重新启动WINDOWS的快捷操作

１、功能键法：启动Ｗｉｎｄｏｗｓ时，如果感到启动画面显示的时间太长，在启动时按［Ｅｓｃ］键即可跳过启动画面。如果你即嫌启动画面显示时间长，又懒得按功能键，可以事先用记事本修改Ｍｓｄｏｓ．ｓｙｓ文件，在“Ｏｐｔｉｏｎ”节中加入“Ｌｏｇｏ＝０”即可。再使用 ［开始］→［关闭系统］→［重新启动计算机］时，先按下［Ｓｈｉｆｔ］键，单击“是”，系统可以跳过自检，直接进入 Ｗｉｎｄｉｗｓ。 ２、快捷图标法：即在桌面上创建一个快速重启的快捷方式。在ＤＯＳ模式中创建一个以“ｂａｔ”结尾的批处理文件（内容只有“＠ｅ…     

FIGARCH模型的参数估计与检验

一、问题的提出ＦＩＧＡＲＣＨ模型［１］是Ｂａｉｌｉｅ、Ｂｏｌｅｒｓｌｖｅ、Ｍｉｋｋｌｓｏｎ在Ｅｎｇｌｅ的ＡＲＣＨ模型［２］（１９８２年）的基础上于１９９６年提出来的。该模型比较擅长于反映这类金融资产的异方差特性以及长记忆的变动特性，它的主要应用领域是...     

基于T-S模糊模型的非线性组合预测方法研究

一、引言自从Ｊ．Ｍ．Ｂａｔｅｓ和Ｃ．Ｗ．Ｊ．Ｇｒａｎｇｅｒ首次提出组合预测方法［１］以来,组合预测的研究已经取得很大的进展,文献［２～６］对此有比较详细的综述和评价。根据集结或组合各单项预测模型的方式不同,组合预测一般可分为线性组合预测和非线性组合预...     

统计软件包SPSS8.0在社会经济统计中的应用

ＳＰＳＳ（ＳｔａｔｉｓｔｉｃａｌＰａｃｋａｇｅｆｏｒｔｈｅｓｏｃｉａｌｓｃｉｅｎｃｅ）社会科学统计软件包是世界上著名的统计分析软件之一。它和ＳＡＳ（Ｓｔａ－ｔｉｓｔｉｃａｌＡｎａｌｙｓｉｓＳｙｓｔｅｍ，统计分析系统）及ＢＭＤＰ（Ｂｉｏｍｅｄｉｃａｌｐ...     

SPSS统计分析研究的有利工具

ＰＳＳ（ｓｔａｔｉｓｔｉｃａｌｐａｃｋａｇｅｆｏｒｔｈｅｓｏｃｉａｌｓｃｉｅｎｃｅ）———社会科学软件包是世界上著名的统计分析软件之一。它和ＳＡＳ（ＳｔａｔｉｓｔｉｃａｌＡｎａｌｙｓｉｓＳｙｓｔｅｍ,统计分析系统）、ＢＭＤＡ（ＢｉｏｍｅｄｉｃａｌＰｒｏｇｒａｍｓ,生物医学程序）并称为国际上最有影响的三大统计软件。ＳＰＳＳ目前已应用于经济学、生物学、医学以及工、农、林业、商业和金融等各个领域,在社会科学和自然科学各个领域发挥了巨大作用。ＳＰＳＳ有两个窗口,一个是ＳＰＳＳ数据编辑窗口,另一个是ＳＰＳＳ输…     

组合证券投资风险最小化模型研究

组合证券投资风险最小化模型研究①杨桂元马永开唐小我ＡＢＳＴＲＡＣＴ①国家教委优秀年轻教师基金资助项目。ＢａｓｅｄｏｎｇｅｎｅｒａｌＭｉｎｉｍｉｚｅｄＲｉｓｋＭｏｄｅｌｏｆＣｏｍｂｉｎｅｄＳｅｃｕｒｉｔｉｅｓｉｎｖｅｓｔｍｅｎｔ,ｔｈｅａｕｔｈｏｒｓｓ...     

空间统计学简介

空间统计学简介上海财经大学张尧庭美国国家研究委员会提交政府一个报告（见参考文献［１］），其中列举２７个重要的研究方向，空间统计学（Ｓｐａｔｉａｌｓｔａｔｉｓｔｉｃｓ）是第１７项。空间统计学是７０年代兴起的。在国内可以见到１９８４年汇编的论文集（见［２...     

