On Berry-Esséen rates,a law of the iterated logarithm and an invariance principle for the proportion of the sample below the sample mean

Let F_n(x) be the empirical distribution function based on n independent random variables X₁,…,X_n from a common distribution function F(x), and let $\text{X}\text{= Σ}{}_{\mathrm{i=1}}{}^{\mathrm{n}}\text{X}{}_{\mathrm{i}}\text{n}$ be the sample mean. We derive the rate of convergence of $\text{F}{}_{\mathrm{n}}\text{(}\text{X}\text{)}$ to normality (for the regular as well as nonregular cases), a law of iterated logarithm, and an invariance principle for $\text{F}{}_{\mathrm{n}}\text{(}\text{X}\text{)}$.     

The minimal generating space of a polynomial

Given a polynomial $\text{P(X}{}_1\text{,…,X}{}_{\mathrm{N}}\text{)∈}\text{R}\text{[}\text{X}\text{]}$, we calculate a subspace G_p of the linear space 〈X〉 generated by the indeterminates which is minimal with respect to the property $\text{P∈}\text{R}\text{[}\text{G}{}_{\mathrm{p}}\text{]}$ (the algebra generated by G_p, and prove its uniqueness. Furthermore, we use this result to characterize the pairs (P,Q) of polynomials P(X₁,…,X_n) and Q(X₁,…,X_n) for which there exists an isomorphism T:〈X〉 →〈X〉 that “separates P from Q,” i.e., such that for some k(1<k<n) we can write P and Q as $\text{P}{}^1\text{(Y}{}_1\text{,…,Y}{}_{\mathrm{k}}\text{)}$ and $\text{Q}{}^1\text{(Y}{}_{\mathrm{<mtext>k+</mtext><mtext>1</mtext>}}\text{,…,Y}{}_{\mathrm{n}}\text{)}$ respectively, where $\text{Y}\text{=T}\text{X}$.     

On projections and limit mappings of inverse systems of compact spaces

A compactificaton αX of a completely regular space X is “determined” by a subset F of C¹(X) if αX is the smallest compactificaton of X to which each element of F extends, and is “generated” by F if the evaluation map $\text{e}{}_{\mathrm{F}}\text{:X →}\text{R}{}^{\mathrm{n}}\text{,n = |F|}$, is an embedding and $\text{αX =}\text{e}{}_{\mathrm{F}}\text{(X)}$. Evidently, if F either determines or generates αX, then every elements of F has an extension to αX; whenever F satisfies this latter condition, the set of all such extensions is denoted F^α.A major results of our previous paper is that F determines αX if and only if F^α separates points of αX ? X. A major result of the present paper is that F generates αX if and only if F^α separates points of αX.     

Limit laws for record values

{X_n,n?1} are i.i.d. random variables with continuous d.f. F(x). X_j is a record value of this sequence if X_j>max{X₁,…,X_j?1}. Consider the sequence of such record values {X_Ln,n?1}. Set R(x)=-log(1?F(x)). There exist B_n > 0 such that . in probability (i.p.) iff i.p. iff $\text{{R(kx)?R(x)}}\text{R}{}^{\mathrm{<mtext>1</mtext><mtext>2</mtext>}}\text{(kx)}$ → ∞ as x→∞ for all k>1. Similar criteria hold for the existence of constants A_n such that X_Ln?A_n → 0 i.p. Limiting record value distributions are of the form N(-log(-logG(x))) where G(·) is an extreme value distribution and N(·) is the standard normal distribution. Domain of attraction criteria for each of the three types of limit laws can be derived by appealing to a duality theorem relating the limiting record value distributions to the extreme value distributions. Repeated use is made of the following lemma: If $\text{P}\text{{X}{}_{\mathrm{n}}\text{?x}=1?e}{}^{\mathrm{-x}}\text{,x?0}$, then X_Ln=Y₀+…+Y_n where the Y_j's are i.i.d. and $\text{P}\text{{Y}{}_{\mathrm{j}}\text{?x}=1?e}{}^{\mathrm{-x}}$.     

