球面数据核近邻估计的强相合性

该文绘出了球面数据密度函数的核近邻估计,通过对核估计与近邻估计相互关系的讨论,建立了核近邻估计的逐点强相合性及一致强相合性．     

概率密度函数核估计的Bootstrap逼近

主要研究了密度函数核估计逼近的速度,用Bootstrap方法对核密度进行估计,在适当的条件下,进一步提高了密度核估计Bootstrap逼近的速度,所得到的结果使得密度核估计Bootstrap逼近的速度与密度函数及其导数之间的关系更加的明确.     

回归函数的混合型递归核估计的强相合性

本文讨论了把最小二乘估计和一类递归核估计结合起来所得回归函数的混合型递归核估计的强相合性.     

关于Devroye－Wagner核估计的一个注记

本文研究了Ｄｅｖｒｏｙｅ和Ｗａｇｎｅｒ提出的两个核估计的有偏性，并给出这两个核估计的Ｐａｒｚｅｎ－Ｒｏｓｅｎｂｌａｔｔ核估计有偏性的一个统一的证明．     

关于回归函数核估计的叠对数律

讨论了非参数回归函数的核估计，用核估计误差分解方法，较弱条件下，到了回归函数核估计的叠对数值。     

纵向数据边际模型非参数光滑方法的比较

对于纵向数据边际模型的均值函数, 有很多非参数估计方法, 其中回归样条, 光滑样条, 似乎不相关(SUR)核估计等方法在工作协方差阵正确指定时具有最小的渐近方差. 回归样条的渐近偏差与工作协方差阵无关, 而SUR核估计和光滑样条估计的渐近偏差却依赖于工作协方差阵. 本文主要研究了回归样条, 光滑样条和SUR核估计的效率问题. 通过模拟比较发现回归样条估计的表现比较稳定, 在大多数情况下比光滑样条估计和SUR核估计的效率高.     

不等式约束条件下部分线性回归模型的参数估计

本文研究了不等式约束条件下部分线性回归模型的参数估计问题，利用最优化方法和贝叶斯方法，给出了不等式约束条件下部分线性回归模型的最小二乘核估计和最佳贝叶斯估计，并且证明了在一定条件下，带约束条件的最小二乘核估计在均方误差意义下要优于无约束条件的最小二乘核估计。     

分布自由的回归函数近邻核估计的相合性

本文获得了基于混合，α－混合样本的回归函数核估计，随机窗宽核估计，近邻核估计的强相合性，积分绝对误差的强相合性与平均相合性，所得结果对所有ｘ的分布μ均成立，其中核函数的支撑可以无界，甚至可以是不可积的。     

金融风险管理中VaR度量的光滑估计效率

本文研究了金融风险管理理论中风险价值(VaR)的非参数核光滑估计和经验估计的效率问题.对非独立的时间序列损失/收益样本,在均方误差(MSE)准则的意义下引入亏量的概念,亏量越大表明估计效率越低.并利用亏量对VaR模型的核光滑估计和基于样本分位数的经验估计进行了比较,在理论上证明了VaR模型的核光滑估计优于经验估计.同时,通过计算机模拟证实了理论获得的结论.本文还对国内沪深两市上的证券投资基金进行了实证分析,计算了样本基金的VaR风险度量的经验估计和核光滑估计,并计算了样本基金基于周收益率和VaR估计的风险调整收益(RAROC)值,以此对样本基金的业绩做出了有用的评价.     

全带宽矩阵的核密度估计在核熵成分分析中的应用

核熵成分分析是一种通过保留数据集最多的Renyi熵进行降维和特征提取的数据处理方法,将方法中的Renyi熵利用全带宽矩阵的核概率密度进行了估计,证明了所得熵估计仍是无偏估计,因为新的估计考虑到了不同坐标方向数据的差异性,故在该估计下核熵成分分析对分布不匀衡的数据的特征提取能力得到了提高.     

The sets of quasi-uniform density are not a topology

In this paper we explore a specialized type of Lebesgue density, quasi-uniform density. We observe some of its behavior and prove some of its properties. Specifically, we prove that some basic properties of Lebesgue density points do not hold for quasi-uniform density points, and that quasi-uniform density is not a lower density operator. From this it follows that there is no analogue of the density topology for quasi-uniform density. This research was done under the direction of Professor W. Wilczynski at-’odz University and supported by National Science Foundation Grant #0456135.     

On Bayesian Adaptation

We show that Bayes estimators of an unknown density can adapt to unknown smoothness of the density. We combine prior distributions on each element of a list of log spline density models of different levels of regularity with a prior on the regularity levels to obtain a prior on the union of the models in the list. If the true density of the observations belongs to the model with a given regularity, then the posterior distribution concentrates near this true density at the rate corresponding to this regularity.     

Densities of maximal embedding dimension numerical semigroups

We define the density of a numerical semigroup and study the densities of all the maximal embedding dimension numerical semigroups with a fixed Frobenius number, as well as the possible Frobenius number for a fixed density. We also prove that for a given possible density, in the sense of Wilf’s conjecture, one can find a maximal embedding dimension numerical semigroup with that density.     

