统计数据质量评估方法研究述评

数据质量评估是统计数据质量管理的重要环节。统计数据质量的评估方法有逻辑关系检验法、计量模型分析法、核算数据重估法、统计分布检验法、调查误差评估法以及多维评估法六个类别,在详细讨论其各自的评估过程、适用性及其优缺点的基础上,按照评估维度和评估方式对各种统计数据质量的评估方法进行再归类,指出评估方法的进一步研究应该围绕计量模型分析法、核算数据重估法中的物量指数重估法、调查误差评估法和多维评估法等方向展开。     

A comparison of statistical methods for combining relative bioactivities from parallel line bioassays

This paper compares the ordinary unweighted average, weighted average, and maximum likelihood methods for estimating a common bioactivity from multiple parallel line bioassays. Some of these or similar methods are also used in meta‐analysis. Based on a simulation study, these methods are assessed by comparing coverage probabilities of the true relative bioactivity and the length of the confidence intervals computed for these methods. The ordinary unweighted average method outperforms all statistical methods by consistently giving the best coverage probability but with somewhat wider confidence intervals. The weighted average methods give good coverage and smaller confidence intervals when combining homogeneous bioactivities. For heterogeneous bioactivities, these methods work well when a liberal significance level for testing homogeneity of bioactivities is used. The maximum likelihood methods gave good coverage when homogeneous bioactivities were considered. Overall, the preferred methods are the ordinary unweighted average and two weighted average methods that were specifically developed for bioassays. Copyright © 2013 John Wiley & Sons, Ltd.     

A comparative study of smoothing procedures for ordered categorical data

Methods for estimating probabilities on sample spaces for ordered-categorical variables are surveyed. The methods all involve smoothing the relative frequencies in manners which recognise the ordering among categories. Approaches of this type include convex smoothing, weighting-function and kernel-based methods, near neighbour methods, Bayes-based methods and penalized minimum-distance methods. The relationships among the methods are brought out, application is made to a medical example and a simulation study is reported which compares the methods on univariate and bivariate examples. Links with smoothing procedures in other contexts are indicated.     

Testing the equality means of several log-normal distributions

Researches propose various methods for comparing the means of two log-normal distributions. Some of these methods have been recently extended to test the equality means of several log-normal populations. Investigations show that none of the established methods is satisfactory. In this article, we provide three methods based on the computational approach test, which is a parametric bootstrap approach, for testing the means of several log-normal distributions. Further, we compare our methods with the existing methods through Monte Carlo simulation. The numerical results show that the Type I errors of these procedures are satisfactory regardless of the sample size, number of populations, and the true parameters. Finally, we explain the considered methods by real examples.     

Estimating the number of factors to include in a high-dimensional multivariate bilinear model

We present two new statistics for estimating the number of factors underlying in a multivariate system. One of the two new methods, the original NUMFACT, has been used in high profile environmental studies. The two new methods are first explained from a geometrical viewpoint. We then present an algebraic development and asymptotic cutoff points. Next we present a simulation study that shows that for skewed data the new methods are typically superior to traditional methods and for normally distributed data the new methods are competitive to the best of the traditional methods. We finally show how the methods compare by using two environmental data sets.     

社会核算矩阵不同更新方法的比较研究

更新社会核算矩阵(Social Accounting Matrix, SAM)作为一种非调查技术一直受到普遍重视。本文用十种更新方法,包括RAS法、交叉熵法、广义交叉熵法等,以江苏省1997年的宏观和细化SAM为初始表,将其分别更新到2002年的宏观和细化SAM。我们通过保号检验、方向检验和接近程度检验比较了各种方法的更新结果。研究结论显示：第一,基于商的更新方法要优于基于距离的更新方法;第二,保号类方法不仅具有保号的功能,而且在方向检验和接近度检验中有着良好表现;第三,对某些重要系数采用适当的外生估算方式,可以提高SAM更新的精度。     

Regression Methods for Combining Multiple Classifiers

As no single classification method outperforms other classification methods under all circumstances, decision-makers may solve a classification problem using several classification methods and examine their performance for classification purposes in the learning set. Based on this performance, better classification methods might be adopted and poor methods might be avoided. However, which single classification method is the best to predict the classification of new observations is still not clear, especially when some methods offer similar classification performance in the learning set. In this article we present various regression and classical methods, which combine several classification methods to predict the classification of new observations. The quality of the combined classifiers is examined on some real data. Nonparametric regression is the best method of combining classifiers.     

教学质量评估的两种方法

文章研究了教学质量评估中两种定量分析的方法:马尔可夫链评估法、奖罚权系数矩阵法,给出了这两种方法的理论依据及实施程序。用该方法对哈尔滨工程大学2002级大学数学(高等数学→线性代数→概率论与数理统计)教学质量进行了分析,指出该方法较之其他教学质量评估法更显合理。     

Methods for national population forecasts: a review

"Three widely used classes of methods for forecasting national populations are reviewed: demographic accounting/cohort-component methods for long-range projections, statistical time series methods for short-range forecasts, and structural modeling methods for the simulation and forecasting of the effects of policy changes. In each case, the major characteristics, strengths, and weaknesses of the methods are described. Factors that place intrinsic limits on the accuracy of population forecasts are articulated. Promising lines of additional research by statisticians and demographers are identified for each class of methods and for population forecasting generally."     

Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches

Two classes of methods properly account for clustering of data: design-based methods and model-based methods. Estimates from both methods have been shown to be approximately equal with large samples. However, both classes are known to produce biased standard error estimates with small samples. This paper compares the bias of standard errors and statistical power of marginal effects for generalized estimating equations (a design-based method) and generalized/linear mixed effects models (model-based methods) with small sample sizes via a simulation study. Provided that the distributional assumptions are met, model-based methods produced the least-biased standard error estimates and greater relative statistical power.     

