两部分因子分析模型的贝叶斯推断

半连续数据在经济和社会科学调查中普遍存在.在分析该类数据时,经典两部分回归模型经常被用来刻画协变量对响应变量可变性的影响.然而,包含协变量并不能完全解释响应变量的可变性.忽略未被观测的数据异质性将导致方差的剧烈波动.在本文中,我们将两部分回归模型推广到两部分因子分析模型.多变量半连续数据未观测的异质性由潜在因子部分来解释.此外,通过引入潜在性因子,多重变量间的相依性也以线性组合方式通过共享因子变量得到刻画.在贝叶斯框架内,我们运用马尔可夫链蒙特卡洛(MCMC)方法来进行后验分析.GIBBS采样器被用于从后验分布中抽取样本.基于模拟的随机样本,未知参数估计和模型评价等统计推断问题获得解决.随机模拟和可卡因使用数据分析等实证结果显示了该方法的有效性和实用性.     

随机权函数半参数回归模型的导方差统计分析

在回归分析中,观测值的方差齐性只是一个基本的假定,在参数、半参数和非参数回归模型中关于异方差检验和估计问题已有很多研究.本文在冉昊和朱忠义(2004)讨论的半参数回归模型的基础上,用随机参数方法,讨论随机权函数半参数回归模型中的异方差检验问题,得到了方差齐性检验Score统计量,同时,当半参数模型存在异方差时,本文还给出了估计方差的方法.     

随机环境下的一类衍生的ARCH模型的极限行为

提出了随机环境下的幂变换门限自回归条件异方差模型,得出了其以几何速率收敛的充分条件.该模型反映了动力系统受随机环境干扰的现象,能更好的拟合现实世界中的诸多实际问题.另一方面,本文推广了自回归条件异方差模型,改善了模型的适应性程度,能够更好地适用于各种不同的金融市场价格行为波动的现象.     

基于空间滞后随机前沿模型技术效率的估计

为了刻画潜在的空间效应,本文提出了一类空间滞后随机前沿模型.在单边误差项分别服从半正态分布、指数分布及截尾正态分布的情况下,我们给出了模型的对数似然函数及迭代步骤.此外,应用JMLS方法给出了单边误差项的条件均值与条件众数,因而得到了相应的技术效率估计.     

基于ARMA-GARCH模型的股票价格分析与预测

利用时间序列模型对大众公用(600635)股票价格进行分析与预测.首先,通过对数据的初步分析,建立ARMA拟合模型;然后,通过模型检验发现模型残差中存在条件异方差性,通过加入GARCH项消除条件异方差性,得到了ARMAGARCH拟合模型;最后实证分析结果表明了模型的有效性与准确性.     

单指标模型异方差检验（英文）

在可加回归模型中,高维回归分析一般采用单指标模型.该模型与参数模型相比更加灵活,同时避免了维数灾难,因为单指标将标准变量向量的维数降低为单变量指标.本文构建了一个带有函数型误差项的单指数回归模型用于检验单指标模型的异方差性.由于回归模型的有效推断要求在存在异方差的情况下考虑异方差,本文提出了检验单指标模型方差不变性的假设.将Levene检验和无限因子水平的方差分析理论结合得到检验统计量用来评估方差同质性.模拟研究显示与已有方法相比,所提检验统计量适用于多种情形.最后将本文的方法应用于分析一组实际数据.     

基于均值-方差准则的保险公司最优投资策略

研究了保险公司在均值-方差准则下的最优投资问题,其中保险公司的盈余过程由带随机扰动的Cramer-Lundberg模型刻画,而且保险公司可将其盈余投资于无风险资产和一种风险资产.利用随机动态规划方法,通过求解相应的HJB方程,得到了均值方差模型的最优投资策略和有效前沿.最后,给出了数值算例说明扰动项对有效前沿的影响.     

基于Jeffreys验前的Bayes方差估计修正

分析了基于Jeffreys验前的经典Bayes方差估计以及考虑验前信息可信度情况下Bayes方差估计存在的问题,在一般情况下,其方差估计要大于验前子样和验后子样的方差,这显然是不合理的.这是采用Jeffreys验前和正态共轭分布假设时存在的固有问题.为了解决这一问题,提出了方差估计的修正公式,经过计算验证,其值在验前子样和验后子样方差之间,说明修正公式是合理的.     

基于均值-VaR的投资组合最优化

利用均值-VaR方法,提出了有交易费用存在时的最优投资组合模型。通过求解均值-方差模型来研究均值-VaR模型的有效前沿,并指出在收益率的分布为正态分布的假设下,均值-VaR模型的有效集是均值-方差有效前沿的子集。有关全局最小VaR的存在性的分析显示在选择VaR的置信水平时必须非常小心。最后给出了应用均值-VaR模型的实例分析。     

非线性随机效应模型的异方差性检验

随机效应模型广泛应用于刻画重复测量数据的特征．在该模型中,随机误差的方差包括受试群体内部及受试群体之间两项方差．Ｚｈａｎｇ和 Ｗｅｉｓｓ ２０００年研究了线性随机效应模型的异方差检验,本文对非线性随机效应模型,分别讨论了群体内、群体间和多变量的异方差性的检验问题,得到了检验的ｓｃｏｒｅ统计量,并讨论了三种情形下,相应的ｓｃｏｒｅ函数之间的关系．最后给出一个数值例子说明上述方法的有用性．     

Stability of pure strategy sampling equilibria

The idea of procedurally rational players was introduced in [4]. Among other procedures, the sampling procedure was proposed. The resulting equilibrium was called a sampling equilibrium. Evolutionary approach to the notion of a sampling equilibrium was developed in [7], where some instability results were proved. Using the concept of first-order stochastic dominance, we introduce the notion of a superior strategy and prove a stability result. The paper was supported by the Polish Government Grant No. KBN 5P03A 025 20. Author wishes to thank an anonymous referee for useful comments and suggestions.     