对损失规律的一种新解释：混合负二项分布及其应用

一、引言在世界上任何一个国家 ,几乎所有的汽车保险人都采用了无赔款优待系统 ,即所谓的ＢＭＳ(Ｂｏｎｕｓ ＭａｌｕｓＳｙｓｔｅｍ)。在该系统中 ,保险公司将根据投保人以往年份的索赔情况调整其续期保费。通常的原则是 ,上一保险年度发生的索赔次数越多 ,次年的续期保费将越高。反之 ,如果上一保险年度没有发生索赔 ,保险公司将降低投保人的续期保费。毫无疑问 ,保险公司调整投保人续期保费的主要目的之一就是为了公平投保人的保费负担 ,使高风险的投保人缴纳相对较高的保险费。但是 ,实证研究结果表明 (参见文献 [1]) ,保险公司目前应用…     

如何实现Linux与Windows资源共享

Ｌｉｎｕｘ是一个优秀的网络操作系统，它可与多种网络集成。在统计系统内很多省的ＷＥＢ服务器采用Ｌｉｎ－ｕｘ－Ａｐａｈｃｅ。由于客户机大量采用Ｗｉｎｄｏｗｓ９Ｘ，让Ｗｉｎ－ｄｏｗｓ客户机共享Ｌｉｎｕｘ系统中的资源成为一个重要的问题。这种Ｌｉｎｕｘ、Ｗｉｎｄｏｗｓ网络集成是通过Ｓａｍｂａ来实现的，Ｗｉｄｎｏｗｓ用户可以方便地通过网上邻居访问Ｌｉｎ－ｕｘ系统上的资源。 Ｓａｍｂａ是一组软件包，使Ｌｉｎｕｘ支持ＳＭＢ协议，该协议由ＴＣＰ／ＩＰ实现，它是ｗｉｎｇｄｏｗｓ网络文件和打印共享的基础，负责处理和使…     

数据库应用新技术—知识发现（KDD）

引言随着信息时代的到来 ,社会、政治、经济等等方面的信息量急剧增长 ,这些信息是一种宝贵的信息资源 ,其背后隐藏着许多重要的信息 ,可以很好地支持人们的决策。目前的数据库系统 (ＤＢＭＳ)提供的查询手段一般只限于一些基本的数据库操作和聚集函数 ,例如ＯＬＡＰ所提供的决策用数据就是这样产生的。因此通过ＤＢＭＳ只能对数据“粗加工” ,并从中获得一些初级的综合信息。数据库中常常蕴涵着规则 (ｒｕｌｅｓ)、规律 (ｒｅｇｕｒｌａｒｉｔｉｅｓ)、论断 (ｉｎｆｅｒｅｎｃｅ)之类的高层次信息或知识 ,这些信息在决策生成的过程中具有重…     

Poisson Regression and the Case-Crossover Design: Similarities and Differences

Poisson regression and case-crossover are frequently used methods to estimate transient risks of environmental exposures such as particulate air pollution on acute events such as mortality. Roughly speaking, a case-crossover design results from a Poisson regression by conditioning on the total number of failures. We show that the case-crossover design is somewhat more generally applicable than Poisson regression. Stratification in the case-crossover design is analogous to Poisson regression with dummy variables, or to a marked Poisson regression. Poisson regression makes it possible to express case-crossover likelihood functions as multinomial likelihoods without making reference to cases, controls, or matching. This derivation avoids the counterintuitive notion of basing inferences on exposures that occur post-failure.     

A new modified Jackknifed estimator for the Poisson regression model

The Poisson regression is very popular in applied researches when analyzing the count data. However, multicollinearity problem arises for the Poisson regression model when the independent variables are highly intercorrelated. Shrinkage estimator is a commonly applied solution to the general problem caused by multicollinearity. Recently, the ridge regression (RR) estimators and some methods for estimating the ridge parameter k in the Poisson regression have been proposed. It has been found that some estimators are better than the commonly used maximum-likelihood (ML) estimator and some other RR estimators. In this study, the modified Jackknifed Poisson ridge regression (MJPR) estimator is proposed to remedy the multicollinearity. A simulation study and a real data example are provided to evaluate the performance of estimators. Both mean-squared error and the percentage relative error are considered as the performance criteria. The simulation study and the real data example results show that the proposed MJPR method outperforms the Poisson ridge regression, Jackknifed Poisson ridge regression and the ML in all of the different situations evaluated in this paper.     

