Limit theorems for a general weighted process under random censoring

Necessary and sufficient conditions for weak and strong convergence are derived for the weighted version of a general process under random censoring. To be more explicit, this means that for this process complete analogues are obtained of the Chibisov-O'Reilly theorem, the Lai-Wellner Glivenko-Cantelli theorem, and the James law of the iterated logarithm for the empirical process. The process contains as special cases the so-called basic martingale, the empirical cumulative hazard process, and the product-limit process. As a tool we derive a Kiefer-process-type approximation of our process, which may be of independent interest.     

Discrete distributions with maximum expected value of the maximum

We give an upper bound for the expected value of the largest order statistic of a simple random sample of size n from a discrete distribution on N points. We also characterize the distributions that attain such bound. In the particular case n=2, we obtain a characterization of the discrete uniform distribution. © 1998 Elsevier Science B.V. All rights reserved.     

The number of records within a random interval of the current record value

LetX ₁,X ₂, … be a sequence of i.i.d. random variables with some continuous distribution functionF. LetX(n) be then-th record value associated with this sequence and μ _n ⁻ , μ _n ⁺ be the variables that count the number of record values belonging to the random intervals(f−(X(n)), X(n)), (X(n), f+(X(n))), wheref−, f+ are two continuous functions satisfyingf−(x)<x, f+(x)>x. Properties of μ _n ⁻ , μ _n ⁺ are studied in the present paper. Some statistical applications connected with these variables are also provided.     

Probability of ties and Markov property in discrete order statistics

It is well known that, in the continuous case, the probability that two consecutive order statistics are equal to zero, whereas it is not true when the distribution is discrete. It is, perhaps, for this reason that order statistics from discrete distributions has not been investigated in the literature as much as from a continuous distribution. The main purpose of this paper, therefore, is to obtain the probability of ties when the distribution is discrete. Also it is shown that, in the discrete case, the Markov property does not hold good. However, the order statistics from a geometric distribution forms a Markov chain.     

On the number of near-records after the maximum observation in a continuous sample

{X_n, n≥1} are independent and identically distributed random variables with continuous distribution function F(x). For j=1,…,n, X_j is called a near-record up to time n if X_j ∈ (M_na, M_n], where M_n = max_1≤j≤n {X_j} and a is a positive constant. Let Z_n(a) denote the number of near-records after, and including the maximum observation of the sequence. In this paper, the distributional results of Z_n(a) are considered and its asymptotic behaviours are studied.     

Linearity of regression for the past weak and ordinary records

In this paper we study the property of linearity of backward regression for non-adjacent records. In the case of weak records, a characterization of the geometric distribution is obtained. It also appears that a related characterization for ordinary records does not hold, showing the difference in conditional behaviour between weak and ordinary records.     

Limit theorems for random maximum of independent and non-identically distributed random vectors

In this paper, we study the weak convergence of the random maximum of independent and non-identical random vectors. When the random sample size is assumed to be independent of the basic variables and its distribution function is assumed to converge weakly to a non-degenerate limit, the necessary and sufficient conditions for the weak convergence of the random maximum are derived. An illustrative example is given.     

Central Limit Theorem approximations for the number of runs in Markov-dependent binary sequences

We consider Markov-dependent binary sequences and study various types of success runs (overlapping, non-overlapping, exact, etc.) by examining additive functionals based on state visits and transitions in an appropriate Markov chain. We establish a multivariate Central Limit Theorem for the number of these types of runs and obtain its covariance matrix by means of the recurrent potential matrix of the Markov chain. Explicit expressions for the covariance matrix are given in the Bernoulli and a simple Markov-dependent case by expressing the recurrent potential matrix in terms of the stationary distribution and the mean transition times in the chain. We also obtain a multivariate Central Limit Theorem for the joint number of non-overlapping runs of various sizes and give its covariance matrix in explicit form for Markov dependent trials.     

Asymptotic properties of numbers of observations in random regions determined by central order statistics

In this paper, we extend the concept of near order statistic observation by considering observations that fall into a random region determined by a given order statistic and a Borel set. We study asymptotic properties of numbers of such observations as the sample size tends to infinity and the order statistic is a central one. We show that then proportions of these numbers converge in probability to some population probabilities. We also prove that these numbers can be centered and normalized to yield normal limit law. First, we derive results for one order statistic; next we give extensions to the multivariate case of two or more order statistics.     