Automated rejection sampling from product of distributions

Summary  Sampling from probability density functions (pdfs) has become more and more important in many areas of applied science, and has therefore been the subject of great attention. Many sampling procedures proposed allow for approximate or asymptotic sampling. On the other hand, very few methods allow for exact sampling. Direct sampling of standard pdfs is feasible, but sampling of much more complicated pdfs is often required. Rejection sampling allows to exactly sample from univariate pdfs, but has the huge drawback of needing a case-by-case calculation of a comparison function that often reveals as a tremendous chore, whose results dramatically affect the efficiency of the sampling procedure. In this paper, we restrict ourselves to a pdf that is proportional to a product of standard distributions. From there, we show that an automated selection of both the comparison function and the upper bound is possible. Moreover, this choice is performed in order to optimize the sampling efficiency among a range of potential solutions. Finally, the method is illustrated on a few examples.     

Characterization of local sampling sequences for spline subspaces

The sampling theorem is one of the most powerful tools in signal analysis. It says that to recover a function in certain function spaces, it suffices to know the values of the function on a sequence of points. Most of known results, e.g., regular and irregular sampling theorems for band-limited functions, concern global sampling. That is, to recover a function at a point or on an interval, we have to know all the samples which are usually infinitely many. On the other hand, local sampling, which invokes only finite samples to reconstruct a function on a bounded interval, is practically useful since we need only to consider a function on a bounded interval in many cases and computers can process only finite samples. In this paper, we give a characterization of local sampling sequences for spline subspaces, which is equivalent to the celebrated Schönberg-Whitney Theorem and is easy to verify. As applications, we give several local sampling theorems on spline subspaces, which generalize and improve some known results.     

A fast algorithm for balanced sampling

Summary  The cube method (Deville & Tillé 2004) is a large family of algorithms that allows selecting balanced samples with equal or unequal inclusion probabilities. In this paper, we propose a very fast implementation of the cube method. The execution time does not depend on the square of the population size anymore, but only on the population size. Balanced samples can thus be selected in very large populations of several hundreds of thousands of units.     

Counting Contingency Tables via Multistage Markov Chain Monte Carlo

This article describes a multistage Markov chain Monte Carlo (MSMCMC) procedure for estimating the count, , where denotes the set of all a × b contingency tables with specified row and column sums and m total entries. On each stage s = 1, …, r, Hastings–Metropolis (HM) sampling generates states with equilibrium distribution , with inverse-temperature schedule β₁ = 0 < β₂ < ??? < β _r < β _{r + 1} = ∞; nonnegative penalty function H, with global minima H(x) = 0 only for ; and superset , which facilitates sampling by relaxing column sums while maintaining row sums. Two kernels are employed for nominating states. For small β _s , one admits moderate-to-large penalty changes. For large β _s , the other allows only small changes. Neither kernel admits local minima, thus avoiding an impediment to convergence. Preliminary sampling determines when to switch from one kernel to the other to minimize relative error. Cycling through stages in the order r to 1, rather than 1 to r, speeds convergence for large β _s . A comparison of estimates for examples, whose dimensions range from 15 to 24, with exact counts based on Barvinok’s algorithm and estimates based on sequential importance sampling (SIS) favors these alternatives. However, the comparison strongly favors MSMCMC for an example with 64 dimensions. It estimated count with 3.3354 × 10^{? 3} relative error, whereas the exact counting method was unable to produce a result in more than 182 CPU (computational) days of execution, and SIS would have required at least 42 times as much CPU time to generate an estimate with the same relative error. This latter comparison confirms the known limitations of exact counting methods and of SIS for larger-dimensional problems and suggests that MSMCMC may be a suitable alternative. Proofs not given in the article appear in the Appendix in the online supplemental materials.     

一类抽样统计量的性质

通过把多指标变量（X1,X2…,Xk）的值域划分为若干矩形子区域的方式,构造出一类抽样统计量,得出了其具有的优良性质,并在合理分配样本容量的条件下得出了其抽样误差明显好于简单随机抽样误差的结论．     

Irregular sampling for multivariate band-limited functions

In this paper, we present some new algorithms to reconstruct multivariate band-limited functions from irregular sampled values, which allow more arbitrary sampling points and lower sampling densities than known results.     

Reconstruction of functions in spline subspaces from local averages

In this paper, we study the reconstruction of functions in spline subspaces from local averages. We present an average sampling theorem for shift invariant subspaces generated by cardinal B-splines and give the optimal upper bound for the support length of averaging functions. Our result generalizes an earlier result by Aldroubi and Gröchenig.

     

抽样调查的设计与分析(Ⅰ)

本文讨论抽样调查实践中有关设计和数据分析的若干问题．第一部分主要涉及设计问题．     

Local sampling set conditions in weighted shift-invariant signal spaces

How to represent a continuous signal in terms of a discrete sequence is a fundamental problem in sampling theory. Most of the known results concern global sampling in shift-invariant signal spaces. But in fact, the local reconstruction from local samples is one of the most desirable properties for many applications in signal processing, e.g. for implementing real-time reconstruction numerically. However, the local reconstruction problem has not been given much attention. In this article, we find conditions on a finite sampling set X such that at least in principle a continuous signal on a finite interval is uniquely and stably determined by their sampling value on the finite sampling set X in shift-invariant signal spaces.