Modeling the effects of variable external influences and demographic processes on innovation diffusion

In this paper, a nonlinear mathematical model for innovation diffusion is proposed and analyzed by considering the effects of variable external influences (cumulative marketing efforts) and human population (variable marketing potential) in a society. The change in the population density is caused by various demographic processes such as immigration, emigration, intrinsic growth rate, death rate, etc.Thus, the problem of innovation diffusion is governed by three dynamic variables, namely, non adopters’ density, adopters’ density and the cumulative density of external influences. The model is analyzed by using the stability theory of differential equations and computer simulation.The model analysis shows that the main effect of the increase in cumulative density of external influences is to make the adopter population density reach its equilibrium at a much faster rate. It further shows that the density of adopters’ population increases as the parameters related to increase in non adopters’ population density increase. The effects of various parameters in the model on the nature of existing single equilibrium have also been discussed by using numerical simulation. It is shown that parameters related to the growth of non adopters’ population density have stabilizing effects on the system.     

Improved prediction for a multivariate normal distribution with unknown mean and variance

The prediction problem for a multivariate normal distribution is considered where both mean and variance are unknown. When the Kullback–Leibler loss is used, the Bayesian predictive density based on the right invariant prior, which turns out to be a density of a multivariate t-distribution, is the best invariant and minimax predictive density. In this paper, we introduce an improper shrinkage prior and show that the Bayesian predictive density against the shrinkage prior improves upon the best invariant predictive density when the dimension is greater than or equal to three.     

Complete density topology

Using the notion of the complete convergence of a sequence of measurable functions we introduce the notion of a complete density point of a measurable set. Using complete density points we generate a topology on the real line between ordinary and density topology. An ingenious construction of Lekkerkerker enables us to prove that the simple density topology is strictly stronger than the complete topology.     

ALLEE EFFECTS: POPULATION GROWTH,CRITICAL DENSITY,AND THE CHANCE OF EXTINCTION

This paper develops mathematical models to describe the growth, critical density, and extinction probability in sparse populations experiencing Allee effects. An Allee effect (or depensation) is a situation at low population densities where the per-individual growth rate is an increasing function of population density. A potentially important mechanism causing Allee effects is a shortage of mating encounters in sparse populations. Stochastic models are proposed for predicting the probability of encounter or the frequency of encounter as a function of population density. A negative exponential function is derived as such an encounter function under very general biological assumptions, including random, regular, or aggregated spatial patterns. A rectangular hyperbola function, heretofore used in ecology as the functional response of predator feeding rate to prey density, arises from the negative exponential function when encounter probabilities are assumed heterogeneous among individuals. These encounter functions produce Allee effects when incorporated into population growth models as birth rates. Three types of population models with encounter-limited birth rates are compared: (1) deterministic differential equations, (2) stochastic discrete birth-death processes, and (3) stochastic continuous diffusion processes. The phenomenon of a critical density, a major consequence of Allee effects, manifests itself differently in the different types of models. The critical density is a lower unstable equilibrium in the deterministic differential equation models. For the stochastic discrete birth-death processes considered here, the critical density is an inflection point in the probability of extinction plotted as a function of initial population density. In the continuous diffusion processes, the critical density becomes a local minimum (antimode) in the stationary probability distribution for population density. For both types of stochastic models, a critical density appears as an inflection point in the probability of attaining a small population density (extinction) before attaining a large one. Multiplicative (“environmental”) stochastic noise amplifies Allee effects. Harvesting also amplifies those effects. Though Allee effects are difficult to detect or measure in natural populations, their presence would seriously impact exploitation, management, and preservation of biological resources.     

A Generalized Single Linkage Method for Estimating the Cluster Tree of a Density

The goal of clustering is to detect the presence of distinct groups in a dataset and assign group labels to the observations. Nonparametric clustering is based on the premise that the observations may be regarded as a sample from some underlying density in feature space and that groups correspond to modes of this density. The goal then is to find the modes and assign each observation to the domain of attraction of a mode. The modal structure of a density is summarized by its cluster tree; modes of the density correspond to leaves of the cluster tree. Estimating the cluster tree is the primary goal of nonparametric cluster analysis. We adopt a plug-in approach to cluster tree estimation: estimate the cluster tree of the feature density by the cluster tree of a density estimate. For some density estimates the cluster tree can be computed exactly; for others we have to be content with an approximation. We present a graph-based method that can approximate the cluster tree of any density estimate. Density estimates tend to have spurious modes caused by sampling variability, leading to spurious branches in the graph cluster tree. We propose excess mass as a measure for the size of a branch, reflecting the height of the corresponding peak of the density above the surrounding valley floor as well as its spatial extent. Excess mass can be used as a guide for pruning the graph cluster tree. We point out mathematical and algorithmic connections to single linkage clustering and illustrate our approach on several examples. Supplemental materials for the article, including an R package implementing generalized single linkage clustering, all datasets used in the examples, and R code producing the figures and numerical results, are available online.     

密度函数垂直表示及在非均匀随机数生成中的应用

本文给出密度函数垂直表示法,以及利用该方法产生给定概率密度函数的随机向量的一般算法。