A bayesian alternative to the sign test in the presence of ties

Bayesian alternatives to the sign test are proposed which incorporate the number of ties observed. These alternatives arise from different strategies in dealing with the number of ties. One strategy is incorporating the true proportion of ties into the hypotheses of interest. The Bayesian methods are compared to each other and to the typical sign test in a simulation study. Also, the new methods are compared to another version of the sign test proposed by Coakley and Heise (1996). This new version of the sign test was shown to perform especially well in situations where the probability of observing a tie is very high. Although one of the Bayesian methods appears to perform best overall in the simulation study, its performance is not dominating and the easy to use typical sign test generally performs very well.     

Weak representation of the cumulative hazard function under semiparametric random censorship models

In this paper we derive a weak representation of the semiparametric estimator A^se _nof the cumulative hazard function A in the random censorship model. Based on this representation we show that |A^se _n- A| is uniformly bounded in probability up to the last order statistic of the observations.     

Limit Theorems for the Counting Process of Near-Records

Near-records of a sequence, as defined in Balakrishnan et al. (2005 Balakrishnan , N. , Pakes , A. G. , Stepanov , A. ( 2005 ). On the number and sum of near-record observations . Advances in Applied Probability 37 : 765 – 780 .[Crossref], [Web of Science ®] , [Google Scholar]), are observations lying within a fixed distance of the current record. In this article we study the asymptotic behavior of the number of near-records, among the first n observations in a sequence of independent, identically distributed and absolutely continuous random variables. We give conditions for the finiteness of the total number of near-records as well as laws of large numbers for their counting process. For distributions with a finite number of near-records, we carry out a simulation study suggesting that the total number of near-records has a geometric distribution.     

Non-parametric testing for the number of change points in a sequence of independent random variables

A non-parametric procedure is derived for testing for the number of change points in a sequence of independent continuously distributed variables when there is no prior information available. The procedure is based on the Kruskal–Wallis test, which is maximized as a function of all possible places of the change points. The procedure consists of a sequence of non-parametric tests of nested hypotheses corresponding to a decreasing number of change points. The properties of this procedure are analyzed by Monte Carlo methods and compared to a parametric procedure for the case that the variables are exponentially distributed. The critical values are given for sample sizes up to 200.     

Bounds on the mean of the maximum of a number of normal random variables

A simple method producing lower and upper bounds on E max(X₁,...,X_n) is presented under assumption that the X_i's are independent normal random variables. Furthermore the upper bounds are determined when the X_i's are normal and positively correlated     

A discrete model for the segmentation of chains and strings

A statistical model is presented that can be used to represent a discrete breakage process. It considers a fixed length chain or string made up of n +1 pieces joined together. The chain is stressed at each connection or link andrupture occurs at some of the links. Models are developed to answer the question, “what is the expected proportion of chain segments of a given size?” The model is modified to handle those experiments where only thetotal weight of a given size segment is known. Expressions are obtained for the expected value and variance of the number of segments of a given size. The model is used to predict the expected number of segments which results when a fixed length DNA molecule chain is subjected to carcinogenic agents and is applied to industrial examples.     

Limit distributions of order statistics with random indices in a stationary Gaussian sequence

In this article, we study the limit distributions of the extreme, intermediate, and central order statistics (os) of a stationary Gaussian sequence under equi-correlated setup. When the random sample size is assumed to converge weakly and to be independent of the basic variables, the sufficient (and in some cases the necessary) conditions for the convergence are derived. Finally, we show that the obtained result for the maximum os, with random sample size, is also applicable in the case of the non constant correlation case.     

Asymptotic normality of numbers of observations in random regions determined by order statistics

In this paper, we study the joint limiting behaviour of numbers of observations that fall into regions determined by order statistics and Borel sets. We show that suitably centred and normed versions of these numbers are asymptotically multivariate normal under some conditions. We consider two cases: one where the population distribution function is discontinuous and the other where it is continuous and the order statistics are extreme. Finally, we compare results obtained for the two cases with their analogues for absolutely continuous distribution function and central-order statistics.