带投资的时依更新风险模型中破产概率的一致渐近估计

本文考虑索赔额过程与索赔时间过程具有相依性的更新风险模型.假定保险公司将其盈余投资到金融市场中,该投资的价格过程服从几何L′evy过程.当索赔额分布属于L∩D时,本文得到有限时间总索赔额现值尾概率的一致渐近估计,同时也得到有限时间破产概率的一致渐近估计.     

多项分布最大概率的检验

考察了对多项分布最大概率p_[1]的单边假设检验. 将Ethier (1982)的一个小样本检验推广到了一般情形. 基于一个对p_[1]的估计, 给出了一类大样本检验. 求出了在局部备择假设下以上这些检验的渐近功效. 最后给出了一个实例分析.     

缺失数据下面板数据模型的正交逆概率加权估计

在模型的响应变量部分缺失的情况下,考虑一类带固定效应的面板数据模型的估计问题.通过结合逆概率加权方法和矩阵的QR分解技术,提出了一个基于正交逆概率加权的估计过程.证明了所得估计的相合性和渐近分布等渐近性质,并且通过数值模拟研究了所得估计的有限样本性质.     

常利息力下非标准连续时间更新风险模型破产概率的渐近性态

讨论一个非标准连续时间更新风险模型,其中理赔变量序列为一列两两尾拟渐近独立(TQAI)非负随机变量,在常数利息力假定下,得到了其有限时间破产概率的渐近估计式,并进一步讨论了估计的一致性,推广了[1,2,8]等文献的结果.     

次指数随机变量最大和的大偏差概率及其在有限时间破产概率中的应用

对于由独立同分布的标准均匀分布随机变量中心化的次指数随机变量序列,对于其部分和的最大值 建立了一个大偏差概率的渐近关系.该结果扩展了Korshunov相应的结论. 作为应用, 将Tang的结果,即关于有限时间破产概率的一致渐近估计,由一致变化分布族推广到了整个强次指数族.     

多源数据融合视角下非概率样本与概率样本的大量插补推断方法

随着社会的发展,概率样本无回答率越来越高,其目标变量可能存在缺失的情况.同时,大数据与网络调查的发展使得获得的样本大多数是非概率样本,如何结合这两种样本推断总体是当今时代多源数据融合领域的一个热点问题.假设存在目标变量完全缺失的概率样本和数据完整的非概率样本,提出基于非概率样本建立超总体局部多项式模型,插补概率样本缺失的目标变量,并利用插补后的概率样本估计总体,进一步证明提出估计的渐近性质.模拟和实证研究表明:与基于非概率样本的倾向得分逆加权估计相比,提出估计的绝对相对偏差,方差与均方误差更小,且与基于真实概率样本的总体估计相接近;提出总体均值估计的方差估计的绝对相对偏差与95%置信区间覆盖率也接近于基于真实概率样本的总体估计的相应指标,估计效果较好.     

变利率风险模型下破产概率的渐近估计

本文考虑变利率的离散时间风险模型的破产概率．在个体净损失服从ERV族和DnL族时,分别得到了有限时间和无限时间破产概率的渐近估计及上下界表达式,并利用matlab软件对有限时间破产概率的下界进行了数值模拟．     

各向异性Besov-Wiener类的平均宽度

得到了各向异性Besov Wiener类S^r_pqθb(R^d)和S^r_pqθB((R^d))在L_q(R^d) ( 1≤q≤p <∞ )内及其对偶情形的平均σ- K宽度和平均σ- L宽度的弱渐近估计 .     

k近邻判别法后验错误概率的指数限

本文对k>1的情况证明了:k近邻判别的后验错误概率L_n在一定条件下,仍以概率1收敛于其无条件错误概率的渐近值R_k,且P(|L_n-R_k|≥ε)有形如O(exp(-cn^1/3))的指数限。k=1的情况已在文献[1]中解决,且上述表达式中的1/3可以用1/2代替。     

C半群的概率逼近问题

借助Pettis积分、随机过程、矩生成函数及算子值数学期望,给出了一般形式的C半群概率逼近指数公式、生成定理及其收敛速度的估计式,也从另一个角度得出C半群概率表示的Vonorovskaya型渐近公式.     

