Subsampling-extrapolation bandwidth selection in bivariate kernel density estimation

This paper focuses on bivariate kernel density estimation that bridges the gap between univariate and multivariate applications. We propose a subsampling-extrapolation bandwidth matrix selector that improves the reliability of the conventional cross-validation method. The proposed procedure combines a U-statistic expression of the mean integrated squared error and asymptotic theory, and can be used in both cases of diagonal bandwidth matrix and unconstrained bandwidth matrix. In the subsampling stage, one takes advantage of the reduced variability of estimating the bandwidth matrix at a smaller subsample size m (m < n); in the extrapolation stage, a simple linear extrapolation is used to remove the incurred bias. Simulation studies reveal that the proposed method reduces the variability of the cross-validation method by about 50% and achieves an expected integrated squared error that is up to 30% smaller than that of the benchmark cross-validation. It shows comparable or improved performance compared to other competitors across six distributions in terms of the expected integrated squared error. We prove that the components of the selected bivariate bandwidth matrix have an asymptotic multivariate normal distribution, and also present the relative rate of convergence of the proposed bandwidth selector.     

Tempered Mittag-Leffler Lévy processes

In this article, we introduce tempered Mittag-Leffler Lévy processes (TMLLP). TMLLP is represented as tempered stable subordinator delayed by a gamma process. Its probability density function and Lévy density are obtained in terms of infinite series and Mittag-Leffler function, respectively. Asymptotic forms of the tails and moments are given. A step-by-step procedure of the parameters estimation and simulation of sample paths is given. We also provide main results available for Mittag-Leffler Lévy processes (MLLP) and some extensions which are not available in a collective way in a single article. Our results generalize and complement the results available on Mittag-Leffler distribution and MLLP in several directions. Further, the asymptotic forms of the moments of the first-exit times of the TMLLP are also discussed.     

Non-parametric estimation of reciprocal coordinate subtangent for right censored dependent scheme

The concept of reciprocal coordinate subtangent (RCST) has been used as a useful tool to study the monotone behavior of a continuous density function and for characterizing probability distributions. In this paper, we propose a non-parametric estimator for RCST based on the censored dependent data. Asymptotic properties of the estimator are established under suitable regularity conditions. A simulation study is carried out to examine the performance of the estimator. The usefulness of the estimator is also examined through a real data.     

Nonparametric beta kernel estimator for long and short memory time series

In this article we introduce a nonparametric estimator of the spectral density by smoothing the periodogram using beta kernel density. The estimator is proved to be bounded for short memory data and diverges at the origin for long memory data. The convergence in probability of the relative error and Monte Carlo simulations show that the proposed estimator automatically adapts to the long- and the short-range dependency of the process. A cross-validation procedure is studied in order to select the nuisance parameter of the estimator. Illustrations on historical as well as most recent returns and absolute returns of the S&P500 index show the performance of the beta kernel estimator. The Canadian Journal of Statistics 48: 582–595; 2020 © 2020 Statistical Society of Canada     

信心、货币政策与中国经济波动关系的统计检验

文章基于时变视角,依据行为经济学理论,构建了一个包含企业家信心、投资者信心、利率、货币增长率、经济增长率和通货膨胀率六变量的TVP-VAR模型,研究信心、货币政策与经济波动之间的时变特征。结果表明:企业家信心和投资者信心能够影响利率和货币增长率,即信心可以通过影响货币政策进而作用于宏观经济。货币增长率和利率能够影响投资者信心和企业家信心,进而可以通过信心影响宏观经济。从时变角度看,企业家信心一单位的正向冲击在整个样本区间内均会促进经济的增长;在短期内投资者信心和货币增长率对经济增长具有促进作用,但是在长期内货币增长率的提高会阻碍经济的增长。     

创业者个体学习、组织学习如何交互影响企业创新行为？——基于整合视角的纵向单案例研究

社会经济的不断发展促使分工愈发精细,对个人与组织间的技术隔离也愈加明显,亟待创业者个体及组织开展跨层次的交互学习以构建组织创新优势。基于此,本文援引学习理论与意义构建视角,从"行动"与"解释"出发,采用纵向单案例研究方法,基于"个体-组织"整合性分析框架,探究了不同创业阶段内跨层次行动主体间的学习互动模型,揭示了其对企业创新的影响路径。研究发现:创业者个体学习与组织学习间的互动,表现为渐变初期个体利用式学习经"自上而下"团队互动主导形成组织单环式行动;而在调整转变期,个体学习与组织学习发生交互影响,包含个体探索式学习经"横向协同式"团队互动主导形成组织双环式行动,以及组织双环式行动转换为单环式之后,经由持续反思与质询,刺激形成创业者个体利用式学习;最后,在发展突变期,个体利用式学习经"制度化统筹式"团队交互合作促成组织单环式行动,同时个体探索式学习经"跨越层次自主式"团队交互激活组织双环式学习。此外,在不同创业阶段,3种互动逻辑与意义构建框架下的创新决定机制呈现出以个体创意激发与扩散、信息整合与匹配以及平衡矛盾与冲突3种不同的影响路径展开的态势。本研究为创业学习理论和创业实践过程贡献了新的启示。     

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.     

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.     

信任对农民创业机会识别的影响

基于信任视角,构建农民创业者信任对创业机会识别的理论分析框架,运用链式多重中介方法,对浙江、安徽、河南和陕西4省880份农民创业者访谈数据进行实证分析,结果发现:信任能显著提高农民创业机会识别;农民创业者信任能通过外部知识获取与创业警觉性对创业机会识别产生间接影响,其作用路径有三条:外部知识获取的部分中介作用、创业警觉性的部分中介作用及外部知识获取与创业警觉性的链式中介作用。因此,政府应完善农民创业相关规章与制度;推进农村文化建设,提高信任水平;增加农民创业知识与技能的供给,培养农民创业者的机会警觉性。     

制度环境、创业能力对国际创业模式选择的影响

基于制度理论和组织能力理论,通过对我国的172家国际新创企业进行问卷调查,运用层次回归分析模型对影响国际创业模式选择的能力因素和制度因素进行实证研究。研究表明,创业能力强的国际新创企业偏好于采用高级的国际创业模式,而且这种创业偏好在制度环境友好的东道国更加明显。