加强统计职业道德的研究和建设

加强统计职业道德的研究和建设李成瑞ＡＢＳＴＲＡＣＴＴｈｅｐｒｏｆｅｓｓｉｏｎａｌｅｔｈｉｃｓｏｆｓｔａｔｉｓｔｉｃｓｉｓａｎｉｍｐｏｒｔａｎｔｓｐｉｒｉｔｐｏｗｅｒｔｏｄｒｉｖｅｔｈｅｄｅｖｅｌｏｐｍｅｎｔｏｆｓｔａｔｉｓｔｉｃａｌｕｎｄｅｒｔａｋｉ...     

一种无偏组合预测方法及其贝叶斯模型

一种无偏组合预测方法及其贝叶斯模型①①本项研究系国家教委优秀年轻教师基金资助项目。曾勇唐小我ＡＢＳＴＲＡＣＴＴｈｉｓｐａｐｅｒｄｉｓｃｕｓｓｅｓｓｅｖｅｒａｌｍｅｔｈｏｄｓｔｏｔｅｓｔｕｎｂｉａｓｅｄｎｅｓｓｉｎｆｏｒｅｃａｓｔｍｏｄｅｌｓ,ｔｈｅｎ...     

成数抽样的比率估计

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养老保险统筹费率的统计研究

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统计调查中回答误差的计量

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我国农产品波动分析

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中国国债发行规模的预测研究

中国国债发行规模的预测研究朱平芳刘弘张人骥ＡＢＳＴＲＡＣＴＯｎｔｈｅｂａｓｉｓｏｆａｎａｌｙｓｉｓｏｎｔｈｅｍｅｃｈａｎｉｓｍｏｆＮａｔｉｏｎａｌＤｅｂｔｓｉｓｓｕｅｉｎＣｈｉｎａ,ｔｈｅｐａｐｅｒｓｅｔｓｕｐｔｈｅｓｔａｔｉｓｔｉｃａｌｍｏｄｅｌｆ...     

顺应统计学发展的历史趋势构建大统计学学科体系

顺应统计学发展的历史趋势构建大统计学学科体系姜万军ＡＢＳＴＲＡＣＴＴｈｅｐａｐｅｒａｔｅｍｐｔｓｔｏｄｉｓｃｕｓｓｔｈｅｉｍｐｏｒｔａｎｃｅｏｆｂｕｉｌｄｉｎｇｔｈｅｓｙｓｔｅｍｏｆｇｅｎｅｒａｌｓｔａｔｉｓｔｉｃｓｆｒｏｍｔｈｅｈｉｓｔｏｒｉｃａｌ...     

关于技术进步与经济增长关系的若干思考

关于技术进步与经济增长关系的若干思考白雪梅赵松山ＡＢＳＴＲＡＣＴＴｗｏｉｓｓｕｅｓｗｅｒｅｄｉｓｃｕｓｓｅｄ．Ｔｈｅｆｉｒｓｔｏｎｅｉｓｈｏｗｔｏｍｅａｓｕｒｅｔｈｅｃｏｎｔｒｉｂｕｔｉｏｎｏｆｔｅｃｈｎｉｃａｌｐｒｏｇｒｅｓｓｔｏｔｈｅｅｃｏｎｏｍ...     

应当关注度量技术供给与需求关系的统计指标

应当关注度量技术供给与需求关系的统计指标丁晓良牛文元ＡＢＳＴＲＡＣＴＴｈｅｐａｐｅｒｄｉｓｃｕｓｓｅｓｆｒｏｍｔｈｒｅｅａｓｐｅｃｔｓｔｈａｔｔｈｅｓｔａｔｉｓｔｉｃａｌｉｎｄｉｃａｔｏｒｓｒｅｆｌｅｃｔｉｎｇｔｈｅｍｅａｓｕｒｅｍｅｎｔｏｆｒｅｌａ...     

Interlitter Response Variability in a Threshold Dose-Response Model

In this article, we extend a previously formulated threshold dose-response model with random litter effects that was applied to a data set from a developmental toxicity study. The dose-response pattern of the data indicates that a threshold dose level may exist. Additionally, there is noticeable variation between the responses across the dose levels. With threshold estimation being critical, the assumed variability structure should adequately model the variation while not taking away from the estimation of the threshold as well as the other parameters directly involved in the dose-response relationship. In the prior formulation, the random effect was modeled assuming identical variation in the interlitter response probabilities across all dose levels, that is, the model had a single parameter to account for the interlitter variability. In this new model, the random effect is modeled as having different response variability across dose levels, that is, multiple interlitter variability parameters. We performed the likelihood ratio test (LRT) to compare our extended model to the previous model. We conducted a simulation study to compare the bias of each model when fit to data generated with the underlying parametric structure of the opposing model. The extended threshold dose-response model with multiple response variation was less biased.     

Prediction in Multilevel Logistic Regression

The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss–Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.     

