The asymptotic variance of a time average in a birth-death process

The variance of a time average is important for planning, running and interpreting experiments. This paper derives a simple method to find this variance for the case of a Markov process. This method is then applied to obtain the variance of a time average for the case of a birth-death process.     

Mean and variance of first passage time of non-homogeneous random walk

The prime concern of this paper is the first passage time of a non-homogeneous random walk, which is nearest neighbor but able to stay at its position. It is revealed that the branching structure of the walk corresponds to a 2-type non-homogeneous branching process and the first passage time of the walk can be expressed by that branching process. Therefore, one can calculate the mean and variance of the first passage time, though its exact distribution is unknown.     

Minimizing flow time variance in a single machine system using genetic algorithms

In this paper, we address an n-job, single machine scheduling problem with an objective to minimize the flow time variance. We propose heuristic procedure based on genetic algorithms with the potential to address more generalized objective function such as weighted flow time variance. The development and implementation of the algorithm is supported with literature review and statistical analysis of the results. Some general guidelines to select the parameter values of the genetic algorithm are also developed using an experimental design approach.     

中国试点碳排放权交易市场有效性分析

为应对气候变化问题,《京都议定书》等文件催生了以CO₂排放权作为商品的碳交易市场的建立。研究中国试点碳排放权交易市场的有效性对我国统一碳市场的建设以及碳中和目标的实现具有重要的指导意义。本文选取有效交易日数据,对中国试点碳市场的有效性进行探索性研究,结合有效市场假说理论与分形市场假说理论,分别采用游程检验法、方差比检验法以及重标极差分析法对中国碳市场有效性进行综合分析,同时探讨各方法导致计算结果不一致的原因,并利用GARCH模型进行检验。研究表明:中国试点碳市场未能达到弱式有效水平。同时,根据中国目前碳市场运行现状给出一定的建议。     

On the cusum of squares test for variance change in nonstationary and nonparametric time series models

In this paper we consider the problem of testing for a variance change in nonstationary and nonparametric time series models. The models under consideration are the unstable AR(q) model and the fixed design nonparametric regression model with a strong mixing error process. In order to perform a test, we employ the cusum of squares test introduced by Inclán and Tiao (1994,J. Amer. Statist. Assoc.,89, 913–923). It is shown that the limiting distribution of the test statistic is the sup of a standard Brownian bridge as seen in iid random samples. Simulation results are provided for illustration.     

Simultaneous optimal estimates of fixed effects and variance components in the mixed model

For a general linear mixed model with two variance components, a set of simple conditions is obtained, under which, (i) the least squares estimate of the fixed effects and the analysis of variance (ANOVA) estimates of variance components are proved to be uniformly minimum variance unbiased estimates simultaneously; (ii) the exact confidence intervals of the fixed effects and uniformly optimal unbiased tests on variance components are given; (iii) the exact probability expression of ANOVA estimates of variance components taking negative value is obtained.     

Managerial and economic impacts of reducing delivery variance in the supply chain

This paper addresses the managerial and economic impacts of improving delivery performance in a serial supply chain when delivery performance is evaluated with respect to a delivery window. Building on contemporary management theories that advocate variance reduction as the critical step in improving the overall performance of a system, we model the variance of delivery time to the final customer as a function of the investment to reduce delivery variance and the costs associated with untimely delivery (expected earliness and lateness). A logarithmic investment function is used and the model solution involves the minimization of a convex–concave total cost function. A numerical example is provided to illustrate the model and the solution procedure. The model presented provides guidelines for determining the optimal level of financial investment for reducing delivery variance. The managerial implications as well as the economic aspects of delivery variance reduction in supply chain management are discussed.     

THE INEFFICIENCY OF LEASTSQUARES IN GAUSS-MARKOV ANDVARIANCE COMPONENT MODELS

1.IntroductionThispaperwillusethefollowingnotations.ForanymatrixA,A andW(A)denotetheMoors-PenroseinverseandthecolumnspaceofA,respectively.ForanysquarematrixA,IAImeansthedeterminantofA,A20(A>0)meansAisnonnegative(positive)definite.ConsidertheGauss-MarkovmodelY=Xo e,E(e)=0,Coy(e)~a'E,(1.1)whereYisann-dimensionalobservablerandomvector,XisannxpknownmatrixwithcolUInn-fUllrank,Tisapdimensionalparametervector,eisann-dimensionalunobserVablerandomvector,aZisanunknownpositiveparameter,Z20.I…     

Parameter estimation in systems of differential equations,based on three-way data arrays and random time changes

Consider a system of n₁ × n₂ differential equations depending on a vector θ of unknown parameters. We suggest an iterative estimation procedure for θ, based on a three-way array of observations. The method involves random time changes driven by multidimensional integrated Ornstein-Uhlenbeck processes.     

Investigation and reduction of variance of a weighted estimate in numerical statistical simulation

The efficiency of the “value” and “partial value” modeling related to the construction of a modeling distribution for an auxiliary random variable by multiplying the initial density by a value function is investigated. The value function usually corresponds to a solution of the adjoint equation. Conditions under which the value modeling of the initial distribution reduces the variance compared to the direct simulation are obtained. It is proved that the variance of the weighted estimate is bounded in the case of the partial value modeling. This proposition provides a basis for a method for determining whether or not the variance of the weighted estimate is bounded. This method uses the majorizing adjoint equation. Using a practically important problem in transport theory as an example, the asymptotic optimization of the distribution of the mean free path is presented. The application of the proposed method of the investigation of the variance boundedness for the analysis of the classical exponential transformation method of simulating the mean free path of a particle in the one-dimensional and the spherical variants is discussed.     

