Estimating the limits for statistical process control charts: A direct method improving upon the bootstrap

This paper considers the problem of finding limits for a statistical process control (SPC) chart for the process mean, when the process distribution is unknown. The bootstrap method estimates these limits relying on Monte Carlo methods, which are subject to simulation errors. Therefore this paper develops a computationally efficient enumeration method for exact calculations of the control limits.     

A new aspect of a risk process and its statistical inference

We introduce a new aspect of a risk process, which is a macro approximation of the flow of a risk reserve. We assume that the underlying process consists of a Brownian motion plus negative jumps, and that the process is observed at discrete time points. In our context, each jump size of the process does not necessarily correspond to the each claim size. Therefore our risk process is different from the traditional risk process. We cannot directly observe each jump size because of discrete observations. Our goal is to estimate the adjustment coefficient of our risk process from discrete observations.     

A statistical process control approach to selecting a warm-up period for a discrete-event simulation

The selection of a warm-up period for a discrete-event simulation continues to be problematic. A variety of selection methods have been devised, and are briefly reviewed. It is apparent that no one method can be recommended above any other. A new approach, based upon the principles of statistical process control, is described (SPC method). Because simulation output data are often highly autocorrelated and potentially non-normal, the batch means method is employed in constructing the control chart. The SPC method is tested on seven data sets and encouraging results are obtained concerning its accuracy. The approach is also discussed with respect to its ease of implementation, simplicity, generality of use and requirements for parameter estimation.     

The consistency index in reciprocal matrices: Comparison of deterministic and statistical approaches

When checking the inconsistency level of a positive reciprocal matrix Saaty uses a deterministic criterion based on two parameters, a benchmark (the average), and a consistency level, usually 10%. Using results from a simulation experiment with 100,000 positive random reciprocal matrices of size varying from 3 to 15, we developed a probabilistic criterion and compare it to Saaty’s index. We found that if a positive reciprocal matrix is consistent according to the deterministic criterion is also consistent according to the probabilistic criterion only if we accept a higher than usual probability of Type I error. Reducing this error implies that the benchmark must be a small percentile of the probability distribution of the consistency index.     

The evolution of the min-min random graph process

We study the following min-min random graph process G=(G₀,G₁,…): the initial state G₀ is an empty graph on n vertices (n even). Further, G_M+1 is obtained from G_M by choosing a pair {v,w} of distinct vertices of minimum degree uniformly at random among all such pairs in G_M and adding the edge {v,w}. The process may produce multiple edges. We show that G_M is asymptotically almost surely disconnected if M≤n, and that for M=(1+t)n, constant, the probability that G_M is connected increases from 0 to 1. Furthermore, we investigate the number X of vertices outside the giant component of G_M for M=(1+t)n. For constant we derive the precise limiting distribution of X. In addition, for n⁻¹ln⁴n≤t=o(1) we show that tX converges to a gamma distribution.     

On the evaluation of moments for solute transport by random velocity fields

In this paper, we consider the random linear transport equation. We show that standard averaging approaches to obtain an equation for the evolution of the statistical mean of the solution may also be valid for all the statistical moments of the solution. With this result we can obtain more statistical information about the random solution, as illustrated in two particular examples.     

Maxima of random particles scores in Markov branching process with continuous time

We consider supercritical Markov branching processes with continuous time where every particle has one or two random scores. We are interested in maxima of these scores over the population. The class of nondegenerate limit laws for linear normed maxima is described. Limit copulas, upper and lower tail dependence coefficients are obtained for cases of two scores and two time points. Results are illustrated by the computer simulation. The work was partially supported by RFBR grants No. 07-01-00077, No. 07-01-00373.     

On the statistical variation of sequences

We introduce a generalization of the notion of sequence of finite variation, using asymptotic density of sets of positive integers. Some approximation results about approaching any sequence by sequences of finite statistical variation are given.     