Limiting distributions of two random sequences

For fixed p (0 ≤ p ≤ 1), let {L₀, R₀} = {0, 1} and X₁ be a uniform random variable over {L₀, R₀}. With probability p let {L₁, R₁} = {L₀, X₁} or = {X₁, R₀} according as $\text{X}{}_1\text{≥}\text{1}\text{2}\text{(L}{}_0\text{+ R}{}_0\text{)}\text{or}\text{<}\text{1}\text{2}\text{(L}{}_0\text{+ R}{}_0\text{)}$; with probability 1 ? p let {L₁, R₁} = {X₁, R₀} or = {L₀, X₁} according as $\text{X}{}_1\text{≥}\text{1}\text{2}\text{(L}{}_0\text{+ R}{}_0\text{)}\text{or}\text{<}\text{1}\text{2}\text{(L}{}_0\text{+ R}{}_0\text{)}$, and let X₂ be a uniform random variable over {L₁, R₁}. For n ≥ 2, with probability p let {L_n, R_n} = {L_{n ? 1}, X_n} or = {X_n, R_{n ? 1}} according as $\text{X}{}_{\mathrm{n}}\text{≥}\text{1}\text{2}\text{(L}{}_{\mathrm{n\; ?\; 1}}\text{+ R}{}_{\mathrm{n\; ?\; 1}}\text{)}\text{or}\text{<}\text{1}\text{2}\text{(L}{}_{\mathrm{n\; ?\; 1}}\text{+ R}{}_{\mathrm{n\; ?\; 1}}\text{)}$, with probability 1 ? p let {L_n, R_n} = {X_n, R_{n ? 1}} or = {L_{n ? 1}, X_n} according as $\text{X}{}_{\mathrm{n}}\text{≥}\text{1}\text{2}\text{(L}{}_{\mathrm{n\; ?\; 1}}\text{+ R}{}_{\mathrm{n\; ?\; 1}}\text{)}\text{or}\text{<}\text{1}\text{2}\text{(L}{}_{\mathrm{n\; ?\; 1}}\text{+ R}{}_{\mathrm{n\; ?\; 1}}\text{)}$, and let X_{n + 1} be a uniform random variable over {L_n, R_n}. By this iterated procedure, a random sequence {X_n}_{n ≥ 1} is constructed, and it is easy to see that X_n converges to a random variable Y_p (say) almost surely as n → ∞. Then what is the distribution of Y_p? It is shown that the Beta, (2, 2) distribution is the distribution of Y₁; that is, the probability density function of Y₁ is g(y) = 6y(1 ? y) I_0,1(y). It is also shown that the distribution of Y₀ is not a known distribution but has some interesting properties (convexity and differentiability).     

On some results of Strichartz and of Rallis and Schiffman

Let (i, H, E) and (j, K, F) be abstract Wiener spaces and let α be a reasonable norm on E ? F. We are interested in the following problem: is ($\text{i ? j, H}\text{}\text{?}\text{bo}{}_2\text{K, E}\text{}\text{?}\text{bo}{}_{\mathrm{\alpha F}}$) an abstract Wiener space ? The first thing we do is to prove that the setting of the problem is meaningfull: namely, i ? j is always a continuous one to one map from $\text{H}\text{}\text{?}\text{bo}{}_2\text{K}$ into $\text{E}\text{}\text{?}\text{bo}{}_{\mathrm{\alpha }}\text{F}$. Then we exhibit an example which shows that the answer cannot be positive in full generality. Finally we prove that if F=L^p(X,$\text{X}$,λ) for some σ-finite measure λ ? 0 then $\text{(i?j, H}\text{?}{}_2\text{K,L}{}^{\mathrm{p}}$(X,$\text{X}$,λ) is an abstract Wiener space. By-products are some new results on γ-radonifying operators, and new examples of Banach spaces and cross norms for which the answer is affirmative (in particular α = π the projective norm, and F=L¹(X,$\text{X}$,λ)).     

Duality between some linear preserver problems. II. Isometries with respect to c-special norms and matrices with fixed singular values

Let $\text{F}{}_{\mathrm{m\times n}}$ (m?n) denote the linear space of all m × n complex or real matrices according as $\text{F}$=$\text{C}$ or $\text{R}$. Let c=(c₁,…,c_m)≠0 be such that c₁???c_m?0. The c-spectral norm of a matrix A?$\text{F}$_m×n is the quantity

$\text{6A6}{}_{\mathrm{c}}\text{∑}\text{i=I}\text{m}\text{c}{}_{\mathrm{i}}\text{σ}{}_{\mathrm{i}}\text{(A)}$

