一般分布区间型符号数据的SOM聚类方法

区间型符号数据是一种重要的符号数据类型,现有文献往往假设区间内的点数据服从均匀分布,导致其应用的局限性。本文基于一般分布的假设,给出了一般分布区间型符号数据的扩展的Hausdorff距离度量,基于此提出了一般分布的区间型符号数据的SOM聚类算法。随机模拟试验的结果表明,基于本文提出的基于扩展的Hausdorff距离度量的SOM聚类算法的有效性优于基于传统Hausdorff距离度量的SOM聚类算法和基于μσ距离度量的SOM聚类算法。最后将文中方法应用于气象数据的聚类分析,示例文中方法的应用步骤与可操作性,并进一步评价文中方法在解决实际问题中的有效性。     

A Tree Algorithm for Isotropic Finite Elements on the Sphere

The earth's surface is an almost perfect sphere. Deviations from its spherical shape are less than 0.4% of its radius and essentially arise from its rotation. All equipotential surfaces are nearly spherical, too. In consequence, multiscale modeling of geoscientifically relevant data on the sphere plays an important role. In this paper, we deal with isotropic kernel functions showing a local support (briefly called isotropic finite elements) for reconstructing square-integrable functions on the sphere. An essential tool is the concept of multiresolution analysis by virtue of the spherical up function. Because the up function is built by an infinite convolution product, we do not know an explicit representation of it. However, the tree algorithm for the multiresolution analysis based on the up functions can be formulated by convolutions of isotropic kernels of low-order polynomial structure. For these kernels, we are able to find an explicit representation, so that the tree algorithm can be implemented efficiently.     

Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds

The energy distribution of wind-driven ocean waves is of great interest in marine science. Discovering the generating process of ocean waves is often challenging and the direction is the key for a better understanding. Typically, wave records are transformed into a directional spectrum which provides information about the wave energy distribution across different frequencies and directions. Here, we propose a new time series clustering method for a series of directional spectra to extract the spectral features of ocean waves and develop informative visualization tools to summarize identified wave clusters. We treat directional distributions as functional data of directions and construct a directional functional boxplot to display the main directional distribution of the wave energy within a cluster. We also trace back when these spectra were observed, and we present color-coded clusters on a calendar plot to show their temporal variability. For each identified wave cluster, we analyze wind speed and wind direction hourly to investigate the link between wind data and wave directional spectra. The performance of the proposed clustering method is evaluated by simulations and illustrated by a real-world dataset from the Red Sea. Supplementary materials for this article are available online.     

A Reliable Iteration Algorithm for One-Bit Compressive Sensing on the Unit Sphere

The one-bit compressed sensing problem is of fundamental importance in many areas,such as wireless communication,statistics,and so on.However,the optimization of one-bit problem coustrained on the unit sphere lacks an algorithm with rigorous mathematical proof of convergence and validity.In this paper,an iteration algorithm is established based on difference-of-convex algorithm for the one-bit compressed sensing problem constrained on the unit sphere,with iterating formula ■,where C is the conve...     

基于删失数据的指数威布尔分布最大似然估计的新算法

本文讨论了指数威布尔分布当观测数据是删失数据情形时参数的最大似然估计问题.因为删失数据是一种不完全数据,我们利用EM算法来计算参数的近似最大似然估计.由于EM算法计算的复杂性,计算效率也不理想.为了克服牛顿-拉普森算法和EM算法的局限性,我们提出了一种新的方法.这种方法联合了指数威布尔分布到指数分布的变换和等效寿命数据的技巧,比牛顿-拉普森算法和EM算法更具有操作性.数据模拟讨论了这一方法的可行性.为了演示本文的方法,我们还提供了一个真实寿命数据分析的例子.     

