金融发展与区域创新的交互影响

基于2008—2017年长三角地区41个地级市的数据,通过构建联立方程模型,考察金融发展与区域创新之间的交互影响,探讨两者的空间溢出效应与地区交互影响。研究发现：金融发展对区域创新的正向促进作用不受时间跨度影响,但区域创新对金融发展的影响存在时间跨度异质性,短期内区域创新对金融发展影响不确定,而长期内区域创新反向促进金融发展,忽略这种反向影响会显著高估金融发展对区域创新的促进作用;区域创新与金融发展均存在正向空间外溢性,临近地区金融发展促进本地区的区域创新,但临近地区区域创新对本地区的金融发展作用并不显著。金融发展与区域创新应当融合联动,互利共生,实现双赢。     

New evidence about effects of reproductive variables on child mortality in sub-Saharan Africa

There is still considerable uncertainty about how reproductive factors affect child mortality. This study, based on Demographic and Health Survey data from 28 countries in sub-Saharan Africa, shows that mortality is highest for firstborn children with very young mothers. Other children with young mothers, or of high birth order, also experience high mortality. Net of maternal age and birth order, a short preceding birth interval is associated with above average mortality. These patterns change, however, if time-invariant unobserved mother-level characteristics of importance for both mortality and fertility are controlled for in a multilevel–multiprocess model. Most importantly, there are smaller advantages associated with longer birth intervals and being older at first birth. The implications of alternative reproductive ‘strategies’ are discussed, taking into account that if the mother is older at birth, the child will also be born in a later calendar year, when mortality may be lower.     

感知风险对在线旅游产品购买意愿的影响分析

旅游电子商务发展迅猛,互联网购物平台的虚拟性和远程性强化了消费者感知风险,它也成为影响旅游电子商务发展的重要因素之一.文章聚焦消费者感知风险,运用SPSS 19.0及AMOS 21.0软件,构建以5个维度的感知风险为自变量、购买意愿为因变量、信任为调节变量的结构方程模型,探讨变量间相关关系.结果发现:感知风险对信任的影响差异较大,仅经济风险感知与信任呈显著负相关;感知风险对购买意愿总效应皆呈负相关;信任作为中介变量存在明显的调节作用,有效地降低了感知风险对购买意愿的负向影响.     

《结构实验与方法》课程案例库建设研究

为提高土木工程专业《结构实验与方法》研究生课程的教学质量,培养研究生的应用与创新研究能力,基于《结构实验与方法》研究生课程的具体教学实践,分析了建设《结构实验与方法》研究生课程教学案例库的必要性,探讨了《结构实验与方法》研究生课程教学案例库建设的程序与目标,提出了建设《结构实验与方法》研究生课程教学案例库的基本方法。     

State‐led industrial development,structural transformation and elite‐led plunder: Angola (2002–2013) as a developmental state

From 2002 to 2013, Angola engaged in large‐scale state‐led reconstruction and development alongside an elite‐led appropriation and seizure of national assets. Until the oil price shock, Angola had been succeeding in promoting rapid economic growth, and possibly even significant social development, alongside a massive grab of wealth and power by local elites. Today, though an economic crisis has taken hold, frequent predictions of the country's imminent collapse have yet to be fulfilled. This article reviews the state's development planning and expenditure with a focus on public investment and industrial development to determine to what extent Angola during this period might be considered a developmental or petro‐developmental state. It is argued that, while more significant than generally thought, petro‐developmental outcomes were and are limited by the autocratic and neopatrimonial tendencies of the Angolan elite. Nevertheless, limited success with structural transformation may have lasting effects. Following its long civil war, the conditions existed for Angola to follow a new path of state‐led development. Though it may now be more difficult, structural transformation and economic diversification remain the only path to economic and social development.     

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Summary.  Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models , where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.     

On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions of pseudo-value regression equations.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

Invariant co-ordinate selection

Summary.  A general method for exploring multivariate data by comparing different estimates of multivariate scatter is presented. The method is based on the eigenvalue–eigenvector decomposition of one scatter matrix relative to another. In particular, it is shown that the eigenvectors can be used to generate an affine invariant co-ordinate system for the multivariate data. Consequently, we view this method as a method for invariant co-ordinate selection . By plotting the data with respect to this new invariant co-ordinate system, various data structures can be revealed. For example, under certain independent components models, it is shown that the invariant co- ordinates correspond to the independent components. Another example pertains to mixtures of elliptical distributions. In this case, it is shown that a subset of the invariant co-ordinates corresponds to Fisher's linear discriminant subspace, even though the class identifications of the data points are unknown. Some illustrative examples are given.     

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model. The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil.