VisIOT:经济产业结构关联可视分析

随着经济统计范畴的精细化以及统计过程的规范化，能够表现产业部门关联关系的投入产出表（Input-Output Table，IOT）数据日益呈现复杂的结构特性。传统的统计分析软件和方法形式单一且传达信息有限，面对结构关系复杂且动态演化的IOT数据，难以有效分析和探索其中复杂的关联模式和时序变化特征。为此，本文设计面对IOT数据分析的经济产业结构关联特征可视化工具——VisIOT。首先设计双向力导向图描述国民经济结构关联网络，并对网络中的顶点和边进行属性映射；然后构建时序矩阵图，直观地展示IOT数据差异，并按照时间顺序依次嵌入时序IOT数据；其次利用部门间的经济技术联系优化模块度算法，发掘经济产业结构关联网络中隐含的社区特征，有效支持关联紧密的社区结构的交互式分析和提取；再次设计社区时序演变图展示社区结构特征的时序演化规律，借助交叉优化算法和前后向的扫描算法，优化部门排列顺序，减少部门交叉，帮助用户有效捕捉社区结构的稳定性；最后有效设计交互方案关联可视化界面，实现经济产业结构关联可视分析系统。本文利用真实的IOT数据进行实例分析与验证，结果表明本文设计的VisIOT系统能够帮助用户快速识别和感知IOT数据中隐含的关联特征及其时序变化规律。     

Pattern Matching

An important subtask of the pattern discovery process is pattern matching, where the pattern sought is already known and we want to determine how often and where it occurs in a sequence. In this paper we review the most practical techniques to find patterns of different kinds. We show how regular expressions can be searched for with general techniques, and how simpler patterns can be dealt with more simply and efficiently. We consider exact as well as approximate pattern matching. Also we cover both sequential searching, where the sequence cannot be preprocessed, and indexed searching, where we have a data structure built over the sequence to speed up the search.     

Comparing Joint GQL Estimation and GMM Adaptive Estimation in COM-Poisson Longitudinal Regression Model

It is of scientific interest to study the application of COM-Poisson model to the case of longitudinal response data, the analysis of which is quite challenging due to the fact that longitudinal responses of a subject are correlated and the correlation pattern is usually unknown. In this article, we extend the COM-Poisson GLM to the generalized linear longitudinal model. We also develop a joint generalized quasi-likelihood estimating equation approach based on a stationary autocorrelation structure for the repeated count data. We further compare the performance of this estimation method with that of Generalized Method of Moments through a simulation study.     

产业结构优化研究

中国的工业化进程在实现数量上的高速增长和产业规模大幅扩张的同时,其增长方式、增长质量和效率已然成为新时期的重大问题,对我国产业结构优化进程的考察无疑在上述方面具有重要意义。本文在清楚辨析产业结构升级和产业结构优化的基础上,对我国“十一五”期间产业结构的优化进程进行分析。结论表明,我国产业结构具有较明显的高度化发展态势;但是产业结构合理化进程并不明显,特别是产业结构的高度化和合理化的进程并不一致,产业结构调整并没有取得“对经济发展方式转变具有促进作用”的优化效果。在通往新型工业化的道路上,经济结构调整和增长方式的转变仍然是我们亟需解决的重要问题。     

国际生产网络视角下的增加值贸易结构分析与主导因素识别——以中美双边贸易为例

本文利用世界投入产出表测度了2001—2014年中美双边增加值贸易结构。结果表明,中国在双边贸易中的单位出口获利能力低于美国,国际分工地位的差异使得两国的贸易结构显示出较强的互补性。在对增加值贸易构成分解的基础上,本文识别并分析了国际生产网络体系下影响出口国内增加值的主导因素及其作用机制。研究发现,网络外最终需求、生产网络上游因素分别是推动中国和美国出口国内增加值扩大的主导因素。此外,产业层面的研究结果显示,中国高技术制造业出口国内增加值的扩大主要是由生产网络上游因素驱动的,生产网络外最终需求则是中国传统制造业出口国内增加值扩张的重要推动力。中国在扩大对外开放的同时应与国际生产网络中的上下游经济体形成多元化匹配,进而加快形成以国内大循环为主体、国内国际双循环相互促进的新发展格局。     

Bayesian image analysis with Markov chain Monte Carlo and coloured continuum triangulation models

It is now possible to carry out Bayesian image segmentation from a continuum parametric model with an unknown number of regions. However, few suitable parametric models exist. We set out to model processes which have realizations that are naturally described by coloured planar triangulations. Triangulations are already used, to represent image structure in machine vision, and in finite element analysis, for domain decomposition. However, no normalizable parametric model, with realizations that are coloured triangulations, has been specified to date. We show how this must be done, and in particular we prove that a normalizable measure on the space of triangulations in the interior of a fixed simple polygon derives from a Poisson point process of vertices. We show how such models may be analysed by using Markov chain Monte Carlo methods and we present two case-studies, including convergence analysis.     

