独立随机变量第k个最大值的密度函数的局部一致收敛

ξ_1,ξ_2,…,ξ_n是独立同分布随机变量,公共分布函数F(x)绝对连续,g_n.k(x)为ξ_1,…,ξ_n的第k个规范化最大值的分布密度函数.本文讨论了g_n,k(x)的局部一致收敛性以及在L_p(O<p≤∞)空间中的收敛性.

独立重尾随机变量随机和的大偏差估计(英文)

本文研究了一类独立重尾随机变量随机和S(t) ∑N(t)k=1Xk ,t≥ 0的大偏差概率 .其中 {N(t) ,t≥ 0 }是一族非负整数值随机变量 ;{Xn,n≥ 1}是非负、独立随机变量序列 ,并与 {N(t) ,t≥ 0 }独立 .本文的结果将{Xn,n≥ 1}为独立同分布情形推广到了独立不同分布情形 .

On the Confidence Limits of Bernoulli Parameter in Any Sequential Case

Let x 1,x 2,… be independent identically distributed (i.i.d.) random variables, in which x n=0 or 1 and the probability of ｛x n=1｝ is p. Here p is unknown. Let τ be any finite stopping time for (x n,n1). For any sequential sample (x 1,x 2,…,x τ ) and γ∈(0,1), we have given an optimal confidence limit of p with confidence level γ . Some related problems are also discussed.

Some Practical Issues Related to Univariate Regression Analysis Prior to Multivariate Regression Analysis in Randomized Controlled Clinical Trials

Often many variables have to be analyzed for their importance in terms of significant contribution and predictability in medical research. One of the possible analytical tools may be the Multiple Linear Regression Analysis. However, research papers usually report both univariate and multivariate regression analyses of the data. The biostatistician sometimes faces practical difficulties while selecting the independent variables for logical inclusion in the multivariate analysis. The selection criteria for inclusion of a variable in the multivariate regression is that the variable at the univariate level should have a regression coefficient with p 〈 0.20. However, there is a chance that an independent variable with p 〉 0.20 at univariate regression may become significant in the multivariate regression analysis and vice versa, provided the above criteria is not strictly adhered to. We undertook both univariate and multivariate linear regression analyses on data from two multi-centric clinical trials. We recommend that there is no need to restrict the p value of 〈= 0.20. Because of high speed computer and availability of statistical software, the desired results could be achieved by considering all relevant independent variables in multivariate regression analysis.

GL（n, R）-Equivalence of a Pair of Curves in Terms of Invariants

In this paper, the generator set of R 〈 x1,x2 〉G is obtained in according to the group G = Gl（n,R）. The conditions of G = Gl（n, R） -equivalence of a pair of curves are found in terms of G = Gl（n, R）-invariants. And the independence of GL（n, R） -invariants is shown.

The Law of the Iterated Logarithm for Independent Random Variables with Multidimensional Parameters and Its Application

For a set of i.i.d.r.v. indexed by positive integer d-dimensional lattice points, and for some general normalizing sequence, we determine necessary and sufficient conditions for the law of iterated logarithm. As its application, we give conditions for the existence of moments of the supremum of normed partial sums.

Sharp large deviation results for sums of independent random variables

We show sharp bounds for probabilities of large deviations for sums of independent random variables satisfying Bernstein's condition. One such bound is very close to the tail of the standard Gaussian law in certain case; other bounds improve the inequalities of Bennett and Hoeffding by adding missing factors in the spirit of Talagrand(1995). We also complete Talagrand's inequality by giving a lower bound of the same form, leading to an equality. As a consequence, we obtain large deviation expansions similar to those of Cram′er(1938),Bahadur-Rao(1960) and Sakhanenko(1991). We also show that our bound can be used to improve a recent inequality of Pinelis(2014).

Self-normalized moderate deviations for independent random variables

Let X1,X2,... be a sequence of independent random variables （r.v.s） belonging to the domain of attraction of a normal or stable law. In this paper, we study moderate deviations for the self-normalized sum n X ∑^n_i=1Xi/Vm,p ,where Vn,p （∑^n_i=1｜Xi｜p）^1/p （P 〉 1）.Applications to the self-normalized law of the iteratedlogarithm, Studentized increments of partial sums, t-statistic, and weighted sum of independent and identically distributed （i.i.d.） r.v.s are considered.

EQUIVALENCE OF COMPLETE CONVERGENCEAND LAW OF LARGE NUMBERS FOR B-VALUED RANDOM ELEMENTS

Under some conditions on probability, this note discusses the equivalence between the complete convergence and the law of large number for B-valued independent random elements. The results of [10] become a simple corollary of the results here. At the same time, the author uses them to investigate the equivalence of strong and weak law of large numbers, and there exists an example to show that the conditions on probability are weaker.

