On asymptotic distribution of prediction in functional linear regression

There has been substantial recent attention on problems involving a functional linear regression model with scalar response. Among them, there have been few works dealing with asymptotic distribution of prediction in functional linear regression models. In recent literature, the centeral limit theorem for prediction has been discussed, but the proof and conditions under which the random bias terms for a fixed predictor converge to zero have been ignored so that the impact of these terms on the convergence of the prediction has not been well understood. Clarifying the proof and conditions under which the bias terms converge to zero, we show that the asymptotic distribution of the prediction is normal. Furthermore, we have derived those results related to other terms that already obtained by others, under milder conditions. Finally, we conduct a simulation study to investigate performance of the asymptotic distribution under various parameter settings.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.     

不同城市的房价是否具有相同的人口集聚效应——基于35个大中城市PVAR模型的实证分析

人口集聚会引起房价上涨,但不同类型的城市由于集聚人口的教育程度不同,房价上涨也具有不同的效应。首先建立人口流动理论模型分析人口集聚对房价上涨的影响,然后通过建立房价、人口集聚、房地产投资和人均可支配收入的PVAR模型进行实证分析。结论表明,一类城市人口集聚对房价上涨具有正效应,二类城市为平效应,三类城市为负效应;三类城市的房价上涨主要是房地产投资和人均收入提高所推动的。     

陕西高校各类教育提升全人素质的预测模型

采用"高校全人素质量表"随机抽样省部属8所高校4 953名大学生,对调查数据做SPSS多元回归参数估计和AMOS结构方程模型构建。结果表明,德育教育和专业教育分别对大学生全人素质16项指数产生显著正效应;德育教育"立德效应"主提升德道品质、心身健康、人生意义等素质;专业教育"树人效应"主提升求知钻研、学术探索、技术能力、语言思维等素质。研究结果为陕西高校全人素质教育提供了实证参数和理论依据。     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

模具钢干铣削试验及参数优化研究

为了对铣削力做进一步的研究,以及预测铣削参数的改变对铣削力变化的影响,文章建立了铣削力预测模型,引 入了PSO优化算法。试验采用正交设计方法,干式铣削SKD61模具钢;KISTLER测力仪测量铣削力;HRsoft_DW数采软 件采集试验数据,并对数据进行极差分析。研究结果表明每齿进给量是铣削参数中影响铣削力最为主要的因素。研究 验证了PSO算法对铣削参数优化问题具有有效性。     

福建省“十一五”期间人口变化与人力资源供给分析

人口预测和人力资源供给分析是为了适应社会经济发展的客观需要而提出的,它们未来的发展状况,对于一个地区的社会经济的发展影响深远。在对福建省"十五"期间人口与人力资源状况分析的基础上,对福建省"十一五"期间人口与人力资源状况进行初步预测,旨在为福建省制定未来的社会经济发展战略提供人口统计上的数据依据。