A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

Spatially Balanced Sampling of Continuous Populations

When sampling from a continuous population (or distribution), we often want a rather small sample due to some cost attached to processing the sample or to collecting information in the field. Moreover, a probability sample that allows for design‐based statistical inference is often desired. Given these requirements, we want to reduce the sampling variance of the Horvitz–Thompson estimator as much as possible. To achieve this, we introduce different approaches to using the local pivotal method for selecting well‐spread samples from multidimensional continuous populations. The results of a simulation study clearly indicate that we succeed in selecting spatially balanced samples and improve the efficiency of the Horvitz–Thompson estimator.     

A robust automatic clustering algorithm for probability density functions with application to categorizing color images

This study develops a robust automatic algorithm for clustering probability density functions based on the previous research. Unlike other existing methods that often pre-determine the number of clusters, this method can self-organize data groups based on the original data structure. The proposed clustering method is also robust in regards to noise. Three examples of synthetic data and a real-world COREL dataset are utilized to illustrate the accurateness and effectiveness of the proposed approach.     

Several least-squares problems related to the Hodrick–Prescott filtering

The Hodrick–Prescott (HP) filtering is frequently used in macroeconometrics to decompose time series, such as real gross domestic product, into their trend and cyclical components. Because the HP filtering is a basic econometric tool, it is necessary to have a precise understanding of the nature of it. This article contributes to the literature by listing several (penalized) least-squares problems that are related to the HP filtering, three of which are newly introduced in the article, and showing their properties. We also remark on their generalization.     

Non uniform exponential bounds on normal approximation by Stein’s method and monotone size bias couplings

It is known that the normal approximation is applicable for sums of non negative random variables, W, with the commonly employed couplings. In this work, we use the Stein’s method to obtain a general theorem of non uniform exponential bound on normal approximation base on monotone size bias couplings of W. Applications of the main result to give the bound on normal approximation for binomial random variable, the number of bulbs on at the terminal time in the lightbulb process, and the number of m runs are also provided.     

基于雨课堂的电工电子学混合式智慧教学方法研究

《电工电子学》是高等学校针对非电类工科专业开设的一门有关电工、电子技术的重要专业基础课。针对《电工电子学》传统理论教学空洞乏味、师生互动不足、学生学习兴趣不足等问题，在《电工电子学》课程教学中引入雨课堂智慧教学工具，论述了雨课堂从课前、课上和课后三个方面实践的特点和使用方法，实践表明，基于PPT和微信的雨课堂混合传统教学的教学方法可以实现师生实时线上互动，建立多元化科学评价机制，激发学生学习主动性，活跃课堂气氛，提高学生学习积极性，改善本门课程的教学效果。     

“GPS测量与数据处理”课程教学与改革

卫星导航定位是测绘工程中发展较快、应用较广的一项热门技术,“GPS测量与数据处理”课程也一直是测绘专业本科教学的主干课程,具有更新快、理论深、实践性强等特点,对教师授课及学生学习都具有较高的要求。文章对东华理工大学测绘工程专业GPS课程的授课内容、授课方式、实践教学及考核模式等方面存在的问题进行了分析,并提出了相应的教学改革建议。     

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

There are no practical and effective mechanisms to share high-dimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes it less useful for modelling or analytical processing. Insufficient preprocessing may compromise sensitive information and introduce a substantial risk for re-identification of individuals by various stratification techniques. To address this problem, we developed a novel statistical obfuscation method (DataSifter) for on-the-fly de-identification of structured and unstructured sensitive high-dimensional data such as clinical data from electronic health records (EHR). DataSifter provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Simulation results suggest that DataSifter can provide privacy protection while maintaining data utility for different types of outcomes of interest. The application of DataSifter on a large autism dataset provides a realistic demonstration of its promise practical applications.     

听说法研究的历史、现状和反思

听说法产生于19世纪40年代的美国，以结构主义语言学和行为主义心理学为基础，主张在对外汉语教学中注重学生口语能力的培养，遵循“听说领先，读写跟上”的原则，经过反复的句型操练，最终使学习者能够灵活地运用所学语言材料进行交际。听说法20世纪60年代传入中国,率先广泛应用于英语教学中，2000年后则成为对外汉语教学重要的教学方法。文章首先梳理国内关于听说法研究的历史演进，然后利用数据统计对我国听说法研究现状进行分析，包括文献发表数量、研究热点、研究内容、实践案例研究来源等。基于以上梳理和分析，文章对听说法研究的反思，重点关注听说法应用于对外汉语教育实践的相关研究所面临的挑战，并提出相应的研究建议，以期为对外汉语教学的深入研究提供参考，从而推进对外汉语教学在“一带一路”建设中发挥重大作用。     

Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.