. where σ₁(A)???σ_m(A) are the singular values of A. Let d=(d₁,…,d_m)≠0, where d₁???d_m?0. We consider the linear isometries between the normed spaces $\text{(}\text{F}{}_{\mathrm{m\times }}{}_{\mathrm{n}}\text{,∥·∥}{}_{\mathrm{<mtext>c</mtext>}}\text{)}$ and $\text{(}\text{F}{}_{\mathrm{m\times }}{}_{\mathrm{n}}\text{,∥·∥}{}_{\mathrm{d}}\text{)}$, and prove that they are dual transformations of the linear operators which map $\text{L}$(d) onto $\text{L}$(c), where

$\text{L}\text{(c)= {X?}\text{F}{}_{\mathrm{m\times n}}\text{:X}\text{has singular values}\text{c}{}_1\text{,…,c}{}_{\mathrm{m}}\text{}}$

.     

Central limit theorems for C(S)-valued random variables

Let C(S) be the space of real-valued continuous functions on a compact metric space S. Let {X_n, n ? 1} be a sequence of independent identically distributed C(S)-valued random variables with mean zero and sup_t?sE[X₁²(t)] = 1. We show that the measures induced by ($\text{X}{}_1\text{+ ··· + X}{}_{\mathrm{n}}\text{) n}{}^{\mathrm{<mtext>?</mtext><mtext>1</mtext><mtext>2</mtext>}}$ converge weakly to a Gaussian measure on C(S) under different conditions on X₁, one of which consolidates and extends results of Strassen and Dudley, Giné, and Dudley. Our method of proof is different from the methods employed by these authors.     

Three problems on the lengths of increasing runs

Let U₁, U₂,… be a sequence of independent, uniform (0, 1) r.v.'s and let R₁, R₂,… be the lengths of increasing runs of {U_i}, i.e., X₁=R₁=inf{i:U_i+1<U_i},…, X_n=R₁+R₂+?+R_n=inf{i:i>X_n?1,U_i+1<U_i}. The first theorem states that the sequence $\text{(}\text{3}\text{2}\text{n)}{}^{\mathrm{<mtext>1</mtext><mtext>2</mtext>}}\text{(X}{}_{\mathrm{n}}\text{?2n)}$ can be approximated by a Wiener process in strong sense.Let τ(n) be the largest integer for which R₁+R₂+?+R_τ(n)?n, $\text{R}{}^1{}_{\mathrm{n}}\text{=n?(R}{}_1\text{+R}{}_2\text{+?+R}{}_{\mathrm{\tau (n)}}\text{)}$ and $\text{M}{}_{\mathrm{n}}\text{=}\text{max}\text{{R}{}_1\text{,R}{}_2\text{,…,R}{}_{\mathrm{\tau (n)}}\text{,R}{}^1{}_{\mathrm{n}}\text{}}$. Here M_n is the length of the longest increasing block. A strong theorem is given to characterize the limit behaviour of M_n.The limit distribution of the lengths of increasing runs is our third problem.     

Spaces of matrices of equal rank

Let M_m,n(F) denote the space of all mXn matrices over the algebraically closed field F. A subspace of M_m,n(F), all of whose nonzero elements have rank k, is said to be essentially decomposable if there exist nonsingular mXn matrices U and V respectively such that for any element A, UAV has the form

$\text{UAV=}\text{A}{}_1\text{A}{}_2\text{A}{}_3\text{0}$

where A₁ is iX(k–i) for some i?k. Theorem: If $\text{K}$ is a space of rank k matrices, then either $\text{K}$ is essentially decomposable or dim $\text{K}$?k+1. An example shows that the above bound on non-essentially-decomposable spaces of rank k matrices is sharp whenever n?2k–1.     

The construction of stationary two-dimensional Markoff fields with an application to quantum field theory

It is shown that if φ(f)  ∝_R^dφ(y) f(y) dy is a Markoff random field and X_α are multiplicative functionals of φ (with E(X_α) = 1) which converge locally in L₁, then there exists a locally Markoff random field $\text{φ}{}_1$ such that $\text{E(exp(iφ}{}_1\text{(f))) =}\text{lim}{}_{\mathrm{\alpha }}\text{E(X}{}_{\mathrm{<mtext>\alpha </mtext>}}\text{exp}\text{(iφ(φ)))}$. We choose φ to be the two-dimensional generalization of the Ornstein-Uhlenbeck velocity process and take X_α proportional to exp(?λ∝_R² : P(φ(y)) : g_α(y) dy), where: P(φ(y)) : is a regularized even degree polynomial in φ(y). It is then proved that for an appropriate choice of g_α → 1 and small λ, {X_α} does converge locally in L₁ and that the corresponding $\text{φ}{}_1$ is stationary.     

