Uniform Distribution,Discrepancy, and Reproducing Kernel Hilbert Spaces

In this paper we define a notion of uniform distribution and discrepancy of sequences in an abstract set E through reproducing kernel Hilbert spaces of functions on E. In the case of the finite-dimensional unit cube these discrepancies are very closely related to the worst case error obtained for numerical integration of functions in a reproducing kernel Hilbert space. In the compact case we show that the discrepancy tends to zero if and only if the sequence is uniformly distributed in our sense. Next we prove an existence theorem for such uniformly distributed sequences and investigate the relation to the classical notion of uniform distribution. Some examples conclude this paper.     

A note on the de La Vallée Poussin criterion for uniform integrability

In this paper, we present new versions of the classical de La Vallée Poussin criterion for uniform integrability. Our results concern the uniform integrability of a continuous function relative to a sequence of distribution functions. We apply our results to obtain a result on the convergence of a sequence of integrals which we illustrate with an example.     

Distribution of Global Measures of Deviation Between the Empirical Distribution Function and Its Concave Majorant

We investigate the distribution of some global measures of deviation between the empirical distribution function and its least concave majorant. In the case that the underlying distribution has a strictly decreasing density, we prove asymptotic normality for several L _k -type distances. In the case of a uniform distribution, we also establish their limit distribution together with that of the supremum distance. It turns out that in the uniform case, the measures of deviation are of greater order and their limit distributions are different.     

On Gruber’s problem concerning uniform distribution on the sphere

In this note we give a partial answer to a problem posed by Peter Gruber on the determination of uniform distribution of a sequence of points on the three-dimensional sphere from its appropriate behavior on caps.     

Son of the linear search problem

I wish to find something which is located on a certain road. I start at a point on the road, but I do not know in which direction the object sought is to be found. Somehow, I must incorporate in my way of searching the possibility that it is either to the right or to the left. Thus, I must search first to the right, and then to the left, and then to the right again until it is found. What is a good way of conducting this search, and what is a bad way? This general problem can be phrased in many ways mathematically, some of which are answered in the papers in the bibliography. In this paper, we consider three well-known assumptions concerning thea priori guesses for the probability distribution on where the object is located. These concern uniform distribution on an interval, triangular distribution around the original point, and normal distribution about that point. The uniform distribution has a simple answer. For the triangular distribution, we obtain qualitative results and calculate approximate values for the turning points.     

Kronecker–Halton sequences in Fp((X−1))

In this paper we investigate the distribution properties of hybrid sequences which are made by combining Halton sequences in the ring of polynomials and digital Kronecker sequences. We give a full criterion for the uniform distribution and prove results on the discrepancy of such hybrid sequences.     

球内三维均匀分布区域半径的估计

讨论了球内三维均匀分布区域半径的估计,利用次序统计量得到球形区域半径的估计量和置信区间,并证明了所给置信区间为一定条件下的最短置信区间.     

Limiting distribution of fading evolution in some semi-Markov media

We consider fading evolutions on a line. The limiting distribution of a fading evolution in the case where the driving process has the uniform distribution of sojourn times is obtained. In addition, we study the limiting distributions for stable distributed sojourn times of the driving renewal process.     

Bayesian shrinkage prediction for the regression problem

We consider Bayesian shrinkage predictions for the Normal regression problem under the frequentist Kullback-Leibler risk function.Firstly, we consider the multivariate Normal model with an unknown mean and a known covariance. While the unknown mean is fixed, the covariance of future samples can be different from that of training samples. We show that the Bayesian predictive distribution based on the uniform prior is dominated by that based on a class of priors if the prior distributions for the covariance and future covariance matrices are rotation invariant.Then, we consider a class of priors for the mean parameters depending on the future covariance matrix. With such a prior, we can construct a Bayesian predictive distribution dominating that based on the uniform prior.Lastly, applying this result to the prediction of response variables in the Normal linear regression model, we show that there exists a Bayesian predictive distribution dominating that based on the uniform prior. Minimaxity of these Bayesian predictions follows from these results.     

关于均匀分布区间长度的区间估计

给出了均匀分布区间长度的估计量以及概率密度,并给出了区间长度的区间估计.     

