关于试行企业增加值核算问题

增加值是国民经济核算的一个基础性指标.各个部门、各个企业的增加值之和,就是国内生产总值(GDP);国内生产总值加上国际间要素收入的增减额,就是国民生产总值(GNP).现在,我们研究和试行增加值核算,主要是为了探讨在各行业的基层企业里建立这一指标的必要性和可能性,通过它把宏观核算和微观核算有机地联系起来,使国民生产总值核算建立在微观的增加值核算这一可靠的基础     

谈建立统计学案例教程的若干问题

本文针对目前统计学案例教学法中案例教程落后的现状,提出了建立与现代统计学相匹配的先进案例教程的必要性并对统计学案例教程的基本特征以及如何建立和完善统计学案例教程作了分析和探讨。     

对大统计思路的认识

最近几年，一些统计学者提出建立一门综合的、具有鲜明层次的学科体系的统计学，以结束社会经济统计学与数理统计学长期对峙的局面。同时，国家有关部门将统计从经济学中分离出来，成为与哲学、数学、经济学等并列的一级学科。这些，都需要对统计学的学科性质、研究对象、研究领域等有一个再认识，重新估量统计学的地位和作用。一、统计发展史，揭示了统计学由实质论科学到方法论科学的转变过程。说明了建立综合统计学有其可能性。人类在很早以前就进行过统计活动，但直到近三百年前，统计才成为一门科学。十七世纪的国势学派（德国）的产生…     

统计学专业发展的历史机遇

文章通过对信息社会中技术结构的特征分析推演出管理创新实践的客观要求,从对统计学学科功效的剖析,阐明了统计学在未来社会的重要地位以及重建统计学专业知识,实现统计技术创新的必要性与历史必然性,从而为统计学的专业定位找到了经济的、社会的、科学的现实依据     

西安辐射能力度量初探

西安位于关中——天水经济区的中心，是关中——天水经济区最大、最发达的中心城市，在区位和功能上恰好成为辐射周边的核心，这种优势决定了西安成为关中——天水经济区经济中心的可能性和必要性。如何评价和正确估量当前西安市对大关中城市群中其余城市的经济辐射力，是摆在我们面前的重要课题。     

对统计学学科体系建设的感悟

一、对几个历史事件的感悟(一 ) 2 0世纪 5 0～ 70年代我国统计学的基本模式2 0世纪 5 0～ 70年代 ,我国的统计学是按照前苏联统计学的模式建立起来的。什么是前苏联统计学模式 ?让我们从统计学的历史谈起。在统计学的历史上 ,先是作为统计学的诞生出现了“政治算术” ;后来又出现了数理统计学。“政治算术”出于用数字描述国情国力的需要 ,先是有一些零散的国情国力调查 ,后来逐渐在世界各国建立了专门负责完成此种任务的政府调查机构 ;数理统计学当其发展到现代阶段以后则进一步应用于自然科学和工程技术科学领域 ,建立了这些领域的统计学…     

我国大统计学科建设中存在的几个问题

1992年11月1日．国家技术监督局发布了中华人民共和国国家标准《学科分类与代码》，把统计学从经济学的二级学科中独立出来，成为与数学、管理学、哲学、经济学等并列的一级学科。在此之前．我国统计学界存在着统计学是一门还是两门的争论．随着统计学一级学科地位的确立，对统计学科建设问题的讨论就显得必要而且是重要的了。为此，国家统计局统计干部培训中心于1994年8月在北京组织召开了全国“大统计学科建设研讨会”，会上就我国大统计学科建设的必要性和可能性进行了讨论，并结合国外统计学科的分类情况讨论了我国统计学科的分类以及统…     

社会经济统计学与数理统计学的比较

多年来,在统计学界关于社会经济统计学与数理统计学的“是非”问题一直争论不休,本文通过对这两种统计学在研究对象、研究范围和内容,以及二者的发展与创新机理和研究理论几个方面的比较,阐述了保持两门统计学并存的必要性。     

关于统计信息商品化的思考

一、统计信息商品化的必要性及可能性（一）统计信息商品化的必要性随着社会的发展,人们对统计信息的需求骤增,如人类要保持可持续发展,就必须对全世界的自然资源状况有更好的分析手段和充足的信息资源;随着经济发     

社会经济统计学的“21世纪议程”——可持续发展统计学刍议

 可持续发展是人类发展的必由之路。1992年联合国环境与发展大会《21世纪议程》的通过和可持续发展战略的开始实施，对统计的实践与理论方法提出了一些列新的要求和挑战。适应新世纪人类发展的需要，把建立可持续发展统计学作为社会经济统计学的“21世纪议程”，这一重大任务已经历史地落在当代统计学家的肩上。     

One-step local quasi-likelihood estimation

Local quasi-likelihood estimation is a useful extension of local least squares methods, but its computational cost and algorithmic convergence problems make the procedure less appealing, particularly when it is iteratively used in methods such as the back-fitting algorithm, cross-validation and bootstrapping. A one-step local quasi-likelihood estimator is introduced to overcome the computational drawbacks of the local quasi-likelihood method. We demonstrate that as long as the initial estimators are reasonably good, the one-step estimator has the same asymptotic behaviour as the local quasi-likelihood method. Our simulation shows that the one-step estimator performs at least as well as the local quasi-likelihood method for a wide range of choices of bandwidths. A data-driven bandwidth selector is proposed for the one-step estimator based on the pre-asymptotic substitution method of Fan and Gijbels. It is then demonstrated that the data-driven one-step local quasi-likelihood estimator performs as well as the maximum local quasi-likelihood estimator by using the ideal optimal bandwidth.     