The Finite-time Ruin Probability for the Jump-Diffusion Model with Constant Interest Force

In this paper, we consider the finite time ruin probability for the jump-diffusion Poisson process. Under the assurnptions that the claimsizes are subexponentially distributed and that the interest force is constant, we obtain an asymptotic formula for the finite-time ruin probability. The results we obtain extends the corresponding results of Kliippelberg and Stadtmüller and Tang.     

Probability that the commutator of two group elements is equal to a given element

In this paper we study the probability that the commutator of two randomly chosen elements in a finite group is equal to a given element of that group. Explicit computations are obtained for groups G which |G^′| is prime and G^′≤Z(G) as well as for groups G which |G^′| is prime and G^′∩Z(G)=1. This paper extends results of Rusin [see D.J. Rusin, What is the probability that two elements of a finite group commute? Pacific J. Math. 82 (1) (1979) 237-247].     

Rare Event Probability Estimation in the Presence of Epistemic Uncertainty on Input Probability Distribution Parameters

The accurate estimation of rare event probabilities is a crucial problem in engineering to characterize the reliability of complex systems. Several methods such as Importance Sampling or Importance Splitting have been proposed to perform the estimation of such events more accurately (i.e., with a lower variance) than crude Monte Carlo method. However, these methods assume that the probability distributions of the input variables are exactly defined (e.g., mean and covariance matrix perfectly known if the input variables are defined through Gaussian laws) and are not able to determine the impact of a change in the input distribution parameters on the probability of interest. The problem considered in this paper is the propagation of the input distribution parameter uncertainty defined by intervals to the rare event probability. This problem induces intricate optimization and numerous probability estimations in order to determine the upper and lower bounds of the probability estimate. The calculation of these bounds is often numerically intractable for rare event probability (say 10^?5), due to the high computational cost required. A new methodology is proposed to solve this problem with a reduced simulation budget, using the adaptive Importance Sampling. To this end, a method for estimating the Importance Sampling optimal auxiliary distribution is proposed, based on preceding Importance Sampling estimations. Furthermore, a Kriging-based adaptive Importance Sampling is used in order to minimize the number of evaluations of the computationally expensive simulation code. To determine the bounds of the probability estimate, an evolutionary algorithm is employed. This algorithm has been selected to deal with noisy problems since the Importance Sampling probability estimate is a random variable. The efficiency of the proposed approach, in terms of accuracy of the found results and computational cost, is assessed on academic and engineering test cases.     

Partly Divisible Probability Measures on Locally Compact Abelian Groups

A notion of admissible probability measures μ on a locally compact Abelian group (LCA ‐ group) G with connected dual group Ĝ = ℝ^d × 𝕋ⁿ is defined. To such a measure μ, a closed semigroup Λ(μ) ⊆ (0, ∞) can be associated, such that, for t ∈ Λ(μ), the Fourier transform to the power t, (μˆ)^t, is a characteristic function. We prove that the existence of roots for non admissible probability measures underlies some restrictions, which do not hold in the admissible case. As we show for the example ℤ₂, in the case of LCA ‐ groups with non connected dual group, there is no canonical definition of the set Λ(μ).     

Geometric Inference for Probability Measures

Data often comes in the form of a point cloud sampled from an unknown compact subset of Euclidean space. The general goal of geometric inference is then to recover geometric and topological features (e.g., Betti numbers, normals) of this subset from the approximating point cloud data. It appears that the study of distance functions allows one to address many of these questions successfully. However, one of the main limitations of this framework is that it does not cope well with outliers or with background noise. In this paper, we show how to extend the framework of distance functions to overcome this problem. Replacing compact subsets by measures, we introduce a notion of distance function to a probability distribution in ℝ ^d . These functions share many properties with classical distance functions, which make them suitable for inference purposes. In particular, by considering appropriate level sets of these distance functions, we show that it is possible to reconstruct offsets of sampled shapes with topological guarantees even in the presence of outliers. Moreover, in settings where empirical measures are considered, these functions can be easily evaluated, making them of particular practical interest.     