Bayesian local influence analysis of skew-normal spatial dynamic panel data models

The existing studies on spatial dynamic panel data model (SDPDM) mainly focus on the normality assumption of response variables and random effects. This assumption may be inappropriate in some applications. This paper proposes a new SDPDM by assuming that response variables and random effects follow the multivariate skew-normal distribution. A Markov chain Monte Carlo algorithm is developed to evaluate Bayesian estimates of unknown parameters and random effects in skew-normal SDPDM by combining the Gibbs sampler and the Metropolis–Hastings algorithm. A Bayesian local influence analysis method is developed to simultaneously assess the effect of minor perturbations to the data, priors and sampling distributions. Simulation studies are conducted to investigate the finite-sample performance of the proposed methodologies. An example is illustrated by the proposed methodologies.     

Gaussian Markov random field spatial models in GAMLSS

This paper describes the modelling and fitting of Gaussian Markov random field spatial components within a Generalized AdditiveModel for Location, Scale and Shape (GAMLSS) model. This allows modelling of any or all the parameters of the distribution for the response variable using explanatory variables and spatial effects. The response variable distribution is allowed to be a non-exponential family distribution. A new package developed in R to achieve this is presented. We use Gaussian Markov random fields to model the spatial effect in Munich rent data and explore some features and characteristics of the data. The potential of using spatial analysis within GAMLSS is discussed. We argue that the flexibility of parametric distributions, ability to model all the parameters of the distribution and diagnostic tools of GAMLSS provide an ideal environment for modelling spatial features of data.     

Partial functional linear regression

It is frequently the case that a response will be related to both a vector of finite length and a function-valued random variable as predictor variables. In this paper, we propose new estimators for the parameters of a partial functional linear model which explores the relationship between a scalar response variable and mixed-type predictors. Asymptotic properties of the proposed estimators are established and finite sample behavior is studied through a small simulation experiment.     

改进的随机截尾模型

在随机截尾模型的基础上,为了保护被调查者的隐私,文章提出了一种改进的调查数量敏感性问题的随机化回答方法,并把改进模型的精度与随机截尾模型、随机截尾Warner模型进行了比较。     

Estimation of group means in generalized linear mixed models

In this study, we investigate the concept of the mean response for a treatment group mean as well as its estimation and prediction for generalized linear models with a subject‐wise random effect. Generalized linear models are commonly used to analyze categorical data. The model‐based mean for a treatment group usually estimates the response at the mean covariate. However, the mean response for the treatment group for studied population is at least equally important in the context of clinical trials. New methods were proposed to estimate such a mean response in generalized linear models; however, this has only been done when there are no random effects in the model. We suggest that, in a generalized linear mixed model (GLMM), there are at least two possible definitions of a treatment group mean response that can serve as estimation/prediction targets. The estimation of these treatment group means is important for healthcare professionals to be able to understand the absolute benefit vs risk. For both of these treatment group means, we propose a new set of methods that suggests how to estimate/predict both of them in a GLMMs with a univariate subject‐wise random effect. Our methods also suggest an easy way of constructing corresponding confidence and prediction intervals for both possible treatment group means. Simulations show that proposed confidence and prediction intervals provide correct empirical coverage probability under most circumstances. Proposed methods have also been applied to analyze hypoglycemia data from diabetes clinical trials.     

A Bayesian random effects model for analyzing mixed negative binomial and continuous longitudinal responses

ABSTRACT

A general Bayesian random effects model for analyzing longitudinal mixed correlated continuous and negative binomial responses with and without missing data is presented. This Bayesian model, given some random effects, uses a normal distribution for the continuous response and a negative binomial distribution for the count response. A Markov Chain Monte Carlo sampling algorithm is described for estimating the posterior distribution of the parameters. This Bayesian model is illustrated by a simulation study. For sensitivity analysis to investigate the change of parameter estimates with respect to the perturbation from missing at random to not missing at random assumption, the use of posterior curvature is proposed. The model is applied to a medical data, obtained from an observational study on women, where the correlated responses are the negative binomial response of joint damage and continuous response of body mass index. The simultaneous effects of some covariates on both responses are also investigated.     

Parametric Estimator of Linear Model with Interval-Censored Data

In this article, we consider the estimation of regression parameters in linear model in the presence of interval-censored data. When the response variable is interval-censored, the traditional methods can not be used to estimate the parameters directly. In this article, unbiased transformation is carried out and a new random variable which has the same expectation as the function of the response variable is established. With the regression analysis for the constructed statistic we conclude the estimator by least square method.     

基于偏正态随机效应模型的信度保费

信度模型是非寿险经验费率厘定的主要方法。传统的Buhlmann-Straub信度模型可以表示为随机截距模型,而随机截距模型假设随机效应服从正态分布。在实际的保险损失数据中,部分个体风险的损失可能远远高于总体平均水平,从而使得不同个体风险之间的风险差异呈现右偏特征。在这种情况下,Buhlmann-Straub模型有可能低估高风险的信度保费。本文在随机截距模型中假设随机效应服从偏正态分布,求得了偏正态随机效应假设下的信度保费。可以证明,Buhlmann-Straub信度保费是其特例。模拟分析和实证研究的结果都表明,偏正态随机效应假设下的信度模型可以更好地预测高风险的信度保费,从而改进传统信度模型的保费估计结果。