Persistence Terrace for Topological Inference of Point Cloud Data

Topological data analysis (TDA) is a rapidly developing collection of methods for studying the shape of point cloud and other data types. One popular approach, designed to be robust to noise and outliers, is to first use a smoothing function to convert the point cloud into a manifold and then apply persistent homology to a Morse filtration. A significant challenge is that this smoothing process involves the choice of a parameter and persistent homology is highly sensitive to that choice; moreover, important scale information is lost. We propose a novel topological summary plot, called a persistence terrace, that incorporates a wide range of smoothing parameters and is robust, multi-scale, and parameter-free. This plot allows one to isolate distinct topological signals that may have merged for any fixed value of the smoothing parameter, and it also allows one to infer the size and point density of the topological features. We illustrate our method in some simple settings where noise is a serious issue for existing frameworks and then we apply it to a real dataset by counting muscle fibers in a cross-sectional image. Supplementary material for this article is available online.     

求解一类非单调线性互补问题的宽邻域内点方法及其计算复杂性

对于一类非单调线性互补问题给出了一种新的算法——宽邻域内点算法，并讨论了其计算复杂性。     

线性分式函数的自迭代周期与吸引子

应用矩阵特征值的方法推导出线性分式的 n次自迭代通项公式 ,证明了对于任意的自然数 n 3 ,存在无穷多以 n为自迭代周期的线性分式 ,给出了线性分式函数的自迭代周期和吸引子的较全面的刻画 .     

基于收缩近邻方法的征信缺失数据插补研究

在海量征信数据的背景下,为降低缺失数据插补的计算成本,提出收缩近邻插补方法.收缩近邻方法通过三阶段完成数据插补,第一阶段基于样本和变量的缺失比例计算入样概率,通过不等概抽样完成数据的收缩,第二阶段基于样本间距离,选取与缺失样本近邻的样本组成训练集,第三阶段建立随机森林模型进行迭代插补.利用Australian数据集和中国各银行数据集进行模拟研究,结果表明在确保一定插补精度的情况下,收缩近邻方法较大程度减少了计算量.     

A General Class of Change Point and Change Curve Modeling for Life Time Data

Change point hazard rate models arise in many life time data analysis, for example, in studying times until the undesirable side effects occur in clinical trials. In this paper we propose a general class of change point hazard model for survival data. This class includes and extends different types of change point models for survival data, e.g. cure rate model and lag model. Most classical approach develops estimates of model parameters, with particular interest in change point parameter and often the whole hazard function, but exclusively in terms of asymptotic properties. We propose a Bayesian approach, avoiding asymptotics and provide inference conditional upon the observed data. The proposed Bayesian models are fitted using Markov chain Monte Carlo method. We illustrate our proposed methodology with an application to modeling life times of the printed circuit board.     

A Strong Representation of the Product-Limit Estimator for Left Truncated and Right Censored Data

In this paper we consider the TJW product-limit estimatorF_n(x) of an unknown distribution functionFwhen the data are subject to random left truncation and right censorship. An almost sure representation of PL-estimatorF_n(x) is derived with an improved error bound under some weaker assumptions. We obtain the strong approximation ofF_n(x)−F(x) by Gaussian processes and the functional law of the iterated logarithm is proved for maximal derivation of the product-limit estimator toF. A sharp rate of convergence theorem concerning the smoothed TJW product-limit estimator is obtained. Asymptotic properties of kernel estimators of density function based on TJW product-limit estimator is given.     

Iteratively Reweighted Generalized Least Squares for Estimation and Testing With Correlated Data: An Inference Function Framework

The focus of this article is on fitting regression models and testing of general linear hypotheses for correlated data using quasi-likelihood based techniques. The class of generalized method of moments or GMMs provides an elegant approach for estimating a vector of regression parameters from a set of score functions. Extending the principle of the GMMs, in the generalized estimating equation framework, leads to a quadratic inference function or QIF approach for the analysis of correlated data. We derive an iteratively reweighted generalized least squares or IRGLS algorithm for finding the QIF estimator and establish its convergence properties. A software library implementing the techniques is demonstrated through several datasets.     

A spectral notion of Gromov–Wasserstein distance and related methods

We introduce a spectral notion of distance between objects and study its theoretical properties. Our distance satisfies the properties of a metric on the class of isometric shapes, which means, in particular, that two shapes are at 0 distance if and only if they are isometric when endowed with geodesic distances. Our construction is similar to the Gromov–Wasserstein distance, but rather than viewing shapes merely as metric spaces, we define our distance via the comparison of heat kernels. This allows us to establish precise relationships of our distance to previously proposed spectral invariants used for data analysis and shape comparison, such as the spectrum of the Laplace–Beltrami operator, the diagonal of the heat kernel, and certain constructions based on diffusion distances. In addition, the heat kernel encodes a natural notion of scale, which is useful for multi-scale shape comparison. We prove a hierarchy of lower bounds for our distance, which provide increasing discriminative power at the cost of an increase in computational complexity. We also explore the definition of other spectral metrics on collections of shapes and study their theoretical properties.     

