Finite sample performance of sequential designs for model identification

Classical regression analysis is usually performed in two steps. In the first step, an appropriate model is identified to describe the data generating process and in the second step, statistical inference is performed in the identified model. An intuitively appealing approach to the design of experiment for these different purposes are sequential strategies, which use parts of the sample for model identification and adapt the design according to the outcome of the identification steps. In this article, we investigate the finite sample properties of two sequential design strategies, which were recently proposed in the literature. A detailed comparison of sequential designs for model discrimination in several regression models is given by means of a simulation study. Some non-sequential designs are also included in the study.     

Sequential Series Systems and Their Risk Assessments

System reliability models are critical in both theory and engineering. Although there are many system reliability models, they still cannot cover some practical situations. In this article, three sequential series systems are introduced in terms of some real backgrounds. An order profile for the system working is also introduced, because it can be used to describe the working component contribution for the system clearly. As the systems become more and more complicated and important, the safety issue is a hot topic. Risk analysis is one of the important ways for safety studies and implements. The formulae for some risk measures for the sequential systems are presented from a safety point of view. A numerical example is given to illustrate the details of the procedure and formulae.     

Nearly optimal truncated group sequential test on binomial proportions

In this article, we systematically study the optimal truncated group sequential test on binomial proportions. Through analysis of the cost structure, average test cost is introduced as a new optimality criterion. According to the new criterion, the optimal tests on different design parameters including the boundaries, success discriminant value, stage sample vector, stage size, and the maximum sample size are defined. Since the computation time in finding optimal designs by exhaustive search is intolerably long, group sequential sample space sorting method and procedures are developed to find the near-optimal ones. In comparison with the international standard ISO2859-1, the truncated group sequential designs proposed in this article can reduce the average test costs around 20%.     

Construction of neighbor designs of block size 4 for all values of v ≥ 5 by using the method of addition

In this article, a general method of construction of neighbor block designs is given. The designs are constructed using variation of a simple method which we refer to as the method of addition (renamed as the method of cyclic shifts). We give complete solution of neighbor balanced designs for k = 4 for any value of v. We also give many series of generalized neighbor designs (GNDs). In the last section, we have constructed GNDs in a sequential manner (as Did John 1981) for v ≤ 50 and r is multiple of k.     

Sequential estimation for nonhomogeneous Ornstein-Uhlenbeck processes

In this paper, we propose a stochastic process, which is a class of nonhomogeneous diffusion process from the perspective of the corresponding nonlinear stochastic differential equation. The parameter included in the drift term are estimated by sequential maximum likelihood methodology on the basis of continuous sampling of the process. The sequential estimators are proved to be closed, unbiased, strongly consistent, normally distributed, and optimal in the mean square sense.     

Monitoring test for stability of copula parameter in time series

In this paper, we consider a monitoring procedure to detect changes of the copula parameter of strong mixing processes. We propose two monitoring procedures based on the cumulative sums of scores evaluated at consistent copula parameter estimates and their fluctuations. We investigate the asymptotic properties of our monitoring procedures under both the null of no change in the copula parameter and its alternative. We also illustrate a simulation study and a real data analysis.     

Following a moving target—Monte Carlo inference for dynamic Bayesian models

Markov chain Monte Carlo (MCMC) sampling is a numerically intensive simulation technique which has greatly improved the practicality of Bayesian inference and prediction. However, MCMC sampling is too slow to be of practical use in problems involving a large number of posterior (target) distributions, as in dynamic modelling and predictive model selection. Alternative simulation techniques for tracking moving target distributions, known as particle filters, which combine importance sampling, importance resampling and MCMC sampling, tend to suffer from a progressive degeneration as the target sequence evolves. We propose a new technique, based on these same simulation methodologies, which does not suffer from this progressive degeneration.     

离散复镜像理论对近地电偶极子的应用

对近地电偶极子赫芝矢位中的索末菲积分,仅用 Prony 法,对其被积函数分式部分进行指数函数逼近,可得到近地电偶极子的一串离散镜像,而这类积分就可看作这些镜像在观察点的解。采用此方法大大节省了计算时间,所得结果与沿实轴数值积分结果吻合很好。对索末菲积分在实轴上可能遇到的奇异点,采用分部积分法消去之,避免了给数值积分带来的麻烦。