基于区间数据最大似然估计的新方法

In this paper, we study the two-parameter maximum likelihood estimation (MLE)problem for the GE distribution with consideration of interval data. In the presence of interval data, the analytical forms for the restricted MLE of the parameters of GE distribution do not exist. Since interval data is kind of incomplete data, the EM algorithm can be applied to compute the MLEs of the parameters. However the EM algorithm could be less effective.To improve effectiveness, an equivalent lifetime method is employed. The two methods are discussed via simulation studies.     

A Fast Estimation Method for Mean Change Point in Massive Data Sets

When the sample size is $N$, the computational complexity of the least squares estimate of mean change point is O(N^2), and it's necessary to reduce the computational complexity in the case of huge data. In this paper, a two-stage fast scanning algorithm is proposed for the estimation of mean change point, and it is proved that this method has the same convergence speed and limiting distribution as the least squares estimation of mean change point, and the optimal complexity of the new algorithm is O(N^{4/3}\cdot b_n^{2/3}). We have conducted sufficient data experiments in terms of computation time and estimated efficiency, and the results show that the estimated efficiency of the new and old methods is similar, but the computation time of our method is obviously shortened.     

A New,Fast Algorithm to Find the Regions of Possible Support for Bivariate Interval-Censored Data

The estimation of the nonparametric maximum likelihood estimate (NPMLE) of the bivariate distribution function on interval-censored data is a recent topic of research. Among other things, it provides a basic tool for checking a parametric model for the bivariate failure times. As a first step in the estimation of the NPMLE for bivariate interval-censored data, the regions of possible support—that is, the rectangles with nonzero mass—are calculated. For this step a new, fast algorithm is introduced here and compared with two existing algorithms. The advantages of our algorithm will be illustrated on the emergence times of permanent teeth on data from the longitudinal Signal® Tandmobiel study.     

方向数据密度核估计的对数律

设X为取值于k维单位球面上的单位随机向量,具有概率密度函数f(x),X_1,…,X_n为X的n个i.i.d.的观察,讨论f(x)具有形式的核估计,其中K为定义于[0,+∞]上的非负核函数,ω_k为Ω_k上的Lebesque测度,本文建立了fn(x)的对数律,并给出了fn(x)的一致强相合速度。     

A Simple Method for Computing the Observed Information Matrix When Using the EM Algorithm with Categorical Data

Abstract

A simple matrix formula is given for the observed information matrix when the EM algorithm is applied to categorical data with missing values. The formula requires only the design matrices, a matrix linking the complete and incomplete data, and a few simple derivatives. It can be easily programmed using a computer language with operators for matrix multiplication, element-by-element multiplication and division, matrix concatenation, and creation of diagonal and block diagonal arrays. The formula is applicable whenever the incomplete data can be expressed as a linear function of the complete data, such as when the observed counts represent the sum of latent classes, a supplemental margin, or the number censored. In addition, the formula applies to a wide variety of models for categorical data, including those with linear, logistic, and log-linear components. Examples include a linear model for genetics, a log-linear model for two variables and nonignorable nonresponse, the product of a log-linear model for two variables and a logit model for nonignorable nonresponse, a latent class model for the results of two diagnostic tests, and a product of linear models under double sampling.     

基于聚类算法的区间数多属性决策方法及应用

针对具有正态分布随机变量的多属性决策问题,依据属性均值聚类思想,提出了一种计算机编程求解方法.方法避开了区间数的运算和比较,评价结果不具有主观随意性.最后,我们给出了一个实际应用的例子,验证了方法的有效性.     

A Feasible Algorithm for Locating Concave and Convex Zones of Interval Data and Its Use in Statistics-Based Clustering

Often, we need to divide n objects into clusters based on the value of a certain quantity x. For example, we can classify insects in the cotton field into groups based on their size and other geometric characteristics. Within each cluster, we usually have a unimodal distribution of x, with a probability density ρ(x) that increases until a certain value x ₀ and then decreases. It is therefore natural, based on ρ(x), to determine a cluster as the interval between two local minima, i.e., as a union of adjacent increasing and decreasing segments. In this paper, we describe a feasible algorithm for solving this problem.     