A spatial analysis on Italian unemployment differences

Using spatial econometric models, this paper focuses attention on the spatial structure of provincial unemployment disparities of Italian provinces for the year 2003. On the basis of findings from the economic literature and of the available socio-economic data, various model specifications including supply- and demand-side variables are tested. Further we use ESDA analysis as equivalent to integration analysis on time series; therefore it is applied on each variable, dependent and independent, involved in the statistical model. The suggestions of ESDA lead us to the most adequate statistical model, which estimates indicate that there is a significant degree of neighbouring effect (i.e. positive spatial correlation) among labour markets at the provincial level in Italy; this effect is present notwithstanding we controlled for local characteristics. The unemployment shows a polarized spatial pattern that is strongly connected to labour demand and to a much lesser extent to the share of young population and economic structural composition.     

Variable selection for high-dimensional generalized linear model with block-missing data

In modern scientific research, multiblock missing data emerges with synthesizing information across multiple studies. However, existing imputation methods for handling block-wise missing data either focus on the single-block missing pattern or heavily rely on the model structure. In this study, we propose a single regression-based imputation algorithm for multiblock missing data. First, we conduct a sparse precision matrix estimation based on the structure of block-wise missing data. Second, we impute the missing blocks with their means conditional on the observed blocks. Theoretical results about variable selection and estimation consistency are established in the context of a generalized linear model. Moreover, simulation studies show that compared with existing methods, the proposed imputation procedure is robust to various missing mechanisms because of the good properties of regression imputation. An application to Alzheimer's Disease Neuroimaging Initiative data also confirms the superiority of our proposed method.     

Common Drifting Volatility in Large Bayesian VARs

The general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar. We propose two models in which conditional volatilities feature comovement and study them using U.S. macroeconomic data. The first model specifies the conditional volatilities as driven by a single common unobserved factor, plus an idiosyncratic component. We label this model BVAR with general factor stochastic volatility (BVAR-GFSV) and we show that the loss in terms of marginal likelihood from assuming a common factor for volatility is moderate. The second model, which we label BVAR with common stochastic volatility (BVAR-CSV), is a special case of the BVAR-GFSV in which the idiosyncratic component is eliminated and the loadings to the factor are set to 1 for all the conditional volatilities. Such restrictions permit a convenient Kronecker structure for the posterior variance of the VAR coefficients, which in turn permits estimating the model even with large datasets. While perhaps misspecified, the BVAR-CSV model is strongly supported by the data when compared against standard homoscedastic BVARs, and it can produce relatively good point and density forecasts by taking advantage of the information contained in large datasets.     

On Robustness of Model-Based Bootstrap Schemes in Nonparametric Time Series Analysis

Theory in time series analysis is often developed under the assumption of finite-dimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is more complex, it is usually necessary to capture the whole dependence structure asymptotically for the bootstrap to be valid. In contrast, we show that certain simplified bootstrap schemes which imitate only some aspects of the time series are consistent for quantities arising in nonparametric statistics. To this end, we generalize the well-known "whitening by windowing" principle to joint distributions of nonparametric estimators of the autoregression function. Consequently, we obtain that model-based nonparametric bootstrap schemes remain valid for supremum-type functionals as long as they mimic those finite-dimensional joint distributions consistently which determine the quantity of interest. As an application, we show that simple regression-type bootstrap schemes can be applied for the determination of critical values for nonparametric tests of parametric or semiparametric hypotheses on the autoregression function in the context of a general process.     

On the clustering term in ecological analysis: how do different prior specifications affect results?

We study how different prior assumptions on the spatially structured heterogeneity term of the convolution hierarchical Bayesian model for spatial disease data could affect the results of an ecological analysis when response and exposure exhibit a strong spatial pattern. We show that in this case the estimate of the regression parameter could be strongly biased, both by analyzing the association between lung cancer mortality and education level on a real dataset and by a simulation experiment. The analysis is based on a hierarchical Bayesian model with a time dependent covariate in which we allow for a latency period between exposure and mortality, with time and space random terms and misaligned exposure-disease data.     

Comment: Uses of Administrative Records: A Social Security Point of View

Recent evidence indicates that using multiple forward rates sharply predicts future excess returns on U.S. Treasury Bonds, with the R²'s being around 30%. The projection coefficients in these regressions exhibit a distinct pattern that relates to the maturity of the forward rate. These dimensions of the data, in conjunction with the transition dynamics of bond yields, offer a serious challenge to term structure models. In this article we show that a regime-shifting term structure model can empirically account for these challenging data features. Alternative models, such as affine specification, fail to account for these important features. We find that regimes in the model are intimately related to bond risk premia and real business cycles.     