Explicit Bounds for the Return Probability of Simple Random Walks

We give an exact computation of the second order term in the asymptotic expansion of the return probability, P_2n^d(0,0), of a simple random walk on the d-dimensional cubic lattice. We also give an explicit bound on the remainder. In particular, we show that P_2n^d(0,0) < 2 (d/4n)^d/2 where n M=M(d) is explicitly given.     

Uniquely Determined Uniform Probability on the Natural Numbers

In this paper, we address the problem of constructing a uniform probability measure on \({\mathbb {N}}\). Of course, this is not possible within the bounds of the Kolmogorov axioms, and we have to violate at least one axiom. We define a probability measure as a finitely additive measure assigning probability 1 to the whole space, on a domain which is closed under complements and finite disjoint unions. We introduce and motivate a notion of uniformity which we call weak thinnability, which is strictly stronger than extension of natural density. We construct a weakly thinnable probability measure, and we show that on its domain, which contains sets without natural density, probability is uniquely determined by weak thinnability. In this sense, we can assign uniform probabilities in a canonical way. We generalize this result to uniform probability measures on other metric spaces, including \({\mathbb {R}}^n\).     

A Chaotic Continuous Map Generates All Probability Distributions

Let ƒ be a continuous map of the compact unit interval I = [0, 1], such that ƒ², the second iterate of ƒ, is topologically transitive in I. If for some x and y in I and any t in I there exists lim(1/n) # {i ≤ n; |ƒⁱ(x) − ƒⁱ(y)| < t} for n → ∞, denote it by φ_xy(t). In the paper we consider the class (ƒ) if all φ_xy. The main results are that (ƒ) is convex and pointwise closed. Using this we show that (ƒ) is always bigger than the class (ƒ) of probability distributions generated analogously by single trajectories (and corresponding to the class of probability invariant measures of ƒ), and prove that there are universal generators of probability distributions, i.e., maps ƒ such that (ƒ) is the class of all non-decreasing functions I I (contrary to this, (ƒ) for no ƒ). These results can be extended to more general continuous maps. One of the possible applications is to use the size of (ƒ) as a measure of the degree of chaos of ƒ.     

Non-Archimedean Probability

We propose an alternative approach to probability theory closely related to the framework of numerosity theory: non-Archimedean probability (NAP). In our approach, unlike in classical probability theory, all subsets of an infinite sample space are measurable and only the empty set gets assigned probability zero (in other words: the probability functions are regular). We use a non-Archimedean field as the range of the probability function. As a result, the property of countable additivity in Kolmogorov’s axiomatization of probability is replaced by a different type of infinite additivity.     

Probability Theory of Random Polygons from the Quaternionic Viewpoint

We build a new probability measure on closed space and plane polygons. The key construction is a map, given by Hausmann and Knutson, using the Hopf map on quaternions from the complex Stiefel manifold of 2‐frames in n‐space to the space of closed n‐gons in 3‐space of total length 2. Our probability measure on polygon space is defined by pushing forward Haar measure on the Stiefel manifold by this map. A similar construction yields a probability measure on plane polygons that comes from a real Stiefel manifold. The edgelengths of polygons sampled according to our measures obey beta distributions. This makes our polygon measures different from those usually studied, which have Gaussian or fixed edgelengths. One advantage of our measures is that we can explicitly compute expectations and moments for chord lengths and radii of gyration. Another is that direct sampling according to our measures is fast (linear in the number of edges) and easy to code. Some of our methods will be of independent interest in studying other probability measures on polygon spaces. We define an edge set ensemble (ESE) to be the set of polygons created by rearranging a given set of n edges. A key theorem gives a formula for the average over an ESE of the squared lengths of chords skipping k vertices in terms of k, n, and the edgelengths of the ensemble. This allows one to easily compute expected values of squared chord lengths and radii of gyration for any probability measure on polygon space invariant under rearrangements of edges. © 2014 Wiley Periodicals, Inc.