Loeb-measurable solutions to 1finite games

Let ¹M be a denumerately comprehensive enlargement of a set-theoretic structure sufficient to model R. If F is an internal ¹finite subset of ¹N such that $\text{F = {1,…,γ}, γ?}{}^1\text{N?N}$, we define a class of ¹finite cooperative games having the form $\text{Γ}{}_{\mathrm{F}}\text{(}{}^1\text{ν) = 〈F,A(F),}{}^1\text{ν〉}$, where A(F) is the internal algebra of the internal subsets of F, and ${}^1\text{ν}$ is a set-function with $\text{Dom}{}^1\text{ν=A(F)}$, $\text{Rng}{}^1\text{ν =}{}^1\text{R}{}_{\mathrm{+}}$, and ${}^1\text{ν(Ø) = 0}$. If $\text{SI(}{}^1\text{ν)}$ is the space of S-imputations of a game Γ_F(¹ν) such that ${}^1\text{ν(F)<η}$, for some $\text{η?}{}^1\text{N}$, then we prove that $\text{SI(}{}^1\text{ν)}$ contains two nonempty subsets: $\text{QK(Γ}{}_{\mathrm{F}}\text{(}{}^1\text{ν))}$ and $\text{SM}{}_1\text{(Γ}{}_{\mathrm{F}}\text{(}{}^1\text{ν))}$, termed the quasi-kernel and S-bargaining set, respectively. Both $\text{QK(Γ}{}_{\mathrm{F}}\text{(}{}^1\text{ν))}$ and $\text{SM}{}_1\text{(Γ}{}_{\mathrm{F}}\text{(}{}^1\text{ν))}$ are external solution concepts for games of the form Γ_F (¹ν) and are defined in terms of predicates that are approximate in infinitesimal terms. Furthermore, if L(Θ) is the Loeb space generated by the ¹finitely additive measure space 〈F, A(F), U_F〉, and if a game Γ_F(¹ν) has a nonatomic representation $\text{ψ(}{}^1\text{ν}{}^{\mathrm{?0}}\text{)}$ on L(Θ) with respect to S-bounded transformations, then the standard part of any element in $\text{QK(Γ}{}_{\mathrm{F}}\text{(}{}^1\text{ν))}$ is Loeb-measurable and belongs to the quasi-kernel of $\text{ψ(}{}^1\text{ν}{}^{\mathrm{?0}}\text{)}$ defined in standard terms.     

Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space

Let {X_n} be a ?-irreducible Markov chain on an arbitrary space. Sufficient conditions are given under which the chain is ergodic or recurrent. These extend known results for chains on a countable state space. In particular, it is shown that if the space is a normed topological space, then under some continuity conditions on the transition probabilities of {X_n} the conditions for ergodicity will be met if there is a compact set K and an ? > 0 such that $\text{E}\text{{6X}{}_{\mathrm{n+1}}\text{6 — 6X}{}_{\mathrm{n}}\text{6 ∣ X}{}_{\mathrm{n}}\text{= x} ? ??}$ whenever x lies outside K and $\text{E}\text{{6X}{}_{\mathrm{n+1}}\text{6 ∣ X}{}_{\mathrm{n}}\text{=x}}$ is bounded, x ∈ K; whilst the conditions for recurrence will be met if there exists a compact K with $\text{E}\text{{6X}{}_{\mathrm{n+1}}\text{6 ? 6X}{}_{\mathrm{n}}\text{6 ∣ X}{}_{\mathrm{n}}\text{= x} ? 0}$ for all x outside K. An application to queueing theory is given.     