关于负超几何分布期望与方差算法的注记

研究了非还原取样模型中负超几何随机变量的联合分布,得到了若干有用的推论.据此给出了负超几何分布的期望和方差的一种分解算法.     

给定密度随机数的生成的通用方法与球面和球体上均匀随机向量的生成法

本文首先介绍基于垂直概率密度表示的,给定密度函数的随机数生成的通用方法;然后介绍球面及球体上均匀随机向量的生成算法。     

基于SVM理论的一种新的数据分类方法

基于 SVM分类器在模式识别问题中有独特的优势 ,本文通过对标准 SVM模型的改造 ,提出了一种新的简单的数据分类方法 .理论分析和实验表明 ,该方法与标准 SVM分类方法相比具有处理大规模数据识别的能力且保持较高的样本识别率 ,节省存储空间等优势 .     

外显子周期性探究及自适应识别快速算法研究

外显子的3-周期性已被广泛的应用到基因识别中,而通过大量的实验表明并不是所有的已知外显子都表现出3-周期性,探究不同变换下外显子3-周期性的表现,得到自相关傅里叶变换可以增强外显子的3-周期性质;同时研究了不同数值映射之间在计算平均功率谱,N/3处功率谱和信噪比的关联,通过理论推导得到关联结果,进行仿真实验,并且结合Voss映射下的快速算法提出Z-curve映射下的快速算法,模拟不同DNA序列分别按照Z-curve映射下原始计算方法和快速算法计算相应的公式,实验结果证明快速算法的有效性和快速性,最后给出自适应窗口平移法识别外显子,利用评价指标,证明算法的实用性和可行性.     

A Fast and Effective Algorithm for the Feedback Arc Set Problem

A divide-and-conquer approach for the feedback arc set is presented. The divide step is performed by solving a minimum bisection problem. Two strategies are used to solve minimum bisection problem: A heuristic based on the stochastic evolution methodology, and a heuristic based on dynamic clustering. Empirical results are presented to compare our method with other approaches. An algorithm to construct test cases for the feedback arc set problem with known optimal number of feedback arcs, is also presented.     

线性规划的目标函数最速递减算法

在对偶单纯形方法的基础上，提出了线性规划的目标函数最速递减算法。它避开求初始可行基或初始基，以目标函数全局快速递减作为选基准则，将选基过程与换基迭代合二为一，从而大大减少了迭代次数。数值算例显示了该算法的有效性和优越性。     

一种基于后验概率判决的one-by-one快速相关攻击算法(英文)

本文描述了一种基于后验概率判决的 one by one快速相关攻击算法.本文试图通过概率的观点来看待和分析快速相关攻击问题.该算法的优点有以下三点.首先,和文献[5]相比 one by one算法减少了对存储空间的需求.其次,提出了攻击失败概率的概念,并利用中心极限定理给出了它和密钥流序列长度的关系.最后,和文献[4]相比,该算法只需要更少的密钥流序列就可以达到几乎相同的攻击效果.     

Orthogonal Zonal, Tesseral and Sectorial Wavelets on the Sphere for the Analysis of Satellite Data

The spherical harmonics Y _n,k } _{n=0,1,...;k=?n,...,n} represent a standard complete orthonormal system in ?²(Ω), where Ω is the unit sphere. In view of present and future satellite missions (e.g., for the determination of the Earth's gravity field) it is of particular importance to treat the different accuracies and sizes of data in dependence of the index pairs (n,k). It is, e.g., known that the GOCE mission yields essentially less accurate data in the zonal (k=0) case. Therefore, this paper presents new ways of constructing multiresolutions for a Sobolev space of functions on Ω allowing the separate treatment of certain classes of pairs (n,k) and, in particular, the separate treatment of different orders k. Orthogonal bandlimited as well as non-bandlimited detail and scale spaces adapted to certain (geo)scientific problems and to the character of the given data can now be used. Finally, an explicit representation of a non-bandlimited wavelet on Ω yielding an orthogonal decomposition of the function space is calculated for the first time.