基于MCMC稳态模拟的贝叶斯经验费率厘定信用模型

B黨lmann-Straub model is one of the most famous applications of the Bayesian method for the experience rate making.However,by the traditional B黨lmann-Straub model one cannot get the unbiased posterior estimation of the parameters when there is not sufficient prior information for the structural parameters;What's more,the difficult of computing high dimension numeration limits the application of Bayesian method.This paper introduces the Markov chain Monte Carlo simulaton method based on the Gibbs sampling after analyzing the structure of the B黨lmann-Straub model and sets up the Bayesian credibility model for estimating the predictive risk premium.Also by using the results of the numeration analysis,this paper proves that from this model one can get the posterior distributions of the parameters dynamically and the posterior estimation of the censoring parameters in the situation that exists unknown parameters,as well as improve the precision of the numeration,which can be helpful to find the heterogeneity of the premium.     

在线双向拍卖中的不完全信息博弈仿真研究

双向拍卖机制能较好地解决在线拍卖中如何有效利用网络资源、减轻网络负荷的问题,而构造双向拍卖的报价模型和策略是其重要内容之一。从实验经济学方法的角度,构造了在线双向拍卖中不完全信息博弈下存在的贝叶斯—纳什均衡并求解,通过计算机仿真详细分析了该模型的特点和可行性,为高效利用网络资源、提高在线拍卖效率提供了可行的思路。     

中国企业年金缴费率的模拟分析

企业年金在多支柱养老保险体系中发挥着越来越重要的作用.以个人账户为研究对象,为我国企业年金建立了精算平衡模型.文章在精算平衡模型的基础上,运用模拟的方法,对企业年金的缴费率进行了测算分析.并根据模拟的结果,对2004年5月1日开始实施的<企业年金试行办法>中缴费率的规定进行了讨论,为企业年金的发展和政策制定提供了建议.     

Substance use disorders and referral to treatment in substantiated cases of child maltreatment

Parents with substance use disorders (SUD) require treatment and support in order to provide children with appropriate care and protection. Using the 2012 National Child Abuse and Neglect Data System (NCANDS), this paper analysed 464 313 substantiated child maltreatment reports to determine (i) the proportion and characteristics of reports involving substance abuse; and (ii) the child and caregiver/perpetrator (C/P) characteristics that predicted referral to treatment as recorded in service plans. Findings indicate that 12% (N = 53 234) of maltreatment reports involved C/P SUD. Yet, of those reports, only (19%) (N = 10 088) were referred to substance abuse treatment as part of their service plan, indicating a large gap between those who need treatment and those who receive it. This finding is important given that parental SUD is consistently linked to poorer child outcomes. Amongst other variables, reports indicate that C/P with co‐occurring emotional disturbance were three times more likely to be referred to treatment for SUD as part of service plans. Additional research is needed regarding the characteristics that distinguish C/P who receive referrals for SUD treatment in substantiated cases of child maltreatment.     

Data envelopment analysis in the presence of measurement error: case study from the National Database of Nursing Quality Indicators® (NDNQI®)

Data envelopment analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency [B. Hollingsworth, The measurement of efficiency and productivity of health care delivery. Health Economics 17(10) (2008), pp. 1107–1128], but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized [B.J. Gajewski, R. Lee, M. Bott, U. Piamjariyakul, and R.L. Taunton, On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process. Journal of Applied Statistics 36(9) (2009), pp. 933–944; J. Ruggiero, Data envelopment analysis with stochastic data. Journal of the Operational Research Society 55 (2004), pp. 1008–1012]. We propose to address measurement error systematically using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data from the National Database of Nursing Quality Indicators^® to estimate nursing units’ efficiency. Several external reliability studies inform the posterior distribution of the measurement error on the DEA variables. We will discuss the case of generalizing the approach to situations where an external reliability study is not feasible.     

投资组合动态VaR预测模型和预测精度评价

采用4种Backtesting检验方法,检验22个常态和时变投资组合动态VaR预测模型的风险预测精度,发现GJR_GPD_TV_Copula具有最高的投资组合风险预测精度,GJR_GPD_Copula的拟合、密度预测和组合风险预测精度都要高于GJR_SKST_Copula,且Copula模型的组合风险预测精度分别与拟合精度和密度预测精度存在较弱的正相关关系.     

A latent variable model for analyzing mixed longitudinal (k,l)-inflated count and ordinal responses

A random effects model for analyzing mixed longitudinal count and ordinal data is presented where the count response is inflated in two points (k and l) and an (k,l)-Inflated Power series distribution is used as its distribution. A full likelihood-based approach is used to obtain maximum likelihood estimates of parameters of the model. For data with non-ignorable missing values models with probit model for missing mechanism are used.The dependence between longitudinal sequences of responses and inflation parameters are investigated using a random effects approach. Also, to investigate the correlation between mixed ordinal and count responses of each individuals at each time, a shared random effect is used. In order to assess the performance of the model, a simulation study is performed for a case that the count response has (k,l)-Inflated Binomial distribution. Performance comparisons of count-ordinal random effect model, Zero-Inflated ordinal random effects model and (k,l)-Inflated ordinal random effects model are also given. The model is applied to a real social data set from the first two waves of the national longitudinal study of adolescent to adult health (Add Health study). In this data set, the joint responses are the number of days in a month that each individual smoked as the count response and the general health condition of each individual as the ordinal response. For the count response there is incidence of excess values of 0 and 30.     

Minimally informative distributions with given rank correlation for use in uncertainty analysis

Minimum information bivariate distributions with uniform marginals and a specified rank correlation are studied in this paper. These distributions play an important role in a particular way of modeling dependent random variables which has been used in the computer code UNICORN for carrying out uncertainty analyses. It is shown that these minimum information distributions have a particular form which makes simulation of conditional distributions very simple. Approximations to the continuous distributions are discussed and explicit formulae are determined. Finally a relation is discussed to DAD theorems, and a numerical algorithm is given (which has geometric rate of covergence) for determining the minimum information distributions.     

Bayesian Estimation and Model Choice in Item Response Models

Item response models are essential tools for analyzing results from many educational and psychological tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the difficulty and the discriminatory power of the questions in the test. Some of these models also incorporate a threshold parameter for the probability of the correct response to account for the effect of guessing the correct answer in multiple choice type tests. In this article we consider fitting of such models using the Gibbs sampler. A data augmentation method to analyze a normal-ogive model incorporating a threshold guessing parameter is introduced and compared with a Metropolis-Hastings sampling method. The proposed method is an order of magnitude more efficient than the existing method. Another objective of this paper is to develop Bayesian model choice techniques for model discrimination. A predictive approach based on a variant of the Bayes factor is used and compared with another decision theoretic method which minimizes an expected loss function on the predictive space. A classical model choice technique based on a modified likelihood ratio test statistic is shown as one component of the second criterion. As a consequence the Bayesian methods proposed in this paper are contrasted with the classical approach based on the likelihood ratio test. Several examples are given to illustrate the methods.     

Incomplete categorical data analysis: a bayesian perspective

In this paper the Bayesian analysis of incomplete categorical data under informative general censoring proposed by Paulino and Pereira (1995) is revisited. That analysis is based on Dirichlet priors and can be applied to any missing data pattern. However, the known properties of the posterior distributions are scarce and therefore severe limitations to the posterior computations remain. Here is shown how a Monte Carlo simulation approach based on an alternative parameterisation can be used to overcome the former computational difficulties. The proposed simulation approach makes available the approximate estimation of general parametric functions and can be implemented in a very straightforward way.