement from $C$ and let $Y_1, Y_2, \cdots, ...
Dotman's user avatar
  • 105
0 votes
1 answer
378 views

Concentration of norm of linearly transformed normal random vector as dimension go to infinity

Earlier asked on MSE, but didn't get an answer, so posting here: Let $X=(X_1 \dots X_n) \in \mathbb{R}^n, X_i\sim N(0,1), iid.$ Let $B: \mathbb{R}^n \to \mathbb{R}^n $ be the diagonal linear map: $...
Learning math's user avatar
0 votes
1 answer
967 views

Bound the norm of sum of random vector that generated from standard basis

I have a question like this: Consider $N$ samples $X_1, X_2, ..., X_N$ that uniformly random generated from standard basis $\{e_i, i=1,2,...,d\}$, i.e. $(1,0,0,\cdots,0),(0,1,0,\cdots,0),(0,0,1,0,\...
Betty's user avatar
  • 25
0 votes
1 answer
78 views

Uniform concentration bound (function-valued random variable / continuous stochastic process)

I'm trying to consider a probability space $\Omega$ and $f(x,\xi):\mathcal{X}\times\Omega\to\mathbb{R}$ (stochastic process over space? or function-valued random variable?), where $\mathcal{X}\subset\...
YJ Kim's user avatar
  • 321
0 votes
0 answers
116 views

Concentration bounds for sum of weighted sampling without replacement

Let $X$ be a collection of $2l$ non-negative numbers $X_1,X_2,\ldots,X_{2l}$. We draw $l$ weighted (proportional to values) samples without replacement from $X$. Let $S$ denote this set of $l$ samples....
Sankhya's user avatar
  • 11
0 votes
0 answers
86 views

Expected diameter of a random point set

General problem: For a point set $S\subset X$ in a metric space $(X,d)$, let $\text{diam}(S)=\max_{x,y\in S}d(x,y)$. Given a distribution $P$ on $X$ and $m$ i.i.d. points $x_1,\ldots,x_m\sim P$, what ...
user34500's user avatar
-1 votes
2 answers
614 views

Bounded difference functions and sub-Gaussian random variables

We have the following standard theorem : Let $X$ be some set and $g : X^n \rightarrow \mathbb{R}$ be a measurable function such that it satisfies the ``bounded difference property" i.e $\exists$ $\{...
gradstudent's user avatar
  • 2,246