线性数据拟合方法的误差分析及其改进应用

利用最小二乘法进行线性数据拟合在一定条件下存在着误差较大的缺陷,为使线性数据拟合方法在科学实验和工程实践中能够更加准确地求解量与量之间的关系表达式,本文通过对常用线性数据拟合方法———最小二乘法进行了误差分析,并在此基础上提出了最小距离平方和法以对最小二乘法作改进处理.最后,通过举例分析对两种线性数据拟合方法的优劣加以讨论并分别给出其较为合理的应用控制条件.     

trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language. Supplementary materials for this article are available online.     

面板数据分位数回归模型求解的模式搜索法

本文应用最优化理论,对固定效应的面板数据分位数回归模型,提出一种模式搜索方法,此方法可以同时估计出所有分位点处的解释变量系数和所有个体的固定效应值。进一步利用蒙特卡洛模拟比较现有文献中涉及的面板数据分位数回归方法,结果显示无论误差项是否满足经典假设,模式搜索分位数回归法较之其他分位数回归估计方法更为有效.     

基于长偏置数据的一个光滑剩余生命函数估计量(英文)

受希勒引理的启发,针对长偏置数据给出一个带泊松权重的平均剩余生命函数的光滑估计量.同时研究了该光滑估计量的渐进性质,例如强相合性及渐进正态性等性质.     

金融资产多分辨风险识别及投资组合策略

为有效地揭示金融市场微观结构、反映和分散金融风险,基于最大重复离散小波变换对高频数据进行了多分辨分析,利用小波方差和小波协方差给出多分辨Beta系数的计算方法,讨论了高频金融资产不同时间尺度下的风险组成。针对收益与风险的多分辨特征,提出多分辨投资组合策略,改进了Markowitz的静态投资组合方法。实证研究表明,同一金融资产在不同时间尺度下的收益与风险存在多分辨特征;CAPM的表现也具有多分辨特征;而多分辨的投资组合策略则将不同时间尺度下的投资风险降到了最低。     

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.     

右删失长度偏差数据分位数差的非参数估计

利用长度偏差数据所特有的辅助信息,对带右删失的长度偏差数据的分位数差提出了一种新的非参数估计.该方法提高了估计的有效性,所得的估计量形式简洁,便于计算.同时,本文用经验过程理论建立了该分位数差估计的相合性及渐近正态性,并给出方差估计的重抽样方法.本文还通过数值模拟考察了该估计量在有限样本下的表现,并将其应用到一个关于老年痴呆的实际数据中.     

Examples of Scientific Problems and Data Analyses in Demography,Neurophysiology, and Seismology

Examples of scientific problems and data analyses are presented for the fields of demography, neurophysiology, and seismology. The examples are connected by the involvement of space or time. The demographic problem is to display quantities derived from spatially aggregated data and associated measures of uncertainty. The neurophysiological problem is to infer the presence of complex pathways among groups of neurons given sequences of firing times. There are two seismological problems: (1) to determine isoseismals of recorded intensities following the Loma Prieta earthquake and (2) to relate intensity and acceleration values measured at distinct locations. The statistical analyses are connected to each other by the application of smoothing in some form and by the provision of consequent graphical displays.     

Analysis of Data with Censored Initiating and Terminating Times: A Missing-Data Approach

Survival data with censored initiating and terminating times have surfaced in some recent epidemiologic studies. Unlike standard survival analysis with known initiating times, analysis of data with both censored initiating and terminating times requires maximization of a complicated bivariate likelihood, which is often difficult to carry out. This article considers a missing-data formulation of the problem and focuses on the use of EM-type algorithms to simplify the computation of maximum likelihood estimates. This approach provides a feasible way of performing regression analysis with such bivariate survival data. Several illustrative examples are provided, including a real-data analysis application involving a cohort of HIV-infected hemophiliac patients.     

截断样本下的强率函数和密度函数核估计的渐进正态性

本文用[1]发展的计数过程去研究截断样本下强率函数核估计的渐进正态性．在弱于[7]和[10]的条件下，得到了更一般的结果．接着我们将这种方法运用到密度函数核估计，在较弱的条件下，得到了截断样本下密度函数核估计的渐进正态性．     

周期数据时段划分的双截断高斯混合模型及其EM算法

有限混合模型是多模态数据拟合和聚类的有力工具,本文针对具有多模态的周期数据提出了双截断高斯混合糢型,并推导出相应的EM算法,再通过BIC准則确定混合成分个数,该方法的优点是可以将相邻周期上距离较近的数据聚为一类.模拟研究显示,在具体参数设置下,EM算法和BIC准则是相合的。最后,该方法应用于车流量数据的时段划分,将一天划分为具有显著特征的6个时段,有助于交通部门采取相应策略,为优化交通灯信号配时提供参考依据.