Kernel density estimation on the torus

Kernel density estimation for multivariate, circular data has been formulated only when the sample space is the sphere, but theory for the torus would also be useful. For data lying on a d-dimensional torus (d?1), we discuss kernel estimation of a density, its mixed partial derivatives, and their squared functionals. We introduce a specific class of product kernels whose order is suitably defined in such a way to obtain L₂-risk formulas whose structure can be compared to their Euclidean counterparts. Our kernels are based on circular densities; however, we also discuss smaller bias estimation involving negative kernels which are functions of circular densities. Practical rules for selecting the smoothing degree, based on cross-validation, bootstrap and plug-in ideas are derived. Moreover, we provide specific results on the use of kernels based on the von Mises density. Finally, real-data examples and simulation studies illustrate the findings.     

混合预期噪声交易模型及我国证券市场非理性交易的实证分析

 证券资产作为以获取未来收益为目的投资工具,它在价格形成及其市场运行模式上都取决于投资者的预期。基于有效市场假说（EMH）的资产定价理论虽然在理论体系及其形式化上都给出了完美的解决方案,但投资者理性和投资者具有完全一致的预期这两个基本假定在现实证券市场投资中却难以得到满足。本文更现实地依据投资者预期形成的差异,讨论了非一致有限理性预期下的证券市场价格的决定,提出了基于混合预期的噪声交易模型。同时本文还利用上海证券市场的相关实际数据,对该模型进行了实证检验,从而克服了行为金融学中噪声交易模型不能用于实证分析的障碍。     

An alternative stochastic restricted Liu estimator in linear regression

In this paper, we introduce an alternative stochastic restricted Liu estimator for the vector of parameters in a linear regression model when additional stochastic linear restrictions on the parameter vector are assumed to hold. The new estimator is a generalization of the ordinary mixed estimator (OME) (Durbin in J Am Stat Assoc 48:799–808, 1953; Theil and Goldberger in Int Econ Rev 2:65–78, 1961; Theil in J Am Stat Assoc 58:401–414, 1963) and Liu estimator proposed by Liu (Commun Stat Theory Methods 22:393–402, 1993). Necessary and sufficient conditions for the superiority of the new stochastic restricted Liu estimator over the OME, the Liu estimator and the estimator proposed by Hubert and Wijekoon (Stat Pap 47:471–479, 2006) in the mean squared error matrix (MSEM) sense are derived. Furthermore, a numerical example based on the widely analysed dataset on Portland cement (Woods et al. in Ind Eng Chem 24:1207–1241, 1932) and a Monte Carlo evaluation of the estimators are also given to illustrate some of the theoretical results.     

Spatial model fitting for large datasets with applications to climate and microarray problems

Many problems in the environmental and biological sciences involve the analysis of large quantities of data. Further, the data in these problems are often subject to various types of structure and, in particular, spatial dependence. Traditional model fitting often fails due to the size of the datasets since it is difficult to not only specify but also to compute with the full covariance matrix describing the spatial dependence. We propose a very general type of mixed model that has a random spatial component. Recognizing that spatial covariance matrices often exhibit a large number of zero or near-zero entries, covariance tapering is used to force near-zero entries to zero. Then, taking advantage of the sparse nature of such tapered covariance matrices, backfitting is used to estimate the fixed and random model parameters. The novelty of the paper is the combination of the two techniques, tapering and backfitting, to model and analyze spatial datasets several orders of magnitude larger than those datasets typically analyzed with conventional approaches. Results will be demonstrated with two datasets. The first consists of regional climate model output that is based on an experiment with two regional and two driver models arranged in a two-by-two layout. The second is microarray data used to build a profile of differentially expressed genes relating to cerebral vascular malformations, an important cause of hemorrhagic stroke and seizures.     

Multilevel multivariate modelling of childhood growth, numbers of growth measurements and adult characteristics

Summary.  A general latent normal model for multilevel data with mixtures of response types is extended in the case of ordered responses to deal with variates having a large number of categories and including count data. An example is analysed by using repeated measures data on child growth and adult measures of body mass index and glucose. Applications are described that are concerned with the flexible prediction of adult measurements from collections of growth measurements and for studying the relationship between the number of measurement occasions and growth trajectories.     

金融监管模式的变革及资本市场统合法的诞生——以金融衍生产品监管为视角

金融衍生产品和混业经营的发展对以金融组织机构性质为标准而形成的传统金融监管模式提出挑战.旨在有效解决金融业混业经营格局下金融创新产品的监管归属问题,避免监管"真空"和多重监管,依功能性监管规则而形成的统一金融监管模式及制定资本市场统合法的金融法改革方兴未艾.我国应积极应对"金融监管现代化"的全球趋势,尽快构建起适合中国国情的和谐生态的金融监管体制,在金融衍生产品的监管上,建立和完善牵头监管模式.     

Autoregressive Forecasting of Some Functional Climatic Variations

Many variations such as the annual cycle in sea surface temperatures can be considered to be smooth functions and are appropriately described using methods from functional data analysis. This study defines a class of functional autoregressive (FAR) models which can be used as robust predictors for making forecasts of entire smooth functions in the future. The methods are illustrated and compared with pointwise predictors such as SARIMA by applying them to forecasting the entire annual cycle of climatological El Nino–Southern Oscillation (ENSO) time series one year ahead. Forecasts for the period 1987–1996 suggest that the FAR functional predictors show some promising skill, compared to traditional scalar SARIMA forecasts which perform poorly.     

The two-sided power-binomial distribution for modeling binary count data

The two-sided power (TSP) distribution is a flexible two-parameter distribution having uniform, power function and triangular as sub-distributions, and it is a reasonable alternative to beta distribution in some cases. In this work, we introduce the TSP-binomial model which is defined as a mixture of binomial distributions, with the binomial parameter p having a TSP distribution. We study its distributional properties and demonstrate its use on some data. It is shown that the newly defined model is a useful candidate for overdispersed binomial data.     

从庇护性就业到支持性就业 ——对广东省残疾人工作整合型社会企业的多个案研究

为适应经济社会的转型发展和残疾人多层次的就业与康复需求,我国残疾人就业模式日益多元化。作为一种新兴的残疾人就业模式,工作整合型社会企业旨在为残疾人提供支持性就业,以促进残疾人的社会融合。作为一种新兴的组织类型,工作整合型社会企业兼具市场、社会与政府三重维度的混合价值取向,致力于运用市场机制解决残疾人所面临的社会问题。工作整合型社会企业在创新残疾人就业模式、促进残疾人社会融合、增加残疾人经济收入、提升残疾人康复服务等方面有着积极作用,但其发展仍处于初级阶段,在专业人员培养、商业运营能力、目标人群覆盖面、政策扶持及法律保障等方面面临不少挑战。整体而言,工作整合型社会企业及其倡导的支持性就业与"重康复、轻就业"的庇护性就业模式并非全然排斥,而是一种相辅相成、前后相继的共融关系,共同塑造我国残疾人就业的多元格局。