Hyperfinite Von Neumann games

Let ${}^1\text{[V(}\text{R}\text{)]}$ be an ω₁-saturated enlargement in the sense of Keisler (1977) and let $\text{F}$ be a hyperfinite finite set in ¹$\text{N}$. Following the suggestion of Wesley (1971) we define a class of hyperfinite games of the form: $\text{Γ}{}_{\mathrm{<mtext>F</mtext>}}\text{(}{}^1\text{υ)=〈Φ,}\text{A}\text{(}\text{F}\text{),}{}^1\text{υ〉}$, and show that measure-theoretic analogues of the kernel and bargaining set exist in this nonstandard setting such that their standard parts Loeb-measurable measurable on the Loeb space generated by the internal ¹finitely additive measure $\text{u}{}_{\mathrm{<mtext>F</mtext>}}\text{:}\text{A}\text{(}\text{F}\text{)→}{}^1\text{R}{}_{\mathrm{+}}$.     

Strong approximation theorems for density dependent Markov chains

A variety of continuous parameter Markov chains arising in applied probability (e.g. epidemic and chemical reaction models) can be obtained as solutions of equations of the form

$\text{X}{}_{\mathrm{N}}\text{(t)=x}{}_0\text{+∑}\text{1}\text{N}\text{lY}{}_1\text{N ∫}{}^{\mathrm{t}}{}_0\text{f}{}_1\text{(X}{}_{\mathrm{N}}\text{(s))ds}$

where $\text{l∈}\text{Z}{}^{\mathrm{t}}$, the Y₁ are independent Poisson processes, and N is a parameter with a natural interpretation (e.g. total population size or volume of a reacting solution).The corresponding deterministic model, satisfies

$\text{X(t)=x}{}_0\text{+ ∫}{}^{\mathrm{t}}{}_0\text{∑ lf}{}_1\text{(X(s))ds}$

Under very general conditions lim_N→∞X_N(t)=X(t) a.s. The process X_N(t) is compared to the diffusion processes given by

$\text{Z}{}_{\mathrm{N}}\text{(t)=x}{}_0\text{+∑}\text{1}\text{N}\text{lB}{}_1\text{N∫}{}^{\mathrm{t}}{}_0\text{ft(Z}{}_{\mathrm{N}}\text{(s))ds}$

and

$\text{V(t)=∑ l∫}{}^{\mathrm{t}}{}_0\text{f}{}_1\text{(X(s))}\text{d}\text{W}\text{?}{}_1\text{+∫}{}^{\mathrm{t}}{}_0\text{?F(X(s))·V(s)}\text{ds.}$

Under conditions satisfied by most of the applied probability models, it is shown that X_N,Z_N and V can be constructed on the same sample space in such a way that

$\text{X}{}_{\mathrm{N}}\text{(t)=Z}{}_{\mathrm{N}}\text{(t)+O}\text{logN}\text{N}$

and

$\text{N}\text{(X}{}_{\mathrm{N}}\text{(t)?X(t))=V(t)+O}\text{log N}\text{N}$

     

Inside the D-stable matrices

Let $\text{DS}{}_{\mathrm{n}}\text{(}\text{F}\text{)}$ denote the set of n×nD-stable matrices with entries from $\text{F}\text{?}\text{C}$. A characterization of the interior of $\text{DS}{}_{\mathrm{n}}\text{(}\text{F}\text{)}$ considered as subset of the topological space $\text{F}$ⁿ², is given for the cases $\text{F}\text{=}\text{R}\text{and}\text{C}$.     

Isomorphisms between Lp-function spaces when p < 1

We prove a number of results concerning isomorphisms between spaces of the type L_p(X), where X is a separable p-Banach space and 0 < p < 1. Our results imply that the quotient of L_p([0, 1] × [0, 1]) by the subspace of functions depending only on the first variable is not isomorphic to L_p, answering a question of N. T. Peck. More generally if $\text{B}$₀ is a sub-σ-algebra of the Borel sets of [0, 1], then $\text{L}{}_{\mathrm{p}}\text{([0, 1])}\text{L}{}_{\mathrm{p}}\text{([0, 1]}$, $\text{B}$₀) is isomorphic to L_p if and only if L_p([0, 1], B₀) is complemented. We also show that L_p has, up to isomorphism, at most one complemented subspace non-isomorphic to L_p and classify completely those spaces X for which $\text{L}{}_{\mathrm{p}}\text{(X) ? L}{}_{\mathrm{p}}$. In particular if $\text{L(L}{}_{\mathrm{p}}\text{, X) = {0}}\text{and}\text{L}{}_{\mathrm{p}}\text{(X) ? L}{}_{\mathrm{p}}$ then $\text{X ? l}{}_{\mathrm{p}}$ or is finite-dimensional. If X has trivial dual and $\text{L}{}_{\mathrm{p}}\text{(X) ? L}{}_{\mathrm{p}}\text{then}\text{X ? L}{}_{\mathrm{p}}$.     

