Debugging software often involves a removal process: the process of detection and removal of faults from a program. This paper gives an e4cient procedure to detect all faults in a software item with high probability. The procedure is such that at any step the probability of leaving just one fault is equal to some speci6ed value: the probability of leaving more than one fault is much smaller. It is found that, after eliminating the risk of early stopping, the probability of incomplete detection is then only slightly greater than). The performance of the proposed procedure is demonstrated by simulation and by application to a real example.

点过程是一个应用广泛的统计模型，在医学，社会学，经济学，电子与通信科学以及软件与硬件可靠性等许多科学领域都能找到应用点过程的例子。在这些实际应用中，一般是根据问题的实际背景假定模型具有一定的参数形式，然后根据观测数据给出未知参数的极大似然估计值以推断事物发展的客观规律。我们知道，一种估计量是否收敛以及收敛速度的快慢，是决定这种估计量好坏的最为重要的标准。本文对于一般的点过程模型中向量参数极大似然估计（MLE）首先给出了一个保证其强相合的较为广泛的充分性条件，然后在进一步的条件下得到了重对数型的收敛速度。

给出了EM算法在统计一个新的应用领域-假设检验，EM算法通常是用来求胸脯模型参数极大似然估计的一种有效的迭代算法，这种算法利用数据扩张，将比较复杂的似然函数的最优化问题成一系列比较简单的函数的优化问题，而对于一个模型来说，假设检验往往极大似然估计更为得杂，所以有必要找到简单的方法解决检验中的较复杂的优化问题，所给出的方法不是建设立在大样本理论基础上，因此特别适合于中小样本情形。

Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expression data are not efficient for biomarker identification and disease diagnosis. In this paper, we propose a general framework to incorporate feature (gene) selection into pattern recognition in the process to identify biomarkers. Using this framework, we develop three feature wrappers that search through the space of feature subsets using the classification error as measure of goodness for a particular feature subset being “wrapped around”: linear discriminant analysis, logistic regression, and support vector machines. To effectively carry out this computationally intensive search process, we employ sequential forward search and sequential forward floating search algorithms. To evaluate the performance of feature selection for biomarker identification we have applied the proposed methods to three data sets. The preliminary results demonstrate that very high classification accuracy can be attained by identified composite classifiers with several biomarkers.

We use the semiparametric additive hazards model to formulate the effects of individual covariates on the capture rates in the continuous-time capturerecapture experiment, and then construct a Horvitz-Thompson-type estimator for the unknown population size. The resulting estimator is consistent and asymptotically normal with an easily estimated variance. Simulation studies show that the asymptotic approximations are adequate for practical use when the average capture probabilities exceed.5. Ignoring covariates would underestimate the population size and the coverage probability is poor. A wildlife example is provided.

In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T 2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets.

给出了在任意序贯试验情形下指数分布均值置信限的一般构造方法，这种序贯试验可是有替换试验也可以是无替换试验，特别是给了了无失效情形下的最好置信限显式表达式。

本文通过修改JM模型给出了一种新的软件可靠性模型，并对软件可靠度给出了点估计和置信限。对实际数据的分析表明这种新模型预测能力比JM模型要好。

在医学研究和产品研制过程中，由于试验对象难于找到或者试验费用昂贵常出现小样本情形。此时，精确置信推断尤其重要只要在样本空间中给出一种序就可以定义模型参数的某个函数的精确置信限。这样得到的置信限称为Buehler置信限。虽然它的定义比较容易，但是当多维参数或者不完全观测数据出现时，计算有时难于实行。为了解决这种计算问题，本文构造出一种基于EN算法的方。法EN算法原本是用于求解极大似然估计的方法，在这里EM算法首次被用于求解精确置信限分析了种模型和一组实际数据以说明这个方法。