Hypersurfaces in and the variance of exit times for Brownian motion

Using the first exit time for Brownian motion from a smoothly bounded domain in Euclidean space, we define two natural functionals on the space of embedded, compact, oriented, unparametrized hypersurfaces in Euclidean space. We develop explicit formulas for the first variation of each of the functionals and characterize the critical points.

     

Mean,variance, and probabilistic criteria in finite Markov decision processes: A review

This paper is a survey of papers which make use of nonstandard Markov decision process criteria (i.e., those which do not seek simply to optimize expected returns per unit time or expected discounted return). It covers infinite-horizon nondiscounted formulations, infinite-horizon discounted formulations, and finite-horizon formulations. For problem formulations in terms solely of the probabilities of being in each state and taking each action, policy equivalence results are given which allow policies to be restricted to the class of Markov policies or to the randomizations of deterministic Markov policies. For problems which cannot be stated in such terms, in terms of the primitive state setI, formulations involving a redefinition of the states are examined.The author would like to thank two referees for a very thorough and helpful referceing of the original article and for the extra references (Refs. 47–52) now added to the original reference list.     

Comparative statics of changes in risk on monotonically and partially responsive kinked payoffs

This paper derives two sets of necessary and sufficient conditions on the comparative statics of changes in risk under kinked payoff functions that are monotonically responsive and partially responsive to the realization of risk, respectively. The former case includes the newsboy problem with backorder; the latter case includes the newsboy problem without backorder and under some restrictions, also includes the optimal deductible insurance problem. Some relatively non-restrictive conditions derived from the necessary and sufficient conditions reveal that the three problems can be quite different, even though they are often viewed in the literature as being congruous. These conditions lead to simple predictions of the direction of change in any risk-averse agent’s optimal choice upon a change in risk, without assuming specific functional forms for the utility function.     

Estimation of time-varying ARMA models with Markovian changes in regime

In this paper, we consider the estimation of time-varying ARMA models subject to Markovian changes in regime. We give explicit conditions ensuring consistency and asymptotic normality, as well as the limiting covariance matrix, of least squares and quasi-generalized least-squares estimators.     

Spreading and generalized propagating speeds of discrete KPP models in time varying environments

The current paper deals with spatial spreading and front propagating dynamics for spatially discrete KPP (Kolmogorov, Petrovsky and Paskunov) models in time recurrent environments, which include time periodic and almost periodic environments as special cases. The notions of spreading speed interval, generalized propagating speed interval, and traveling wave solutions are first introduced, which are proper modifications of those introduced for spatially continuous KPP models in time almost periodic environments. Among others, it is then shown that the spreading speed interval in a given direction is the minimal generalized propagating speed interval in that direction. Some important upper and lower bounds for the spreading and generalized propagating speed intervals are provided. When the environment is unique ergodic and the so called linear determinacy condition is satisfied, it is shown that the spreading speed interval in any direction is a singleton (called the spreading speed), which equals the classical spreading speed if the environment is actually periodic. Moreover, in such a case, a variational principle for the spreading speed is established and it is shown that there is a front of speed c in a given direction if and only if c is greater than or equal to the spreading speed in that direction.      

Minimax estimators for location vectors in elliptical distributions with unknown scale parameter and its application to variance reduction in simulation

In this paper, we give an ever wider and new class of minimax estimators for the location vector of an elliptical distribution (a scale mixture of normal densities) with an unknown scale parameter. The its application to variance reduction for Monte Carlo simulation when control variates are used is considered. The results obtained thus extend (i) Berger's result concerning minimax estimation of location vectors for scale mixtures of normal densities with known scale parameter and (ii) Strawderman's result on the estimation of the normal mean with common unknown variance.Research partially supported by National Science Foundation, Grant #DMS 8901922.     

四川产业结构与就业结构变动关系的实证研究

四川省是农业人口大省,而在西部十二省中四川的经济发展水平处于前列。因此,对四川省产业结构和就业结构的研究对西部乃至全国经济的发展有着重要的意义。为了使四川省产业结构和就业结构之间的关系更加明确,本文运用Granger因果关系检验得出,四川省产业结构变动是引起就业结构变动的原因。在此基础上利用Johansen协整关系检验、线性回归等方法测算出产业结构对就业结构影响程度、做出各产业对就业贡献率的折线图并分析了产业结构与就业结构的偏离度。最后得出结论:第一产业产值的增长不仅无法拉动就业而且对业已存在的剩余劳动力还具有一定的挤出效应,第三产业具有较大的发展空间和吸纳就业的能力,第二产业次之。     

Phase II monitoring of changes in mean from high‐dimensional data

The generalized T² chart (GT‐chart), which is composed of the T² statistic based on a small number of principal components and the remaining components, is a popular alternative to the traditional Hotelling's T² control chart. However, the application of the GT‐chart to high‐dimensional data, which are now ubiquitous, encounters difficulties from high dimensionality similar to other multivariate procedures. The sample principal components and their eigenvalues do not consistently estimate the population values, and the GT‐chart relying on them is also inconsistent in estimating the control limits. In this paper, we investigate the effects of high dimensionality on the GT‐chart and then propose a corrected GT‐chart using the recent results of random matrix theory for the spiked covariance model. We numerically show that the corrected GT‐chart exhibits superior performance compared to the existing methods, including the GT‐chart and Hotelling's T² control chart, under various high‐dimensional cases. Finally, we apply the proposed corrected GT‐chart to monitor chemical processes introduced in the literature.