高绩效工作系统与企业绩效——中国金融业的实证研究

为了检验在中国背景下,高绩效工作系统与企业绩效的关系,通过问卷调查,收集了长三角地区的96家金融企业的人力资源管理实践和企业绩效的数据,并利用相关分析和回归分析的方法对数据进行分析研究。结果表明:高绩效工作系统对金融企业的非财务绩效指标具有正向的影响。     

高职院校公共管理实验室建设的探索与思考

高职院校实验室建设对提高学生的实践动手能力,培养应用型人才具有不可替代的作用。公共管理实验室建设的目标和内容不仅要服务于教学,也要针对社会需要,培养符合社会需要的应用型人才,并结合新形势下高等教育改革提出公共管理实验室建设的基本思路。     

农民对农业科技知识需求强度的实证研究——以杨凌示范区为典型个案

以杨凌农业高新产业示范区为典型个案,通过实证调研方法,研究农民对农业科技知识的需求强度。结果表明：杨凌农业高新技术产业示范区的特殊优势,使得农民对科技知识的需求强度总体较高;不同性别间农业科技知识需求强度不对称,女性处于弱势地位;农民对农业科技知识的需求强度随着年龄的增长逐渐减弱;不同文化程度的农民对农业科技需求强度不同;家庭收入影响农民对农业科技知识的需求强度。为了提高农民对科技知识的需求强度,需要在政策上充分认识农民的群体层次差异,针对不同类型农民选择不同的传播渠道;对不同层次的科技知识使用不同的传播路径;在农业科技知识传播中关注农民中的弱势群体,比如女性农民、年龄较大的农民和文化程度较低的农民,选择他们感兴趣的、容易接受的科技知识进行传播。     

中国高技术产业技术创新与竞争力的关系

高技术产业竞争力来源于技术创新能力。通过构建高技术产业出口贸易竞争力与技术创新能力评价模型,运用灰色关联度评价法对中国高技术产业各行业竞争力与技术创新能力的关联度进行实证研究,得出贸易竞争力与技术创新各指标的关联度。     

现代汉语高频语素的统计及形成原因分析

本文以《现代汉语词典》(2007年修订版)作为选取语料的范围,建立一个现代汉语语素库。在现代汉语语素库的基础上,统计出最高频的300个语素作为研究对象,着重考察其构词能力、语法属性、语义分布的情况。最后作者对高频语素形成原因进行分析,认为现代汉语语素的生命力长久、义项的数量、义域以及语法属性的宽泛程度都是形成高频语素的主要因素。     

高含硫气井井筒硫沉积位置预测模型研究

高含硫气井在气体开采过程中,随着井筒温度压力的降低,硫微粒在气相中的溶解度逐渐减小,在达到临界饱和态后将从气相中析出,当井筒中硫析出位置处的气流速度小于微粒临界悬浮流速时,析出的硫微粒在井筒中沉积,堵塞气体的流动通道,从而导致气井产量降低,影响气井的产能和经济效益,严重时造成气井停产。将井筒压力温度分布预测模型与硫溶解度模型耦合,结合井筒中微粒临界悬浮流速模型建立了高含硫气井井筒硫沉积预测综合模型,并对L 井进行了实例分析。研究结果表明：高含硫气井产量降低至临界产量后,随着气井产量的降低,井筒中气流速度逐渐减小,硫沉积区域沿井筒向下逐渐增加。     

Smoothing spline ANOPOW

This paper is motivated by the pioneering work of Emanuel Parzen wherein he advanced the estimation of (spectral) densities via kernel smoothing and established the role of reproducing kernel Hilbert spaces (RKHS) in field of time series analysis. Here, we consider analysis of power (ANOPOW) for replicated time series collected in an experimental design where the main goals are to estimate, and to detect differences among, group spectra. To accomplish these goals, we obtain smooth estimators of the group spectra by assuming that each spectral density is in some RKHS; we then apply penalized least squares in a smoothing spline ANOPOW. For inference, we obtain simultaneous confidence intervals for the estimated group spectra via bootstrapping.     

Optimal window width choice in spectral density estimation

This paper deals with optimal window width choice in on-parametric lag or spectral window estimation of the spectral density of a stationary zero-mean process. Several approaches are reviewed: cross-validation-based methods as described by Hurvich(1985) BelträHo and Bloomfield (1987) and Hurvich and Belträo (1990); an iterative pro-cedure developed by Bühlmann (1996); and a bootstrap approach followed by Franke and Hardle (1992). These methods are compared in terms of the mean square error,the mean square percentage error, and a third measure of the istance between the true spectral density and its estimate. The comparison is based on a simulation study, the simulated processes being in he class of ARMA (5,5) processes. On the basis of simu-lation evidence we suggest to use a slightly modified version of Biihlmann's (1996)iterative method. This paper also makes a minor correction of the bootstrap criterion by Franke and Härdle (1992).     

Robust estimation for order of hidden Markov models based on density power divergences

In this paper, we study the robust estimation for the order of hidden Markov model (HMM) based on a penalized minimum density power divergence estimator, which is obtained by utilizing the finite mixture marginal distribution of HMM. For this task, we adopt the locally conic parametrization method used in [D. Dacunha-Castelle and E. Gassiate, Testing in locally conic models and application to mixture models. ESAIM Probab. Stat. (1997), pp. 285–317; D. Dacunha-Castelle and E. Gassiate, Testing the order of a model using locally conic parametrization: population mixtures and stationary arma processes, Ann. Statist. 27 (1999), pp. 1178–1209; T. Lee and S. Lee, Robust and consistent estimation of the order of finite mixture models based on the minimizing a density power divergence estimator, Metrika 68 (2008), pp. 365–390] to avoid the difficulties that arise in handling mixture marginal models, such as the non-identifiability of the parameter space and the singularity problem with the asymptotic variance. We verify that the estimated order is consistent and simulation results are provided for illustration.     

Covariance structure approximation via gLasso in high-dimensional supervised classification

Recent work has shown that the Lasso-based regularization is very useful for estimating the high-dimensional inverse covariance matrix. A particularly useful scheme is based on penalizing the ?₁ norm of the off-diagonal elements to encourage sparsity. We embed this type of regularization into high-dimensional classification. A two-stage estimation procedure is proposed which first recovers structural zeros of the inverse covariance matrix and then enforces block sparsity by moving non-zeros closer to the main diagonal. We show that the block-diagonal approximation of the inverse covariance matrix leads to an additive classifier, and demonstrate that accounting for the structure can yield better performance accuracy. Effect of the block size on classification is explored, and a class of asymptotically equivalent structure approximations in a high-dimensional setting is specified. We suggest a variable selection at the block level and investigate properties of this procedure in growing dimension asymptotics. We present a consistency result on the feature selection procedure, establish asymptotic lower an upper bounds for the fraction of separative blocks and specify constraints under which the reliable classification with block-wise feature selection can be performed. The relevance and benefits of the proposed approach are illustrated on both simulated and real data.