改进粒子群算法优化灰色神经网络预测模型及其应用

文章针对神经网络存在局部最优、收敛速度慢以及大样本等缺点,将改进的粒子群算法、灰色模型和神经网络模型有机结合,构建了改进粒子群优化灰色神经网络预测模型(IPSO-GMNN).并与其他预测模型进行比较,实证结果表明:IPSO-GMNN预测模型能够克服神经网络预测模型的不足,更好地识别时间序列的非线性和突变性特征.在对我国专利授权数量的预测应用中,新模型对非线性时间数据预测表现出更好的预测精度和稳定性.     

Robust inference for group sequential trials

For ethical reasons, group sequential trials were introduced to allow trials to stop early in the event of extreme results. Endpoints in such trials are usually mortality or irreversible morbidity. For a given endpoint, the norm is to use a single test statistic and to use that same statistic for each analysis. This approach is risky because the test statistic has to be specified before the study is unblinded, and there is loss in power if the assumptions that ensure optimality for each analysis are not met. To minimize the risk of moderate to substantial loss in power due to a suboptimal choice of a statistic, a robust method was developed for nonsequential trials. The concept is analogous to diversification of financial investments to minimize risk. The method is based on combining P values from multiple test statistics for formal inference while controlling the type I error rate at its designated value.This article evaluates the performance of 2 P value combining methods for group sequential trials. The emphasis is on time to event trials although results from less complex trials are also included. The gain or loss in power with the combination method relative to a single statistic is asymmetric in its favor. Depending on the power of each individual test, the combination method can give more power than any single test or give power that is closer to the test with the most power. The versatility of the method is that it can combine P values from different test statistics for analysis at different times. The robustness of results suggests that inference from group sequential trials can be strengthened with the use of combined tests.     

Parameter estimation for multivariate diffusion processes with the time inhomogeneously positive semidefinite diffusion matrix

Statistical inference for the diffusion coefficients of multivariate diffusion processes has been well established in recent years; however, it is not the case for the drift coefficients. Furthermore, most existing estimation methods for the drift coefficients are proposed under the assumption that the diffusion matrix is positive definite and time homogeneous. In this article, we put forward two estimation approaches for estimating the drift coefficients of the multivariate diffusion models with the time inhomogeneously positive semidefinite diffusion matrix. They are maximum likelihood estimation methods based on both the martingale representation theorem and conditional characteristic functions and the generalized method of moments based on conditional characteristic functions, respectively. Consistency and asymptotic normality of the generalized method of moments estimation are also proved in this article. Simulation results demonstrate that these methods work well.     

大数据背景下线上商品价格变动对CPI的影响

文章利用2011年2月至2015年9月阿里巴巴研究院公布的网络零售价格(ASPI)指数及其各分类商品价格指数与国家统计局公布的CPI历史数据分别作为线上、线下一般商品价格变动的代理变量,在协整检验的基础上采用向量误差修正模型对线上、线下商品价格变动的关联关系进行了定量分析,并对构成ASPI的各分项商品价格指数与CPI之间的关系进行了分析.研究表明:在误差修正机制的作用下,线上、线下商品价格之间存在稳定均衡的关系.     

新一轮区域发展背景下广州南站地区产业发展研究

"高铁经济"的发展对区域经济与社会发展具有重要的带动作用与战略意义,高铁枢纽及其周边地区也将成为城市发展新引擎。对其功能定位与产业发展则是高铁枢纽地区开发建设的前提与成功与否的关键因素之一。以广州南站枢纽地区为例,以区域竞合发展的视角,剖析南站地区发展面临的阶段困境,探索基于新一轮国家对外开放战略与地区合作升级机遇的背景下,南站地区如何利用高铁枢纽和区位优势,对接不同层面的战略需求来清晰定位其功能,并对产业发展路径与空间布局提出相应的合理化建议。     

A varying coefficient approach to estimating hedonic housing price functions and their quantiles

The varying coefficient (VC) model introduced by Hastie and Tibshirani [26 T. Hastie and R. Tibshirani, Varying-coefficient models, J. R. Statist. Soc. (Ser. B) 55 (1993), pp. 757–796.[Web of Science ®] , [Google Scholar]] is arguably one of the most remarkable recent developments in nonparametric regression theory. The VC model is an extension of the ordinary regression model where the coefficients are allowed to vary as smooth functions of an effect modifier possibly different from the regressors. The VC model reduces the modelling bias with its unique structure while also avoiding the ‘curse of dimensionality’ problem. While the VC model has been applied widely in a variety of disciplines, its application in economics has been minimal. The central goal of this paper is to apply VC modelling to the estimation of a hedonic house price function using data from Hong Kong, one of the world's most buoyant real estate markets. We demonstrate the advantages of the VC approach over traditional parametric and semi-parametric regressions in the face of a large number of regressors. We further combine VC modelling with quantile regression to examine the heterogeneity of the marginal effects of attributes across the distribution of housing prices.     

Computing A-optimal and E-optimal designs for regression models via semidefinite programming

In semidefinite programming (SDP), we minimize a linear objective function subject to a linear matrix being positive semidefinite. A powerful program, SeDuMi, has been developed in MATLAB to solve SDP problems. In this article, we show in detail how to formulate A-optimal and E-optimal design problems as SDP problems and solve them by SeDuMi. This technique can be used to construct approximate A-optimal and E-optimal designs for all linear and nonlinear regression models with discrete design spaces. In addition, the results on discrete design spaces provide useful guidance for finding optimal designs on any continuous design space, and a convergence result is derived. Moreover, restrictions in the designs can be easily incorporated in the SDP problems and solved by SeDuMi. Several representative examples and one MATLAB program are given.     

Vocational Experiences and Quality of Life of Migrants: Overview and Future Research

Quality of life is a complicated concept that has attracted broad attention from multiple disciplines. This article introduces a set of studies that employ diverse approaches to explore how work, vocational, and career experiences shape quality of life among migrants, and proposes a future agenda to extend this stream of research. Specifically, operationalizing quality of life in different ways, research showcased in this special issue has demonstrated how international and intranational migrants achieve, maintain, and enhance their well-being in different life domains. Reflecting on the literature and the showcased studies, this article proposes that future research should advance the current inquiry by better categorizing vocational experiences, uniquely operationalizing of quality of life in migration contexts, taking a more culturally sensitive perspective, and broadening methodological approaches.     

The Life Course,Cohort Dynamics,and International Differences in Aging Trajectories

In recent years, population health research has focused on understanding the determinants of later-life health. Two strands of that work have focused on (1) international comparisons of later-life health and (2) assessing the early-life origins of disease and disability and the importance of life course processes. However, the less frequently examined intersection of these approaches remains an important frontier. The present study contributes to the integration of these approaches. We use the Health and Retirement Study family of data sets and a cohort dynamic approach to compare functional health trajectories across 12 high-income countries and to examine the role of life course processes and cohort dynamics in contributing to variation in those trajectories. We find substantial international variation in functional health trajectories and an important role of cohort dynamics in generating that variation, with younger cohorts often less healthy at comparable ages than the older cohorts they are replacing. We further find evidence of heterogeneous effects of life course processes on health trajectories. The results have important implications for future trends in morbidity and mortality as well as public policy.