On the probability of a rational outcome for generalized social welfare functions on three alternatives

In Kalai (2002) [10], Kalai investigated the probability of a rational outcome for a generalized social welfare function (GSWF) on three alternatives, when the individual preferences are uniform and independent. In this paper we generalize Kalai's results to a broader class of distributions of the individual preferences, and obtain new lower bounds on the probability of a rational outcome in several classes of GSWFs. In particular, we show that if the GSWF is monotone and balanced and the distribution of the preferences is uniform, then the probability of a rational outcome is at least 3/4, proving a conjecture raised by Kalai. The tools used in the paper are analytic: the Fourier-Walsh expansion of Boolean functions on the discrete cube, properties of the Bonamie-Beckner noise operator, and the FKG inequality.     

Truncated sequential estimation of the parameter of a first order autoregressive process with dependent noises

For a first-order non-explosive autoregressive process with dependent noise, we propose a truncated sequential procedure with a fixed mean-square accuracy. The asymptotic distribution of the estimator depends on the type of the noise distribution: it is normal when the noise has a Kotz’s distribution, while it is a mixture of normal distributions if the noise distribution is a variance mixture of normal distrbutions as well. In both cases, the convergence to the limiting distribution is uniform in the unknown parameter.      

Statistical modeling for discrete patterns in a sequence of exchangeable trials

This paper proposes a new method for constructing a sequence of infinitely exchangeable uniform random variables on the unit interval. For constructing the sequence, we utilize a Pólya urn partially. The resulting exchangeable sequence depends on the initial numbers of balls of the Pólya urn. We also derive the de Finetti measure for the exchangeable sequence. For an arbitrarily given one-dimensional distribution function, we generate sequences of exchangeable random variables with the one-dimensional marginal distribution by transforming the exchangeable uniform sequences with the inverse function of the distribution function. Among them we mainly investigate sequences of exchangeable discrete random variables. They differ from the well-known exchangeable sequence generated only by the Pólya urn scheme. Some examples are also given as applications of the results to exact distributions of some statistics based on sequences of exchangeable trials. Further, from the above exchangeable uniform sequence we construct partial or Markov exchangeable sequences. We also provide numerical examples of statistical inference based on the exchangeable and Markov exchangeable sequences.     

On measures of the distance of a mixture from its parent distribution

In a previous paper in this Journal, Heyde and Leslie [6] examined moment measures of the distance of a mixture from its parent distribution. They confined their attention to the case where the parent distribution is either normal or exponential, and related the moment measures to the more familiar uniform distance between distributions. In this paper we improve on their results by sharpening one of their inequalities. We then use new techniques to extend their investigation to a larger class of parent distributions.     

On a characterization of the uniform distribution by generalized order statistics

In this paper a new variant of the Choquet-Deny theorem is obtained and used to prove a characterization of the uniform distribution based on spacings of generalized order statistics. This result extends two recent characterizations of the uniform distribution.     

Variable block search with prior information

Consider the problem of optimizing an unknown, unimodal, univariate function by direct function evaluation. This article replaces the classic criterion for evaluating search strategies, minimax, by one we feel to be more practical, minimum expected final interval of uncertainty. This latter measure of effectiveness is useful when the experimenter has some prior knowledge of the underlying function and can specify a probability distribution on the location of the optimum.We show here that for a large class of search strategies under the uniform distribution, minimax is equivalent to minimum expected final interval length, i.e., the uniform distribution represents a ‘worst-case’ prior. In addition, computational procedures are develooed for determining the optimal search strategy for arbitrary a priori distributions. Several examples are included.     

区间截断情况下, 分布函数估计及其收敛速度

首先将文[11]的结论推广到任意$k$点均匀分布(k≥3), 然后用k点均匀分布的累积分布函数去逼近连续总体的分布函数, 在适当的条件下, 证明了用区间数据估计出的分布函数收敛速度为O(n)^{^-2/9}.     

Propriety of posterior distribution for dichotomous quantal response models

In this article, we investigate the property of posterior distribution for dichotomous quantal response models using a uniform prior distribution on the regression parameters. Sufficient and necessary conditions for the propriety of the posterior distribution with a general link function are established. In addition, the sufficient conditions for the existence of the posterior moments and the posterior moment generating function are also obtained. Finally, the relationship between the propriety of posterior distribution and the existence of the maximum likelihood estimate is examined.

     

On the Distribution of the Sum of n Non-Identically Distributed Uniform Random Variables

The distribution of the sum of independent identically distributed uniform random variables is well-known. However, it is sometimes necessary to analyze data which have been drawn from different uniform distributions. By inverting the characteristic function, we derive explicit formulae for the distribution of the sum of n non-identically distributed uniform random variables in both the continuous and the discrete case. The results, though involved, have a certain elegance. As examples, we derive from our general formulae some special cases which have appeared in the literature.