Statistically D-bounded sequences in probabilistic normed spaces

In this study, the concept of a statistically D-bounded sequence in a probabilistic normed (PN) space endowed with the strong topology is introduced and its basic properties are investigated. It is shown that a strongly statistically convergent sequence and a strong statistically Cauchy sequence are statistically D-bounded under certain conditions. A sequence which goes far away from the limit point infinitely many times and presents random deviations in a PN space may be handled with the tools of strong statistical convergence and statistical D-boundedness.     

Statistical Cluster Points of Sequences in Finite Dimensional Spaces

In this paper we study the set of statistical cluster points of sequences in m-dimensional spaces. We show that some properties of the set of statistical cluster points of the real number sequences remain in force for the sequences in m-dimensional spaces too. We also define a notion of -statistical convergence. A sequence xis -statistically convergent to a set Cif Cis a minimal closed set such that for every > 0 the set has density zero. It is shown that every statistically bounded sequence is -statistically convergent. Moreover if a sequence is -statistically convergent then the limit set is a set of statistical cluster points.     

Local rotundity structure of Cesàro-Orlicz sequence spaces

Some criteria for extreme points and strong U-points in Cesàro-Orlicz spaces are given. In consequence we find a Cesàro-Orlicz sequence space different from c₀ which has no extreme points. Some examples show that in these spaces the notion of the strong U-point is essentially stronger than the notion of the extreme point. Various examples presented in this paper show that there are some differences between criteria for extreme points and strong U-points in Orlicz spaces and in Cesàro-Orlicz spaces. We also show that the uniqueness of the local best approximation needs the notion of SU-point, that is, the notion of the extreme point is not strong enough here.     

Some Research on Shannon–McMillan Theorem for mth-Order Nonhomogeneous Markov Information Source

Abstract

In this article, a class of strong limit theorems for relative entropy density of arbitrary stochastic sequence, expressed by inequalities, are obtained by comparing arbitrary dependent distribution with and the mth-order Markov distribution on probability space. As corollaries, some Shannon–McMillan theorems of mth-order nonhomogeneous Markov information source are obtained. Some results of nonhomogeneous Markov information source obtained are generalized.     

Weak and Strong Convergence Theorems for Maximal Monotone Operators in a Banach Space

In this paper, we introduce an iterative sequence for finding a solution of a maximal monotone operator in a uniformly convex Banach space. Then we first prove a strong convergence theorem, using the notion of generalized projection. Assuming that the duality mapping is weakly sequentially continuous, we next prove a weak convergence theorem, which extends the previous results of Rockafellar [SIAM J. Control Optim. 14 (1976), 877–898] and Kamimura and Takahashi [J. Approx. Theory 106 (2000), 226–240]. Finally, we apply our convergence theorem to the convex minimization problem and the variational inequality problem.     

Strong ideal convergence in probabilistic metric spaces

In the present paper we introduce the concepts of strongly ideal convergent sequence and strong ideal Cauchy sequence in a probabilistic metric (PM) space endowed with the strong topology, and establish some basic facts. Next, we define the strong ideal limit points and the strong ideal cluster points of a sequence in this space and investigate some properties of these concepts.     

A Random Set Extension of a Strong Law of Large Numbers of Cantrell and Rosalsky

Abstract

A strong law of large numbers under conditions irrespective of the joint distribution of the sequence is extended to random sets. The extension is such that the role of events of the form {||V _n || ≤ b _n } (where V _n is a random element of a separable Banach space) is played by events of the form {X _n  ? B _n } (where X _n is a random closed bounded set).     

Some difference sequence spaces defined by a sequence of <Emphasis Type="Italic">ϕ</Emphasis> -functions

Abstract  In this paper, we introduce new difference sequence spaces combining with de la Vallee-Poussin mean using by a sequence of modulus functions and ϕ -functions. We also studied connections between statistically convergence related with this space. Keywords Difference sequence, Modulus function, ϕ -function, De la Vallee-Poussin means, Statistical convergence Mathematics Subject Classification (2000) 46A45, 40F05, 46A80     

The asymptotic equipartition property of Markov chains in single infinite Markovian environment on countable state space

ABSTRACT

The asymptotic equipartition property is a basic theorem in information theory. In this paper, we study the strong law of large numbers of Markov chains in single-infinite Markovian environment on countable state space. As corollary, we obtain the strong laws of large numbers for the frequencies of occurrence of states and ordered couples of states for this process. Finally, we give the asymptotic equipartition property of Markov chains in single-infinite Markovian environment on countable state space.     

TVS-锥度量空间中的统计收敛

本文研究TVS-锥度量空间中的统计收敛以及TVS-锥度量空间的统计完备性.令（X，E，P，d）表示一个TVS-锥度量空间.利用定义在有序Hausdorff拓扑向量空间E上的Minkowski函数ρ，证明了在X上存在一个通常意义下的度量d_ρ，使得X中的序列（x_n）在锥度量d意义下统计收敛到x ∈ X，当且仅当（x_n）在度量d_ρ意义下统计收敛到x.基于此，我们证明了任意一个TVS-锥统计Cauchy序列是几乎处处TVS-锥Cauchy序列，还证明了任意一个TVS-锥统计收敛的序列是几乎处处TVS-锥收敛的.从而，TVS-锥度量空间（X，d）是d-完备的，当且仅当它是d-统计完备的.基于以上结论，通常度量空间中统计收敛的许多性质都可以平行地推广到锥度量空间中统计收敛的情形.     