Stochastic modeling of quality of systems operating in a heterogeneous environment

We consider systems that are subject to an external mixed Poisson shock process. Each shock can result in a failure of a system with a given probability and is survived with the complementary probability. Each shock additionally decreases the quality function that describes the performance of a system, thus forming the corresponding stochastic process. Expectations (unconditional and conditional on survival) and relevant variability characteristics for the stochastic quality function are derived. Some monotonicity properties of the conditional quality function are investigated and the future values of this function are derived.     

Stochastic modeling of the growth process

In this paper we estimate the upper boundary of the number of trees in the selection stand determined for harvesting in a future. At the same time we showed that the present resource of the number of trees in selection stand is sustained. This is achieved by stochastic modeling of the number of trees and the number of felled trees and by solving the partial differential equation. The same problem is solved in the papers, Mitrović (Stochastic modeling of the number of trees and the number of felled trees in selection stands, YUJOR, vol 14(1), pp 57–64, 2004; Stochastic modeling of the number of felled trees in selection stands, Computational and Applied Mathematics, vol 24(2), pp 285–292, 2005). In this paper the modified mathematical model is represented.      

Mathematical modeling of seismic and acousto-gravitational waves in a heterogeneous earth-atmosphere model

A numerical-analytical solution to problems of seismic and acoustic-gravitational wave propagation is applied to a heterogeneous Earth-Atmosphere model. The seismic wave propagation in an elastic half-space is described by a system of first order dynamic equations of the elasticity theory. The propagation of acoustic-gravitational waves in the atmosphere is described by the linearized Navier-Stokes equations. The algorithm proposed is based on the integral Laguerre transform with respect to time, the finite integral Bessel transform along the radial coordinate with a finite difference solution of the reduced problem along the vertical coordinate. The algorithm is numerically tested for the heterogeneous Earth-Atmosphere model for different source locations.     

随机场熵率的计算和上界估计

提出了用信息论方法计算随机场熵率和估计其上界的新途径,并用此方法计算了若干实例．     

On estimation and control of errors of the Mann iteration process

The purpose of this note is to study the estimation of errors of the Mann iterative process with random errors. It is shown that the accumulative errors in iterative process is bounded and the errors is controllable with some conditions.     

Development and initial psychometric properties of the mathematical modeling attitude scale

This article describes the development, initial validation, and psychometric evaluation of the mathematical modeling attitude scale (MMAS). Specifically, both qualitative and quantitative techniques were used to generate relevant items. The MMAS assesses K–12 teachers’ attitude toward mathematical modeling and examines their experiences with modeling practices. The initial scale development process included the results of items generated from relevant literature, expert reviews, and cognitive interviews to create and assess content validity of MMAS Items. The pilot study phase describes the results of psychometric properties and exploratory factor analysis conducted to assess the usefulness and reliability of the MMAS items measuring teachers’ attitude toward modeling. The scale development process isolated four factors: constructivism, understanding, relevance and real life, and motivation and interest. These four factors explained about 70.2% of the variation in the 28‐item measure. The Cronbach's coefficients alpha for the four factors ranged between 0.81 and 0.95, with an overall scale coefficient of 0.96. The findings presented provide evidence to support the scale's reliability and potential utility for measuring aspects related to teachers’ attitude toward mathematical modeling. Additional results indicated that teachers had limited experience with modeling practices. Discussion, limitations, and implications for teacher education and future research are presented.     

Bayesian spatial modeling of genetic population structure

Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently. While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available, described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data sets illustrate well the benefits of incorporating spatial information to such an analysis.     

Decision making with imprecise probabilities and utilities by means of statistical preference and stochastic dominance

A problem of decision making under uncertainty in which the choice must be made between two sets of alternatives instead of two single ones is considered. A number of choice rules are proposed and their main properties are investigated, focusing particularly on the generalizations of stochastic dominance and statistical preference. The particular cases where imprecision is present in the utilities or in the beliefs associated to two alternatives are considered.     

Renewal analysis of a replacement process

We consider a unit with a random lifetime which is replaced at renewal times by a new identical one regardless of whether it has failed before or not. For this random periodic replacement policy, we derive exact formulas for the cycle length, defined as the time between the replacements of two successive failed units, the stationary probability of the current unit to have failed, and the stationary and the transient distributions of the residual lifetime of the current unit.