A resisting gross errors capability study of robust estimation of unary linear regression method

Robust estimation methods are often used to eliminate or weaken the influences of gross errors on parameter estimation. However, different robust estimation methods may have different capabilities in eliminating or weakening gross errors. Taking unary linear regression as example, simulation experiments are used to compare 14 frequently used robust estimation methods. The current article summarizes the common characteristics and rules of the robust estimation methods. Finally, we confirm several relatively more efficient methods for unary linear regression.     

A review and discussion of prospective statistical surveillance in public health

Summary. A review of methods suggested in the literature for sequential detection of changes in public health surveillance data is presented. Many researchers have noted the need for prospective methods. In recent years there has been an increased interest in both the statistical and the epidemiological literature concerning this type of problem. However, most of the vast literature in public health monitoring deals with retrospective methods, especially spatial methods. Evaluations with respect to the statistical properties of interest for prospective surveillance are rare. The special aspects of prospective statistical surveillance and different ways of evaluating such methods are described. Attention is given to methods that include only the time domain as well as methods for detection where observations have a spatial structure. In the case of surveillance of a change in a Poisson process the likelihood ratio method and the Shiryaev–Roberts method are derived.     

The Effect of Methods for Handling Missing Values on the Performance of the MEWMA Control Chart

This article is concerned with the effect of the methods for handling missing values in multivariate control charts. We discuss the complete case, mean substitution, regression, stochastic regression, and the expectation–maximization algorithm methods for handling missing values. Estimates of mean vector and variance–covariance matrix from the treated data set are used to build the multivariate exponentially weighted moving average (MEWMA) control chart. Based on a Monte Carlo simulation study, the performance of each of the five methods is investigated in terms of its ability to obtain the nominal in-control and out-of-control average run length (ARL). We consider three sample sizes, five levels of the percentage of missing values, and three types of variable numbers. Our simulation results show that imputation methods produce better performance than case deletion methods. The regression-based imputation methods have the best overall performance among all the competing methods.     

Group variable selection for data with dependent structures

Variable selection methods have been widely used in the analysis of high-dimensional data, for example, gene expression microarray data and single nucleotide polymorphism data. A special feature of the genomic data is that genes participating in a common metabolic pathway or sharing a similar biological function tend to have high correlations. The collinearity naturally embedded in these data requires special handling, which cannot be provided by existing variable selection methods. In this paper, we propose a set of new methods to select variables in correlated data. The new methods follow the forward selection procedure of least angle regression (LARS) but conduct grouping and selecting at the same time. The methods specially work when no prior information on group structures of data is available. Simulations and real examples show that our proposed methods often outperform the existing variable selection methods, including LARS and elastic net, in terms of both reducing prediction error and preserving sparsity of representation.     

Multilevel and nonlinear panel data models

Summary This paper presents a selective survey on panel data methods. The focus is on new developments. In particular, linear multilevel models, specific nonlinear, nonparametric and semiparametric models are at the center of the survey. In contrast to linear models there do not exist unified methods for nonlinear approaches. In this case conditional maximum likelihood methods dominate for fixed effects models. Under random effects assumptions it is sometimes possible to employ conventional maximum likelihood methods using Gaussian quadrature to reduce a T-dimensional integral. Alternatives are generalized methods of moments and simulated estimators. If the nonlinear function is not exactly known, nonparametric or semiparametric methods should be preferred. Helpful comments and suggestions from an unknown referee are gratefully acknowledged.     

MCMC方法的发展与现代贝叶斯的复兴——纪念贝叶斯定理发现250周年

信息科学的进步如何影响统计学理论与方法的发展,是21世纪大数据时代统计学面临的重要问题。回顾20世纪40年代MCMC方法的起源与发展,基于千岛湖植物考察与R软件直观性解释Metropolis–Hastings算法,讨论MCMC方法的发展对现代贝叶斯统计复兴具有至关重要的作用。基于云计算随机模拟,统计学与信息科学密切结合、贝叶斯与频率统计相互交融的统计分析新范式,可能成为大数据研究的新思路。     

主成分与因子分析中指标同趋势化方法探讨

样本主成分和样本因子分析法已成为一种最主要的综合评价方法之一,指标变量的同趋势化是运用该方法的重要步骤。文章总结了主成分与因子分析中指标同趋势化的具体方法,论述了这些方法对综合评价的影响,并指出了这些方法的适用条件。     

Better alternatives to current methods of scaling and weighting data for cluster analysis

Scaling of multivariate data prior to cluster analysis is important as a preprocessing step. Currently there are methods for doing this. This paper proposes some alternatives, which are particularly directed at helping reveal cluster structures in data. These methods are applied to simulated and real data sets and their performances are compared to some currently used methods. The results indicate that, in many situations, the new methods are much better than the most popular method, called autoscaling. In the most challenging clustering example considered, their performances, while poor, are no worse than all the currently used methods.     

Multiple comparisons with a control under heteroscedasticity

This article investigates three procedures on the multiple comparisons with a control in the presence of unequal error variances. The advantages of the proposed methods are illustrated through two examples. The performance of the proposed methods and other alternative methods is compared by simulation studies. The results show that the typical methods assuming equal variance will have inflated error rate and may lead to erroneous inference when the equal variance assumption fails. In addition, the simulation study shows that the proposed approaches always control the family-wise error rate at a specified nominal level α, while some established methods are liberal and have inflated error rate in some scenarios.