Weighted Statistical Relative Invariant Mean in Modular Function Spaces with Related approximation Results

Abstract

The idea of statistical relative convergence on modular spaces has been introduced by Orhan and Demirci. The notion of σ-statistical convergence was introduced by Mursaleen and Edely and further extended based on a fractional order difference operator by Kadak. The concern of this paper is to define two new summability methods for double sequences by combining the concepts of statistical relative convergence and σ-statistical convergence in modular spaces. Furthermore, we give some inclusion relations involving the newly proposed methods and present an illustrative example to show that our methods are nontrivial generalizations of the existing results in the literature. We also prove a Korovkin-type approximation theorem and estimate the rate of convergence by means of the modulus of continuity. Finally, using the bivariate type of Stancu-Schurer-Kantorovich operators, we display an example such that our approximation results are more powerful than the classical, statistical, and relative modular cases of Korovkin-type approximation theorems.     

Normal forms for singularities of pedal curves produced by non-singular dual curve germs in <Emphasis Type="Italic">S</Emphasis><Superscript><Emphasis Type="Italic">n</Emphasis></Superscript>

For an n-dimensional spherical unit speed curve r and a given point P, we can define naturally the pedal curve of r relative to the pedal point P. When the dual curve germs are non-singular, singularity types of such pedal curves depend only on locations of pedal points. In this paper, we give a complete list of normal forms for singularities and locations of pedal points when the dual curve germs are non-singular. As an application of our list, we characterize C ^∞ left equivalence classes of pedal curve germs (I, s ₀) → S ⁿ produced by non-singular dual curve germ from the viewpoint of the relation between tangent space and tangent space.      

SMOOTHNESS PROPERTIES OF REAL-VALUED FUNCTIONS BASED ON THEIR OSCILLATION

Abstract

We consider the following two selection principles for topological spaces:

Principle 1: For each sequence of dense subsets, there is a sequence of points from the space, the n-th point coming from the n-th dense set, such that this set of points is dense in the space;

Principle 2: For each sequence of dense subsets, there is a sequence of finite sets, the n-th a subset of the n-th dense set, such that the union of these finite sets is dense in the space.

We show that for separable metric space X one of these principles holds for the space C_p (X) of realvalued continuous functions equipped with the pointwise convergence topology if, and only if, a corresponding principle holds for a special family of open covers of X. An example is given to show that these equivalences do not hold in general for Tychonoff spaces. It is further shown that these two principles give characterizations for two popular cardinal numbers, and that these two principles are intimately related to an infinite game that was studied by Berner and Juhász.     

Polynomials hardly commuting with increasing bijections

Let I be an interval in the real line ℝ. Among the real polynomials that take I to I, we ask which ones do not commute with any increasing bijection of I other than identity. For this purely algebraic problem, the solution involves concepts in topological dynamics. Our main characterizations are in terms of full orbits of critical points and periodic points. Using these, we obtain simpler criterion, namely, that for no nontrivial subinterval K⊂I, the successive images {f ⁿ (K):n=0,1,2,…} form a pairwise disjoint collection. This problem is of interest in topological dynamics because it is about characterization of polynomials with unique self-topological-conjugacy.     

On ideal convergence in probabilistic normed spaces

An interesting generalization of statistical convergence is I-convergence which was introduced by P.Kostyrko et al [KOSTYRKO,P.—ŠALáT,T.—WILCZYŃSKI,W.: I-Convergence, Real Anal. Exchange 26 (2000–2001), 669–686]. In this paper, we define and study the concept of I-convergence, I*-convergence, I-limit points and I-cluster points in probabilistic normed space. We discuss the relationship between I-convergence and I*-convergence, i.e. we show that I*-convergence implies the I-convergence in probabilistic normed space. Furthermore, we have also demonstrated through an example that, in general, I-convergence does not imply I*-convergence in probabilistic normed space.     

On statistical limit points of double sequences

We investigate the structure of the set of all statistical limit points of a double sequence and prove certain results, mainly showing that this set can be characterized as a F_σ-set.     

Strong Stability of Queues with Multiple Vacation of the Server

Abstract

The main purpose of this article is to use the strong stability method to approximate the characteristics of the M/G/1//N queue with server vacation by those of the classical M/G/1//N queue, when the rate of the vacations is sufficiently small. This last queue is simpler and more exploitable in practice. For this, we proof the stability conditions and next obtain quantitative stability estimates with an exact computation of constants. From these theoretical results, we can develop an algorithm in order to check the conditions of approximation. These results of approximation have a great practical and economic interest in reliability systems and maintenance optimization policy, when we consider elements with constant failure rate.     

On Almost Convergent and Statistically Convergent Subsequences

There are two well-known non-matrix summability methods which we will consider, namely “almost convergence” and “statistical convergence”. The results presented in this paper will be of two types, dealing with Lebesgue measure and Baire category. Establishing a one-to-one correspondence between the interval (0; 1] and the collection of all subsequences of a given sequence s = (s _n), we will examine the measure and category of the set of all almost convergent subsequences of (s _n). Similar questions for statistical and lacunary statistical convergence are considered. Results on rearrangements of sequences are also presented. This revised version was published online in June 2006 with corrections to the Cover Date.     

Lacunary statistically convergent double sequences in probabilistic normed spaces

In this paper, we introduce the concepts of double lacunary statistically convergent and double lacunary statistically Cauchy sequences in probabilistic normed spaces. We have also demonstrated through an example how to check the lacunary statistical convergence of a sequence in probabilistic normed space.     

Advanced topology on the multiscale sequence spaces

We pursue the study of the multiscale spaces S^ν introduced by Jaffard in the context of multifractal analysis. We give the necessary and sufficient condition for S^ν to be locally p-convex, and exhibit a sequence of p-norms that defines its natural topology. The strong topological dual of S^ν is identified to another sequence space depending on ν, endowed with an inductive limit topology. As a particular case, we describe the dual of a countable intersection of Besov spaces.