考虑埋深的物性下限确定方法——以G油田P油层为例

确定储层物性下限的方法很多,但是确定的物性下限均为某一固定值。对于埋深变化较大的岩性油藏,存在较大的误差。以G油田P油层为例论述了应用常规方法确定物性下限存在的问题,并应用考虑埋深的试油法和经验统计法确定了G油田P油层的物性下限,其确定的动态物性下限更符合油田的实际情况,对于向斜油藏的成藏机理,以及后续的储层评价方式都具有值得思考的意义。     

关于类星体的统计研究

介绍了天体物理学一个前沿领域－类星体观测，报道了在该领域中的观测数据和部分统计研究成果。     

对随机变量独立性的几点认识

独立性是概率统计的一个重要概念，给出了随机变量独立性的一些特殊性质．     

心理统计学教学模式的改革尝试

心理统计学是统计学原理与心理学研究相结合的一门学科。传统的教学模式中,理论和实践相脱节,使已毕业的学生反映实际运用时不会用。本着素质教育的理念,本文把统计软件应用与理论知识传授相结合,对传统教学模式进行改革,减轻了学生心理负担,增强了学生的实践能力,一定程度上实现了从以教师为中心取向的教学模式向以学生为中心的教学模式的转换。     

Best Fourier Approximation and Application in Efficient Blurred Signal Reconstruction

We expand upon the known results on sharp linear Fourier methods of approximation where the approximation is the best in terms of both rate and constant among all polynomial procedures of approximation. So far these results have been studied due to their mathematical beauty rather than their practical importance. In this paper we show that they are the core mathematics underlying best statistical methods of solving noisy ill-posed problems. In particular, we suggest a procedure for recovery of noisy blurred signals based on samples of small sizes where a traditional statistics concludes that the complexity of such a setting makes the problem not worthy of a further study. Thus, we present a problem where a combination of the classical approximation theory and statistics leads to interesting practical results.     

用FoxPro制作复合统计报表的方法

介绍了用ＦｏｘＰｒｏ 制作复合统计表和快速计算统计数据的方法，运用此方法可以减少统计时间，提高统计速度。     

Positive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimation

Many statistical methods gain robustness and flexibility by sacrificing convenient computational structures. In this article, we illustrate this fundamental tradeoff by studying a semiparametric graph estimation problem in high dimensions. We explain how novel computational techniques help to solve this type of problem. In particular, we propose a nonparanormal neighborhood pursuit algorithm to estimate high-dimensional semiparametric graphical models with theoretical guarantees. Moreover, we provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Though this article focuses on the problem of graph estimation, the proposed methodology is widely applicable to other problems with similar structures. We also report thorough experimental results on text, stock, and genomic datasets.