Estimation of extreme percentiles in Birnbaum-Saunders distributions

The Birnbaum-Saunders distribution has recently received considerable attention in the statistical literature, including some applications in the environmental sciences. Several authors have generalized this distribution, but these generalizations are still inadequate for predicting extreme percentiles. In this paper, we consider a variation of the Birnbaum-Saunders distribution, which enables the prediction of extreme percentiles as well as the implementation of the EM algorithm for maximum likelihood estimation of the distribution parameters. This implementation has some advantages over the direct maximization of the likelihood function. Finally, we present results of a simulation study along with an application to a real environmental data set.     

Compile and Run-Time Support for the Parallelization of Sparse Matrix Updating Algorithms

This work presents a survey of the capabilities that the sparse computation offers for improving performance when parallelized, either automatically or through a data-parallel compiler. The characterization of a sparse code gets more complicated as code length increases: Access patterns change from loop to loop, thus making necessary to redefine the parallelization strategy. While dense computation solely offers the possibility of redistributing data structures, several other factors influence the performance of a code excerpt in the sparse field, like source data representation on file, compressed data storage in memory, the creation of new nonzeroes at run-time (fill-in) or the number of processors available. We analize the alternatives that arise from each issue, providing a guideline for the underlying compilation work and illustrating our techniques with examples on the Cray T3E.     

Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions

We consider on-line density estimation with a parameterized density from the exponential family. The on-line algorithm receives one example at a time and maintains a parameter that is essentially an average of the past examples. After receiving an example the algorithm incurs a loss, which is the negative log-likelihood of the example with respect to the current parameter of the algorithm. An off-line algorithm can choose the best parameter based on all the examples. We prove bounds on the additional total loss of the on-line algorithm over the total loss of the best off-line parameter. These relative loss bounds hold for an arbitrary sequence of examples. The goal is to design algorithms with the best possible relative loss bounds. We use a Bregman divergence to derive and analyze each algorithm. These divergences are relative entropies between two exponential distributions. We also use our methods to prove relative loss bounds for linear regression.     

Robust Classification for Imprecise Environments

In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.     

On the Sequential Accumulation of Evidence

In this paper, we introduce a method for sequentially accumulating evidence as it pertains to an active observer seeking to identify an object in a known environment. We develop a probabilistic framework, based on a generalized inverse theory, where assertions are represented by conditional probability density functions. This leads to a sequential recognition strategy in which evidence is accumulated over successive viewpoints using Bayesian chaining until a definitive assertion can be made. To illustrate the theory we show how the characteristics of belief distributions can be exploited in a model-based recognition problem, where the task is to identify an unknown model from a database of known objects on the basis of parameter estimates. We illustrate the robustness of the algorithm through recognition experiments in two very different contexts: (1) a highly structured recognition context where 3-D parametric models can be estimated directly from range data, (2) a complex environment, where the relationship between the data and the model is learned through an appearance-based strategy. Specifically, the flow fields computed through the object's motion are used as structural signatures for recognition.     

Optimal acceptance sampling plans for log-location-scale lifetime models using average risks

Assuming that the probability of obtaining a defective unit in a production process, p, is not constant, a versatile methodology is presented for determining optimal failure-censored reliability sampling plans for log-location-scale lifetime models. The optimization procedure to decide the acceptability of a product is usually sufficiently accurate for the most widely used parametric lifetime models, such as the Weibull and lognormal distributions, and fairly robust to small deviations in the prior knowledge. Moreover, lower and upper bounds on the optimal sample size, and the corresponding acceptance constants, are derived in closed-forms. The proposed approach extends the traditional sampling plans to those cases in which appreciable prior information on p exists, and also allows the analyst the flexibility to delimitate the range of p and to incorporate into the reliability analysis prior impartiality between the producer and the consumer. In addition, the practitioners may achieve substantial savings in sample size, better information on the production process and better assessment of the true producer and consumer risks. An example related to the acceptability of a certain kind of gyroscope is included for illustrative purposes. Various practical prior distributions are considered to describe the random fluctuations in the proportion defective.     

基于时频分析的混沌谐波线性混合信号提取算法

混沌信号与谐波信号的分离问题是混沌信号处理中的重点问题,时频分析理论的日趋成熟为该问题带来了新的解决方案。针对不同噪声情况下混沌背景下谐波信号的提取进行分析,将小波变换与EMD方法进行比较,经仿真实验总结出一种新的谐波信号提取的融合算法,仿真实验验证了该算法的有效性。     

抛填骨料工艺超高强混凝土的制备研究

采用抛填骨料工艺制备强度超过100MPa的超高强混凝土。研究表明,采用分层均布的布料方式,可在不影响施工工作性的条件下制得粗骨料体积分数高达54%、骨料相互嵌锁的超高强混凝土,其力学性能、抗氯离子渗透能力均优于常规方法制得的超高强混凝土,更佳的颗粒级配分布(PSD)有利于其密实度的增加。采用贝雷参数检验法评估集料级配,表明级配更为合理。抛填骨料工艺超高强混凝土中胶凝材料的用量大幅度降低,对降低混凝土环境影响具有重要意义。     

基于高维正态积分的串联结构系统可靠性研究

针对串联结构系统可靠性问题,根据一阶可靠性方法,把串联结构系统的失效概率表示为高维正态积分的形式,提出一种有效的计算高维正态积分方法.采用积分变换法将高维正态积分转换为单位超立方域内的高维积分,采用准Monte Carlo方法求解,建立了新的串联结构系统失效概率求解方法.将本文方法和4种近似方法以及精确解进行了比较,研究了该方法的计算精度.最后,进行了某一潜水器结构系统可靠性计算,计算结果表明该方法计算精度较高.