List questions from probability-limit-theorems

42

Views

Why is this estimator with ill-defined moments useful? And why is the Cauchy PV of its expectation integral a reasonable measure of center?

Published on 26 Mar 2026 - 17:53

26

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Does the infinite limit probability of additive closure for pairs of elements from $(0, 1, 2 \cdots, n)$ not exist?

Published on 01 Apr 2026 - 6:30

59

Views

Concentration of probability measures

Published on 17 May 2022 - 17:08

183

Views

Return of Brownian motion to zero

Published on 17 May 2022 - 19:44

359

Views

Tail bounds for sub-Gaussian and sub-exponential distributions

Published on 28 Mar 2026 - 17:39

318

Views

Covariance matrix and the Cramer-Wold device

Published on 19 May 2022 - 19:42

108

Views

Central limit theorems and almost sure invariance principles

Published on 25 May 2022 - 9:20

276

Views

Speed of convergence of empirical probability mass function

Published on 28 Mar 2026 - 17:41

29

Views

Variance of a random variable given a statement about some limit

Published on 27 May 2022 - 14:42

41

Views

Estimate of binomial distribution with its expectation

Published on 29 May 2022 - 7:26

122

Views

Converge almost surely to zero with arbitrary $a_{nk}$

Published on 31 Mar 2026 - 19:12

77

Views

Proof $P \left(\frac{ |X_{1}+...+X_{n}|}{n} > \epsilon \right) \rightarrow 0$

Published on 15 Jun 2022 - 17:12

68

Views

Comparison of limit theorems

Published on 18 Jun 2022 - 20:07

166

Views

Hypothesis test, finding p-value

Published on 23 Feb 2026 - 17:21

55

Views

Strong invariance principles

Published on 19 Jun 2022 - 23:14

List Question

Why is this estimator with ill-defined moments useful? And why is the Cauchy PV of its expectation integral a reasonable measure of center?

Does the infinite limit probability of additive closure for pairs of elements from $(0, 1, 2 \cdots, n)$ not exist?

Concentration of probability measures

Return of Brownian motion to zero

Tail bounds for sub-Gaussian and sub-exponential distributions

Covariance matrix and the Cramer-Wold device

Central limit theorems and almost sure invariance principles

Speed of convergence of empirical probability mass function

Variance of a random variable given a statement about some limit

Estimate of binomial distribution with its expectation

Converge almost surely to zero with arbitrary $a_{nk}$

Proof $P \left(\frac{ |X_{1}+...+X_{n}|}{n} > \epsilon \right) \rightarrow 0$

Comparison of limit theorems

Hypothesis test, finding p-value

Strong invariance principles

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