CE模型下指数分布步加试验的贝叶斯估计

本文利用Gibbs抽样法得到了CE模型下指数分布场合步进应力加速寿命试验的多层Bayes参数估计,最后通过模拟比较表明多层Bayes估计比最大似然估计更加有效而实用。     

Metrics of algebraic closures in pattern recognition problems with two nonoverlapping classes

It is shown that, in the pattern recognition problem with two nonoverlapping classes, the matrices of estimates of the object closeness are described by a metric. The transition to the algebraic closure of the model of recognizing operators of finite degree corresponds to the application of a special transformation of this metric. It is proved that the minimal degree correct algorithm can be found as a polynomial of a special form. A simple criterion for testing classification implementations is obtained.     

Parameter estimation of hidden periodic model in random fields

Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved. Project supported by the National Natural Science Foundation of China (Grant No. 19571002).     

定时截尾场合双参数威布尔分布的贝叶斯推断

给出了定时截尾下,双参数威布尔分布中参数及可靠性指标的Bayes估计,并且给出了未来观测值的贝叶斯预测问题.     

基于分组数据的指数分布参数的同变估计

给出了基于分组数据的指数分布参数的同变估计,其中位置参数是最优同变估计,刻度参数为近似最优同变估计,最后通过Monte-Carlo模拟数据说明方法的可行性.     

Parameter estimation of ordinary differential equations

This paper addresses the development of a new algorithm forparameter estimation of ordinary differential equations. Here,we show that (1) the simultaneous approach combined with orthogonalcyclic reduction can be used to reduce the estimation problemto an optimization problem subject to a fixed number of equalityconstraints without the need for structural information to devisea stable embedding in the case of non-trivial dichotomy and(2) the Newton approximation of the Hessian information of theLagrangian function of the estimation problem should be usedin cases where hypothesized models are incorrect or only a limitedamount of sample data is available. A new algorithm is proposedwhich includes the use of the sequential quadratic programming(SQP) Gauss–Newton approximation but also encompassesthe SQP Newton approximation along with tests of when to usethis approximation. This composite approach relaxes the restrictionson the SQP Gauss–Newton approximation that the hypothesizedmodel should be correct and the sample data set large enough.This new algorithm has been tested on two standard problems.     

Maximum likelihood estimation in the proportional hazards cure model

The proportional hazards cure model generalizes Cox’s proportional hazards model which allows that a proportion of study subjects may never experience the event of interest. Here nonparametric maximum likelihood approach is proposed to estimating the cumulative hazard and the regression parameters. The asymptotic properties of the resulting estimators are established using the modern empirical process theory. And the estimators for the regression parameters are shown to be semiparametric efficient.     

The population-sample decomposition approach to multivariate estimation methods

In this paper it is argued that all multivariate estimation methods, such as OLS regression, simultaneous linear equations systems and, more widely, what are known as LISREL methods, have merit as geometric approximation methods, even if the observations are not drawn from a multivariate normal parent distribution and consequently cannot be viewed as ML estimators. It is shown that for large samples the asymptotical distribution of any estimator, being a totally differentiable covariance function, may be assessed by the δ method. Finally, we stress that the design of the sample and a priori knowledge about the parent distribution may be incorporated to obtain more specific results. It turns out that some fairly traditional assumptions, such as assuming some variables to be non-random, fixed over repeated samples, or the existence of a parent normal distribution, may have dramatic effects on the assessment of standard deviations and confidence bounds, if such assumptions are not realistic. The method elaborated by us does not make use of such assumptions.     

A New Technique to Determine Horizontal and Vertical Permeabilities from the Time-Delayed Response of a Vertical Interference Test

To determine the permeabilities from a vertical interference test, earlier investigators proposed correlations or type curves based on point source solutions or partially penetrated well solutions. However, it is impossible for these correlations or type curves to cover all possible scenarios in the real world. In more recent years, people use regression analysis to simultaneously match the pressure responses at the source interval and the observation point. With regression analysis, we face the problem of non-unique solutions. Sometimes, estimated permeability could be outside a reasonable range when analyzing noisy data from real tests. In this paper, a new technique is presented to estimate horizontal and vertical permeabilities from the time-delayed response in a wireline vertical interference test. In a vertical test, a pressure drawdown test period is followed by a pressure buildup test period. Because of the delay in response, the pressure at the observation probe continues to drop for some time while the pressure at the source interval is being built up. The maximum pressure drop at the observation probe with regard to the maximum pressure drop in the source interval is time-delayed. Using this time delay and the maximum pressure drop at the observation probe, vertical and horizontal permeabilities can be estimated. A novel numerical scheme is used. The new technique is compared with the previous methods, and it shows its superiority in accuracy. In addition, it can be used in different test configurations. Application issues in real testing conditions are discussed. Finally, two field tests are analyzed using this technique, while an earlier effort to analyze the tests using a conventional method was not successful due to poor data quality.