基于核密度估计的互联网金融产品收益率对比分析

针对当下互联网热门金融产品余额宝和财富宝,选取二者7日年化收益率作为对比数据,在验证了收益率均不服从正态分布的情况下,使用非参数估计方法——核密度估计对数据分布特征进行研究,并选择Epan核,使用Silverman嵌入估计进行核密度估计;结果表明:余额宝与财富宝的收益率均服从双峰分布,且财富宝收益率明显高于余额宝收益率,但更易产生收益率极端值;此研究可为互联网金融市场的风险度量等模型在收益率分布方面提供更具说服力的参考。

基于交替条件期望的短期负荷概率密度预测

现今电力系统短期负荷预测的点预测方法多种多样。为弥补传统点预测方法结果过于单一的问题,结合温度及日历序列因素对负荷的影响,提出基于交替条件期望(ACE)的短期电力负荷概率密度预测方法。以温度及日期序列为负荷影响因子,建立基于交替条件期望理论的非参数回归模型,计算历史负荷与影响因子的非线性回归方程;考虑日类型及星期类型等多重因素,利用模糊聚类方法选取相似日;以回归方程为基础,根据所得相似日及预测日影响因子,进行负荷回归值计算,并利用核密度估计(KDE),得到负荷概率密度曲线。利用某市的实测数据,进行负荷概率密度曲线预测,并选取概率密度众数作为负荷点预测值,与其他负荷预测方法结果相比较,仿真结果表明该方法的精度高、可靠性好。

我国优秀运动员空间分布特征及影响因素——基于Arc-GIS视角

以2012-2018年4届冬、夏两季奥运会832名参赛运动员为研究样本,采用数理统计和GIS空间分析法,从关联、密度、色彩等级对其运动员空间分布特征和影响要素进行系统分析。研究结果表明:我国优秀运动员空间分布表现出“两极分化”的非均衡性,呈“簇”“点”相间的团状格局,且东部沿海与中部省域居多、西北部省域稀疏;形成以辽宁、京津冀、长三角地区为核心区的3个高密度核心圈,以黑、鲁、川、粤、闽、豫为核心区的6个次级核心圈,以及围绕鄂、湘、陕、甘、新为点的多个小核心圈;省域间各异的自然地貌环境、历史文化要素支配竞技项目类型布局;区域经济水平、人资后储量强弱程度及个人心理动机直接决定运动员流动及分布格局。

ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION

One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships.

Multimodal background model with noise and shadow suppression for moving object detection

A statistical multimodal background model was described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects, and shadows suppression was provided. The background samples were chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation was used to estimate the probability density function of background intensity. Pixel's neighbor information was considered to remove noise due to camera jitter and small motion in the scene. The hue-max-min-diff color information was used to detect and suppress moving cast shadows. The effectiveness of the proposed method in the foreground segmentation was demonstrated in the traffic surveillance application.

Mean shift algorithm based on fusion model for head tracking

To solve the mismatch between the candidate model and the reference model caused by the time change of the tracked head, a novel mean shift algorithm based on a fusion model is provided. A fusion model is employed to describe the tracked head by sampling the models of the fore-head and the back-head under different situations. Thus the fusion head reference model is represented by the color distribution estimated from both the fore- head and the back-head. The proposed tracking system is efficient and it is easy to realize the goal of continual tracking of the head by using the fusion model. The results show that the new tracker is robust up to a 360°rotation of the head on a cluttered background and the tracking precision is improved.

Data driven particle size estimation of hematite grinding process using stochastic configuration network with robust technique

As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.

Robust background subtraction in traffic video sequence

For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.

Road Centrality and Landscape Spatial Patterns in Wuhan Metropolitan Area,China

Road network is a corridor system that interacts with surrounding landscapes,and understanding their interaction helps to develop an optimal plan for sustainable transportation and land use.This study investigates the relationships between road centrality and landscape patterns in the Wuhan Metropolitan Area,China.The densities of centrality measures,including closeness,betweenness,and straightness,are calculated by kernel density estimation(KDE).The landscape patterns are characterized by four landscape metrics,including percentage of landscape(PLAND),Shannon′s diversity index(SHDI),mean patch size(MPS),and mean shape index(MSI).Spearman rank correlation analysis is then used to quantify their relationships at both landscape and class levels.The results show that the centrality measures can reflect the hierarchy of road network as they associate with road grade.Further analysis exhibit that as centrality densities increase,the whole landscape becomes more fragmented and regular.At the class level,the forest gradually decreases and becomes fragmented,while the construction land increases and turns to more compact.Therefore,these findings indicate that the ability and potential applications of centrality densities estimated by KDE in quantifying the relationships between roads and landscapes,can provide detailed information and valuable guidance for transportation and land-use planning as well as a new insight into ecological effects of roads.

Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking

In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.

AN EFFECTIVE IMAGE RETRIEVAL METHOD BASED ON KERNEL DENSITY ESTIMATION OF COLLAGE ERROR AND MOMENT INVARIANTS

In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The proposed method is called CHK (KDE of Collage error and Hu moment) and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average Retrieval Rate (ARR) can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time.

Spatial Agglomeration of Exhibition Enterprises on a Regional Scale in China

During the past two decades, the exhibition industry in China has been developing rapidly and has become an important part of the modern service industry, particularly the agglomeration characteristics of exhibition enterprises highlighted on the regional scale. Although the development of theoretical research on the western exhibition industry has taken place over time, the spatial perspective has not been at the centre of attention so far. This paper aims to fill this gap and report on the agglomeration characteristics of exhibition enterprises and their influential factors. Based on data about exhibition enterprises in the Pearl River Delta(PRD) during 1991–2013, using the Ripley K function analysis and kernel density estimation, this research identifies that: 1) the exhibition enterprise on the regional scale is significantly characterized by spatial agglomeration, and the agglomeration density and scale are continuously increasing; 2) the spatial pattern of agglomeration has developed from a single-center to multi-center form. Meanwhile, this paper profiles the factors influencing the spatial agglomeration of exhibition enterprises by selecting the panel data of nine cities in the PRD in 1999, 2002, 2006 and 2013. The results show that market capacity, urban informatization level and exhibition venues significantly influence the location choice of exhibition enterprises. Among them, the market capacity is a variable that exerts a far greater impact than other factors do.

Strong Convergence Rates of Double Kernel Estimates of Conditional Desity Under Stationary Sequences

In the paper,we study the strong convergence rates of double kernel estimates of conditional density under stationary sequences.

Calculation of One-Valued Control Limits by Control Chart of Angles

The data we use to express angle or direction are entitled directional data. In a plan right angled coordinate system the traditional control chart can’t solve the quality control problem which the characteristic value is angle. This paper analyses and calculates the one valued control limits by control chart of angles.

Population survey and conservation assessment of the globally threa- tened cheer pheasant （Catreus wallicht） in Jhelum Valley, Azad Kas- hmir, Pakistan

The cheer pheasant Catreus wallichi is a globally threatened species that inhabits the western Himalayas. Though it is well established that the species is threatened and its numbers declining, updated definitive estimates are lacking, so in 2011, we conducted a survey to assess the density, population size, and threats to the species in Jhelum valley, Azad Kashmir, which holds the largest known population of cheer pheasants in Pakistan. We conducted dawn call count surveys at 17 points clustered in three survey zones of the valley, 11 of which had earlier been used for a 2002-2003 survey of the birds. Over the course of our survey, 113 birds were recorded. Mean density of cheer pheasant in the valley was estimated at 11.8±6.47 pairs per km2, with significant differences in terms of both counts and estimated density of cheer were significantly different across the three survey zones, with the highest in the Chinari region and the lowest, that is the area with no recorded sightings of the pheasants, in Gari Doppata. The total breeding population of cheer pheasants is estimated to be some 2 490 pairs, though this does not consider the actual area of occupancy in the study area. On the whole, more cheer pheasants were recorded in this survey than from the same points in 2002-2003, indicating some success in population growth. Unfortunately, increasing human settlement, fires, livestock grazing, hunting, and the collection of non-timber forest products continue to threaten the population of cheer in the Jhelum valley. To mitigate these potential impacts, some degree of site protection should be required for the conservation of cheer pheasants in Pakistan, and more effective monitoring of the species is clearly needed.

Strong Consistency for the Kernal Estimates of the Random Window Width of the Density Function and its Derivatives Under Φ-Mixing Samples

In the paper,we study the strong uniform consistency for the kernal estimates of random window w■th of density function and its derivatives under the condition that the sequence{X_n}of the ■ are the identically Φ-mixing random variabks.

Consistency of kernel density estimators for causal processes

Using the blocking techniques and m-dependent methods,the asymptotic behavior of kernel density estimators for a class of stationary processes,which includes some nonlinear time series models,is investigated.First,the pointwise and uniformly weak convergence rates of the deviation of kernel density estimator with respect to its mean(and the true density function)are derived.Secondly,the corresponding strong convergence rates are investigated.It is showed,under mild conditions on the kernel functions and bandwidths,that the optimal rates for the i.i.d.density models are also optimal for these processes.

An Independent Component Analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation

A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.