A strictly stationary, N-tuplewise independent counterexample to the central limit theorem

A strictly stationary, M-tuplewise independent counterexample to the Central Limit Theorem

For $M \geq 2$ we construct a strictly stationary, $M$-tuplewise independent sequence that is mixing (in the ergodic-theoretic sense) and yet still fails to satisfy the Central Limit Theorem.

A strictly stationary, "causal," 5-tuplewise independent counterexample to the central limit theorem

A strictly stationary sequence of random variables is constructed with the following properties: (i) the random variables take the values -1 and +1 with probability 1/2 each, (ii) every five of the random variables are independent, (iii) the sequence is "causal" in a certain sense, (iv) the sequence has a trivial double tail sigma-field, and (v) regardless of the normalization used, the partial sums do not converge to a (nondegenerate) normal law. The example has some features in common with a recent construction (for an arbitrary fixed positive integer N), by Alexander Pruss and the author, o…

On possible mixing rates for some strong mixing conditions for N-tuplewise independent random fields

For a given pair of positive integers $d$ and $N$ with $N \geq 2$, for strictly stationary random fields that are indexed by the $d$-dimensional integer lattice and satisfy $N$-tuplewise independence, the dependence coefficients associated with the $ρ$-, $ρ'$-, and $ρ^*$-mixing conditions can decay together at an arbitrary rate. Another, closely related result is also established. In particular, these constructions provide classes of examples pertinent to limit theory for random fields that involve such mixing conditions together with certain types of "extra" assumptions on the marginal and bi…

成对和三重独立同分布随机变量的经典中心极限定理反例的完善

A completion of counterexamples to the classical central limit theorem for pair- and triplewise independent and identically distributed random variables

本文完善了经典中心极限定理的反例研究，证明对于任意具有有限方差且不集中于单点的概率分布，均存在三重独立同分布随机变量序列使其标准化部分和依分布收敛于非正态分布，并可推广至 $ k $ 重独立性。

Counterexamples to the classical central limit theorem for triplewise independent random variables having a common arbitrary margin

We present a general methodology to construct triplewise independent sequences of random variables having a common but arbitrary marginal distribution $F$ (satisfying very mild conditions). For two specific sequences, we obtain in closed form the asymptotic distribution of the sample mean. It is non-Gaussian (and depends on the specific choice of $F$). This allows us to illustrate the extent of the 'failure' of the classical central limit theorem (CLT) under triplewise independence. Our methodology is simple and can also be used to create, for any integer $K$, new $K$-tuplewise independent seq…

关于严格平稳、可逆马尔可夫链的中心极限问题

On the central limit question for strictly stationary, reversible Markov chains

本文研究严格平稳马尔可夫链的中心极限问题，通过反例探讨可逆性在不同混合速率下的影响。