Modelling longitudinal semicontinuous emesis volume data with serial correlation in an acupuncture clinical trial

Summary.  In longitudinal studies, we are often interested in modelling repeated assessments of volume over time. Our motivating example is an acupuncture clinical trial in which we compare the effects of active acupuncture, sham acupuncture and standard medical care on chemotherapy-induced nausea in patients being treated for advanced stage breast cancer. An important end point for this study was the daily measurement of the volume of emesis over a 14-day follow-up period. The repeated volume data contained many 0s, had apparent serial correlation and had missing observations, making analysis challenging. The paper proposes a two-part latent process model for analysing the emesis volume data which addresses these challenges. We propose a Monte Carlo EM algorithm for parameter estimation and we use this methodology to show the beneficial effects of acupuncture on reducing the volume of emesis in women being treated for breast cancer with chemotherapy. Through simulations, we demonstrate the importance of correctly modelling the serial correlation for making conditional inference. Further, we show that the correct model for the correlation structure is less important for making correct inference on marginal means.     

LMS方法及其在流量数据中的应用

LMS模型是分析生长发育最常用的方法之一。本文详细阐述了LMS模型的构造原理；基于流量数据提出了费希尔信息矩阵与惩罚贝叶斯后验对数似然算法两种模型算法；利用中国健康营养调查（CHNS）1989-2011年中9年的流量数据，以所提出的LMS曲线算法为基础，通过计算BMI（Body Mass Index）绘制生长发育曲线研究我国青少年儿童生长发育情况和中年人健康问题。研究结果表明：1989-2011年间，我国0~18岁年龄段青少年儿童BMI中位数提高5%左右，生长发育高峰期有提前趋势；中年人群BMI中位数提高了10%左右，2000年后55周岁以上中年人体质差异有增大趋势。     

Bayesian mixture modeling for spatial Poisson process intensities,with applications to extreme value analysis

We propose a method for the analysis of a spatial point pattern, which is assumed to arise as a set of observations from a spatial nonhomogeneous Poisson process. The spatial point pattern is observed in a bounded region, which, for most applications, is taken to be a rectangle in the space where the process is defined. The method is based on modeling a density function, defined on this bounded region, that is directly related with the intensity function of the Poisson process. We develop a flexible nonparametric mixture model for this density using a bivariate Beta distribution for the mixture kernel and a Dirichlet process prior for the mixing distribution. Using posterior simulation methods, we obtain full inference for the intensity function and any other functional of the process that might be of interest. We discuss applications to problems where inference for clustering in the spatial point pattern is of interest. Moreover, we consider applications of the methodology to extreme value analysis problems. We illustrate the modeling approach with three previously published data sets. Two of the data sets are from forestry and consist of locations of trees. The third data set consists of extremes from the Dow Jones index over a period of 1303 days.     

Outliers and Patterns of Outliers in Contingency Tables with Algebraic Statistics

Abstract. In this paper, we provide a definition of pattern of outliers in contingency tables within a model‐based framework. In particular, we make use of log‐linear models and exact goodness‐of‐fit tests to specify the notions of outlier and pattern of outliers. The language and some techniques from Algebraic Statistics are essential tools to make the definition clear and easily applicable. We also analyse several numerical examples to show how to use our definitions.     

Sequential Pattern Analysis: A Statistical Investigation of Sequence Length and Support

In sequential pattern analysis, the frequency of patterns is evaluated by the support. While computed efficiently from large databases, we show that the support cannot be compared between different databases, since it is influenced by the actual sequence length distribution. Models for this sequence length distribution are surveyed. One of these models, the Good distribution, appears to be sufficiently flexible for practice. It is used to exemplify an approach for adjusting the relative support such that the resulting adjusted support values are better comparable between different databases. We illustrate our findings with texts from the bilingual FinDe corpus.     

Bayesian correlated factor analysis of socio-demographic indicators

Recent changes in European family dynamics are often linked to common latent trends of economic and ideational change. Using Bayesian factor analysis, we extract three latent variables from eight socio-demographic indicators related to family formation, dissolution, and gender system and collected on 19 European countries within four periods (1970, 1980, 1990, 1998). The flexibility of the Bayesian approach allows us to introduce an innovative temporal factor model, adding the temporal dimension to the traditional factorial analysis. The underlying structure of the Bayesian factor model proposed reflects our idea of an autoregressive pattern in the latent variables relative to adjacent time periods. The results we obtain are consistent with current interpretations in European demographic trends.     

Optimal design for the Bradley–Terry paired comparison model

Paired comparisons are a popular tool for questionnaires in psychological marketing research. The quality of the statistical analysis of the responses heavily depends on the design, i.e. the choice of the alternatives in the comparisons. In this paper we show that the structure of locally optimal designs changes substantially with the parameters in the underlying utility. This fact is illustrated by elementary examples, where the optimal designs can be completely characterized. As an alternative maximin efficient designs are proposed which perform well for all parameter settings. Research supported by grant Ho 1286 of the German Research Council (Deutsche Forschungsgemeinschaft).