Rosenthal's inequalities for independent and negatively dependent random variables under sub-linear expectations with applications

Classical Kolmogorov's and Rosenthal's inequalities for the maximum partial sums of random variables are basic tools for studying the strong laws of large numbers.In this paper,motived by the notion of independent and identically distributed random variables under the sub-linear expectation initiated by Peng(2008),we introduce the concept of negative dependence of random variables and establish Kolmogorov's and Rosenthal's inequalities for the maximum partial sums of negatively dependent random variables under the sub-linear expectations.As an application,we show that Kolmogorov's strong law of larger numbers holds for independent and identically distributed random variables under a continuous sub-linear expectation if and only if the corresponding Choquet integral is finite.

Strong laws of large numbers for sub-linear expectations

We investigate three kinds of strong laws of large numbers for capacities with a new notion of independently and identically distributed(IID) random variables for sub-linear expectations initiated by Peng.It turns out that these theorems are natural and fairly neat extensions of the classical Kolmogorov's strong law of large numbers to the case where probability measures are no longer additive. An important feature of these strong laws of large numbers is to provide a frequentist perspective on capacities.

A strong approximation of self-normalized sums

Let {X,Xn,n1} be a sequence of independent identically distributed random variables with EX=0 and assume that EX2I(|X|≤x) is slowly varying as x→∞,i.e.,X is in the domain of attraction of the normal law.In this paper a Strassen-type strong approximation is established for self-normalized sums of such random variables.

Strong Convergence for Weighted Sums of Negatively Associated Arrays

Let {Xni} be an array of rowwise negatively associated random variables and Tnk=k∑i=1 i^a Xni for a ≥ -1, Snk =∑｜i｜≤k Ф（i/nη）1/nη Xni for η∈（0,1],where Ф is some function. The author studies necessary and sufficient conditions of ∞∑n=1 AnP（max 1≤k≤n｜Tnk｜〉εBn）〈∞ and ∞∑n=1 CnP（max 0≤k≤mn｜Snk｜〉εDn）〈∞ for all ε 〉 0, where An, Bn, Cn and Dn are some positive constants, mn ∈ N with mn /nη →∞. The results of Lanzinger and Stadtmfiller in 2003 are extended from the i.i.d, case to the case of the negatively associated, not necessarily identically distributed random variables. Also, the result of Pruss in 2003 on independent variables reduces to a special case of the present paper; furthermore, the necessity part of his result is complemented.

CHOVER'S LAW OF THE ITERATED LOGARITHM FOR NEGATIVELY ASSOCIATED SEQUENCES

Consider a sequence of negatively associated and identically distributed random variableswith the underlying distribution in the domain of attraction of a stable distribution with an exponentin(0,2).A Chover's law of the iterated logarithm is established for negatively associated randomvariables.Our results generalize and improve those on Chover's law of the iterated logarithm(LIL)type behavior previously obtained by Mikosch(1984),Vasudeva(1984),and Qi and Cheng(1996)fromthe i.i.d,case to NA sequences.

Some Large Deviation Results for Generalized Compound Binomial Risk Models

This paper is a further investigation of large deviation for partial and random sums of random variables, where {Xn,n ≥ 1} is non-negative independent identically distributed random variables with a common heavy-tailed distribution function F on the real line R and finite mean μ∈ R. {N(n),n ≥ 0} is a binomial process with a parameter p ∈ (0,1) and independent of {Xn,n ≥ 1}; {M(n),n ≥ 0} is a Poisson process with intensity λ > 0, Sn = ΣNn i=1 Xi-cM(n). Suppose F ∈ C, we futher extend and improve some large deviation results. These results can apply to certain problems in insurance and finance.

Local Precise Large and Moderate Deviations for Sums of Independent Random Variables

Let {X, X_k : k ≥ 1} be a sequence of independent and identically distributed random variables with a common distribution F. In this paper, the authors establish some results on the local precise large and moderate deviation probabilities for partial sums S_n =sum from i=1 to n(X_i) in a unified form in which X may be a random variable of an arbitrary type,which state that under some suitable conditions, for some constants T > 0, a and τ >1/2and for every fixed γ > 0, the relation P(S_n- na ∈(x, x + T ]) ~nF((x + a, x + a + T ]) holds uniformly for all x ≥γn~τ as n→∞, that is, P(Sn- na ∈(x, x + T ]) lim sup- 1 = 0.n→+∞x≥γnτnF((x + a, x + a + T ])The authors also discuss the case where X has an infinite mean.

Asymptotic Distribution of the Jump Change-Point Estimator

The asymptotic distribution of the change-point estimator in a jump change- point model is considered. For the jump change-point model Xi - α ＋ θ{[nτ0] 〈 i ≤ n} ＋ εi, where εi （i = 1,…. , n） are independent identically distributed random variables with Eεi -= 0 and Var（εi） 〈 ∞, with the help of the slip window method, the asymptotic distribution of the jump change-point estimator τ is studied under the condition of the local alternative hypothesis.

Moderate deviations principle for products of sums of random variables

Let(Xn)n≥1 be a sequence of independent identically distributed(i.i.d.) positive random variables with EX1 = μ,Var(X1) = σ2.In the present paper,we establish the moderate deviations principle for the products of partial sums(πnk=1Sk/n!μn)1/(γbn√(2n))1where γ = σ/μ denotes the coefficient of variation and(bn) is the moderate deviations scale.