Connections of the Poisson weight function to overdispersion and underdispersion

In this paper, we establish several connections of the Poisson weight function to overdispersion and underdispersion. Specifically, we establish that the logconvexity (logconcavity) of the mean weight function is a necessary and sufficient condition for overdispersion (underdispersion) when the Poisson weight function does not depend on the original Poisson parameter. We also discuss some properties of the weighted Poisson distributions (WPD). We then introduce a notion of pointwise duality between two WPDs and discuss some associated properties. Next, we present some illustrative examples and provide a discussion on various Poisson weight functions used in practice. Finally, some concluding remarks are made.     

Baysian estimation of the parameters in two modified poission distributions

Posterior distributions and moment are derived for the generalized Poisson and the excess zeroes Poisson distributions.Three examples are presented where both maximum likelihood and posterior estimates are given.     

On some models leading to the generalized poisson distribution

A new generalization of the Poisson distribution was given by Consul and Jain (1970, 73). Since then more than twenty papers, written by various researchers, have appeared on this model under the titles of Generalized Poisson Distribution (GPD), Lagrangian Poisson distribution or modified power series distribution. Here the author provides two physical models, based on differential-difference equations, which lead to the GPD. A number of axioms are given for a steady state point process which produce the generalized Poisson process. Also, the GPD is derived as the limiting distribution of the two quasi-binomial distributions based on urn models.     

A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution

Summary.  A useful discrete distribution (the Conway–Maxwell–Poisson distribution) is revived and its statistical and probabilistic properties are introduced and explored. This distribution is a two-parameter extension of the Poisson distribution that generalizes some well-known discrete distributions (Poisson, Bernoulli and geometric). It also leads to the generalization of distributions derived from these discrete distributions (i.e. the binomial and negative binomial distributions). We describe three methods for estimating the parameters of the Conway–Maxwell–Poisson distribution. The first is a fast simple weighted least squares method, which leads to estimates that are sufficiently accurate for practical purposes. The second method, using maximum likelihood, can be used to refine the initial estimates. This method requires iterations and is more computationally intensive. The third estimation method is Bayesian. Using the conjugate prior, the posterior density of the parameters of the Conway–Maxwell–Poisson distribution is easily computed. It is a flexible distribution that can account for overdispersion or underdispersion that is commonly encountered in count data. We also explore two sets of real world data demonstrating the flexibility and elegance of the Conway–Maxwell–Poisson distribution in fitting count data which do not seem to follow the Poisson distribution.     

On characterizing the normal and poisson distributions

Multivariate normal, correlated multivariate Poisson and multiple Poisson distributions are characterized, in the class of exponential-type distributions, by the properties of the linear combinations of the variables, the properties of their cumulants and the recurance relation between the cumulants.     

Nonparametric maximum likelihood estimation of population size based on the counting distribution

Summary.  The paper discusses the estimation of an unknown population size n . Suppose that an identification mechanism can identify n _obs cases. The Horvitz–Thompson estimator of n adjusts this number by the inverse of 1− p ₀, where the latter is the probability of not identifying a case. When repeated counts of identifying the same case are available, we can use the counting distribution for estimating p ₀ to solve the problem. Frequently, the Poisson distribution is used and, more recently, mixtures of Poisson distributions. Maximum likelihood estimation is discussed by means of the EM algorithm. For truncated Poisson mixtures, a nested EM algorithm is suggested and illustrated for several application cases. The algorithmic principles are used to show an inequality, stating that the Horvitz–Thompson estimator of n by using the mixed Poisson model is always at least as large as the estimator by using a homogeneous Poisson model. In turn, if the homogeneous Poisson model is misspecified it will, potentially strongly, underestimate the true population size. Examples from various areas illustrate this finding.     

Heine-euler extensions of the poisson distribution

The paper shows that the Heine and Euler distributions (Benkherouf and Bather, 1988) are members of a family of q-series anologues of the Poisson distribution, with similar probability mass functions, but different restrictions on their parameters, and different modes of genesis and properties. The relationships between the Heine, Euler, pseudo-Euler, Poisson and geometric distributions are explored. Illustrative data sets are discussed.     

Consistent parametric estimation of the intensity of a spatial–temporal point process

We consider conditions under which parametric estimates of the intensity of a spatial–temporal point process are consistent. Although the actual point process being estimated may not be Poisson, an estimate involving maximizing a function that corresponds exactly to the log-likelihood if the process is Poisson is consistent under certain simple conditions. A second estimate based on weighted least squares is also shown to be consistent under quite similar assumptions. The conditions for consistency are simple and easily verified, and examples are provided to illustrate the extent to which consistent estimation may be achieved. An important special case is when the point processes being estimated are in fact Poisson, though other important examples are explored as well.