Reliability assessments by repair shops via maintenance data∗$Fr1:$Sup;∗$esup;$fr:$sup;∗$esup;This is the revised version (containing further applicative examples) of the paper published in the proceedings of the Reliability ‘87 Conference, 14–16 April,Birmimgham, U.K., as anticipated during the oral presentation given then.$ef.

Sometimes, the normal maintenance data recorded by repair shops, working around systems as small as cars or as large as factories, can be used to perform reliability evaluations both in terms of distribution of times to first failure and in terms of expected number of failure in a given time interval. Some examples fiom the field show the use of two estimation procedures proposed for parts and systems respectively, in the context of Non-stationary Stochastic Process. Every attempt has been made to make the whole work practice-oriented, as well as in relation to dimensioning and managing the repair shops themselves.     

Estimating age-specific survival rates of tawny owls--recaptures versus recoveries

We compared estimates of annual survival rates of tawny owls ( Strix aluco ) ringed in southern Finland from several different sampling methods: recoveries of birds ringed as young; recaptures of birds ringed as young; recoveries of birds ringed as adults as well as young; combined recoveries and recaptures of birds ringed as young, and combined recoveries and recaptures of birds ringed as adults and young. From 1979 to 1998, 18 040 young owls were ringed, of which 983 were recaptured as breeders in subsequent years during this period, and 1764 were recovered dead at various locations. In addition, 1751 owls were ringed as adults, of which 612 were later recaptured and 199 were recovered dead. First-year survival rates estimated using only recoveries of birds ringed as young averaged 48%, while apparent survival rates estimated using only recaptures from birds ringed as young averaged 10-13%. Use of combined recapture-recovery models, or supplementary information from recoveries of birds ringed as adults, produced survival estimates of 30-37%. Survival estimates from young-recoveries-only models were biased high, because of violation of the assumption of constant recovery rates with age: birds dying in their first-year were one-third less likely to be found and reported than older birds. In contrast, recaptures-only models confounded emigration with mortality. Despite these differences in mean values, annual fluctuations in estimated first-year survival rates were similar with all models. Estimates of adult survival rates were similar with all models, while those for second-year birds were similar for all models except recaptures-only. These results highlight the potential biases associated with analysing either recaptures or recoveries alone of birds ringed as young, and the benefits of using combined data.     

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.     

Modeling time series data with semi-reflective boundaries

Time series data are increasingly common in many areas of the health sciences, and in some instances, may have natural boundaries serving as performance guidelines or as thresholds associated with adverse outcomes. Such boundaries may be labeled as semi-reflective, in that the time series values have an increased chance of returning towards middle levels as the boundaries are approached, but boundaries can still be breached. In this paper we review a model that was previously proposed for such data and we investigate its statistical properties. Specifically, this model consists of a third-order auto-regressive projection component, parameterized as a constrained linear combination of linear, flat, and quadratic trends, and an error term that uses a logistic regression model for its sign. We describe and compare a previously-proposed estimation method with a modified version thereof, using computer simulations, as well as data examples from heart monitoring and from a driving simulator. We find that the two methods tend to give different results, with the modified technique having lower bias and more accurate confidence intervals than the previously-proposed method.     

Influence diagnostics for polyhazard models in the presence of covariates

In this paper, we present various diagnostic methods for polyhazard models. Polyhazard models are a flexible family for fitting lifetime data. Their main advantage over the single hazard models, such as the Weibull and the log-logistic models, is to include a large amount of nonmonotone hazard shapes, as bathtub and multimodal curves. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. A discussion of the computation of the likelihood displacement as well as the normal curvature in the local influence method are presented. Finally, an example with real data is given for illustration.     

Stochastic population forecasts for the United States: beyond high,medium, and low

"This article presents and implements a new method for making stochastic population forecasts that provide consistent probability intervals. We blend mathematical demography and statistical time series methods to estimate stochastic models of fertility and mortality based on U.S. data back to 1900 and then use the theory of random-matrix products to forecast various demographic measures and their associated probability intervals to the year 2065. Our expected total population sizes agree quite closely with the Census medium projections, and our 95 percent probability intervals are close to the Census high and low scenarios. But Census intervals in 2065 for ages 65+ are nearly three times as broad as ours, and for 85+ are nearly twice as broad. In contrast, our intervals for the total dependency and youth dependency ratios are more than twice as broad as theirs, and our ratio for the elderly dependency ratio is 12 times as great as theirs. These items have major implications for policy, and these contrasting indications of uncertainty clearly show the limitations of the conventional scenario-based methods."     

国外统计科学研究现状评介

The paper introduces and evaluates features of statistical research work abroad from research personnel, research institution and research contents and results as well as research trend; from the hot issues discussed in statistical conference abroad and the treatises published; and also from the motive force as well as the trend of statistical research..     

Variable selection by ensembles for the Cox model

Using Cox regression as the main platform, we study the ensemble approach for variable selection. We use a popular real-data example as well as simulated data with various censoring levels to illustrate the usefulness of the ensemble approach, and study the nature of these ensembles in terms of their strength and diversity. By relating these characteristics to the ensemble's selection accuracy, we provide useful insights for how to choose among different ensemble strategies, as well as guidelines for thinking about how to design more effective ensembles.     

Generalized-order statistics with random indices

In this paper, we study the asymptotic behavior of general sequence of extreme, intermediate and central generalized-order statistics (gos), as well as dual generalized-order statistics (dgos), which are connected asymptotically with some regularly varying functions. Moreover, the limit distribution functions of gos, as well as dgos, with random indices, are obtained under general conditions.