On proportional odds models

Recently, Marshall and Olkin (Biometrika 84(3):641–652 1997) introduced a family of distributions by adding a new parameter to a survival function. In this paper, we give physical interpretation of the family using odds function. It is shown that the family of distributions satisfies the property of proportional odds function. We, then, develop a generalized family and study its properties. Further, we give various definitions of proportional odds model in the bivariate set up. Based on these, we introduce new families of bivariate distributions and study their properties.     

Logistic discrimination using robust estimators: An influence function approach

Logistic regression is frequently used for classifying observations into two groups. Unfortunately there are often outlying observations in a data set and these might affect the estimated model and the associated classification error rate. In this paper, the authors study the effect of observations in the training sample on the error rate by deriving influence functions. They obtain a general expression for the influence function of the error rate, and they compute it for the maximum likelihood estimator as well as for several robust logistic discrimination procedures. Besides being of interest in their own right, the influence functions are also used to derive asymptotic classification efficiencies of different logistic discrimination rules. The authors also show how influential points can be detected by means of a diagnostic plot based on the values of the influence function     

基于界壳论的电信企业竞争力研究

本文基于界壳理论构建了电信企业多重竞争力界壳模型,通过对该模型的定量分析,能够确定电信企业竞争力界壳的开放度和交换率,企业就可以据此制定出具有较强针对性的竞争力发展策略,提高电信企业决策的科学性。     

论我国个人所得税制的建设与改革

完善个人所得税制是构建我国双主体税制的重要内容之一,也是长期以来税制改革的着力点。1980年全国人大通过《个人所得税法》,之后陆续进行五次修改,为我国立法史所罕见。个人所得税税制模式转型、税率水平与结构的调整、所得扣除的改革、纳税申报制度的完善、立法程序的民主化等始终是个人所得税制改革的重点。     

A Note on Nonparametric Regression with β-Mixing Sequences

Statistical risks of least-squares estimates of a regression function (possibly nonlinear) are studied under a β-mixing setup. The assumptions are minimal and are virtually the same as those of the i.i.d. case. The bounds obtained are optimal, up to a term of order log n. Similar results are also obtained for complexity regularized regression estimates in the presence of penalty functions.     

Estimating the parameters of a bathtub-shaped distribution under progressive type-II censoring

We consider the problem of estimating unknown parameters, reliability function and hazard function of a two parameter bathtub-shaped distribution on the basis of progressive type-II censored sample. The maximum likelihood estimators and Bayes estimators are derived for two unknown parameters, reliability function and hazard function. The Bayes estimators are obtained against squared error, LINEX and entropy loss functions. Also, using the Lindley approximation method we have obtained approximate Bayes estimators against these loss functions. Some numerical comparisons are made among various proposed estimators in terms of their mean square error values and some specific recommendations are given. Finally, two data sets are analyzed to illustrate the proposed methods.     

DYNAMIC FACTOR MODELS

This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated simulators. This class of factor models can represent processes with time varying conditional mean, variance, skewness and excess kurtosis. Applications discussed in the paper include dynamic risk analysis, such as risk in price variations (models with stochastic mean and volatility), extreme risks (models with stochastic tails), risk on asset liquidity (stochastic volatility duration models), and moral hazard in insurance analysis.

We propose estimation procedures for models with the marginal density of the series and factor dynamics parameterized by distinct subsets of parameters. Such a partitioning of the parameter vector found in many applications allows to simplify considerably statistical inference. We develop a two- stage Maximum Likelihood method, called the Finite Memory Maximum Likelihood, which is easy to implement in the presence of multiple factors. We also discuss simulation based estimation, testing, prediction and filtering.     

积分中值定理“中间点”收敛速度的一个估计

利用函数连续模分别给出了第一积分中值定理和第二积分中值定理的“中间点”收敛速度的一个估计。     

Context in the Risk Assessment of Digital Systems

As the use of digital computers for instrumentation and control of safety-critical systems has increased, there has been a growing debate over the issue of whether probabilistic risk assessment techniques can be applied to these systems. This debate has centered on the issue of whether software failures can be modeled probabilistically. This paper describes a context-based approach to software risk assessment that explicitly recognizes the fact that the behavior of software is not probabilistic. The source of the perceived uncertainty in its behavior results from both the input to the software as well as the application and environment in which the software is operating. Failures occur as the result of encountering some context for which the software was not properly designed, as opposed to the software simply failing randomly. The paper elaborates on the concept of error-forcing context as it applies to software. It also illustrates a methodology which utilizes event trees, fault trees, and the Dynamic Flowgraph Methodology (DFM) to identify error-forcing contexts for software in the form of fault tree prime implicants.     

信息非对称下基层商业银行经营效果的系统评价

由于信息非对称,现行基层商业银行考核制度中存在很多道德风险问题。本文提出九项重要的观测指标,并利用层次分析法给出一个模型,以便准确、客观、全面地评价基层银行的经营成果。