Comparaison de deux indicateurs de la régularité en variation des queues de distributionsComparison between two indicators for the variation regularity of tails of distributions

We compare assumptions used in [4] in order to study the rate of convergence to 0, as u→s₊(F), of , where $\text{F}{}_{\mathrm{u}}$ is the survival function of the excesses over u, s₊(F)=sup{x,F(x)<1} is the upper end point of the distribution function (d.f.) F and $\text{G}{}_{\mathrm{\gamma }}$ is the survival function of the Generalized Pareto Distribution, with assumptions used in [2] in order to study the rate of convergence to 0, as n→+∞, of $\text{d}\text{?}{}_{\mathrm{n}}\text{=}\text{sup}{}_{\mathrm{<mtext>x\in </mtext><mtext>R</mtext>}}\text{|F}{}^{\mathrm{n}}\text{(x)?H}{}_{\mathrm{\gamma }}\text{(}\text{x?α}{}_{\mathrm{n}}\text{σ}{}_{\mathrm{n}}\text{)|}$, where H_γ is the d.f. of an extreme value distribution. In each case, an indicator linked to regular variation assumptions had been introduced. We characterize situations where these two indicators coincide, and others where they are different. To cite this article: R. Worms, C. R. Acad. Sci. Paris, Ser. I 334 (2002) 709–712.     

Zeta matrices of elliptic curves

Let $\text{O =}\text{lim}{}_{\mathrm{n}}\text{Z/p}{}^{\mathrm{n}}\text{Z}$, let $\text{A}\text{= O[g}{}_2\text{, g}{}_3\text{]}{}_{\mathrm{\Delta }}$, where g₂ and g₃ are coefficients of the elliptic curve: Y² = 4X³ ? g₂X ? g₃ over a finite field and Δ = g₂³ ? 27g₃² and let $\text{B}\text{=}\text{A}\text{[X, Y]}\text{(Y}{}^2\text{? 4X}{}^3\text{+ g}{}_2\text{X + g}{}_3\text{)}$. Then the p-adic cohomology theory will be applied to compute explicitly the zeta matrices of the elliptic curves, induced by the pth power map on the free $\text{A}{}^2\text{?}{}_{\mathrm{Z}}\text{Q}$-module $\text{H}{}^1\text{(X,}\text{A}{}^2\text{?}{}_{\mathrm{Z}}\text{Q)}$. Main results are; Theorem 1.1: X²dY and YdX are basis elements for $\text{H}{}^1\text{(}\text{X}\text{, Γ}{}_{\mathrm{<mtext>A</mtext>}}{}^1\text{(}\text{X}\text{)}{}^2\text{?}{}_{\mathrm{Z}}\text{Q)}$; Theorem 1.2: YdX, X²dY, Y^?1dX, Y^?2dX and XY^?2dX are basis elements for $\text{H}{}^1\text{(}\text{X}\text{? (Y = 0), Γ}{}_{\mathrm{<mtext>A</mtext>}}{}^1\text{(}\text{X}\text{)}{}^2\text{?}{}_{\mathrm{Z}}\text{Q)}$, where $\text{X}$ is a lifting of X, and all the necessary recursive formulas for this explicit computation are given.     

F-mixing and weak disjointness

A topological system (X,f) is $\text{F}$-transitive if for each pair of opene subsets U and V of X, $\text{N}{}_{\mathrm{f}}\text{(U,V)={n∈}\text{Z}{}_{\mathrm{+}}\text{:}\text{f}{}^{\mathrm{n}}\text{U∩V≠∅}∈}\text{F}$, where $\text{F}$ is a collection of subsets of $\text{Z}{}_{\mathrm{+}}$ which is hereditary upward. (X,f) is $\text{F}$-mixing if (X×X,f×f) is $\text{F}$-transitive. In this paper $\text{F}$-mixing systems are characterized in terms of the chaoticity of the systems. Moreover, weak disjointness is studied via family. We will give conditions such that a dual theorem of the Weiss–Akin–Glasner theorem holds. Examples with this dual theorem fails for some “good” families are obtained.