生命信息的交融互渗——中国花鸟画审美初探

自然界的有生命之物,以其不同的形状、色彩、气息和动静之态,而有着不同的审美价值.它们的美是生命之美.中国花鸟画不以再现自然对象的外形为目的,而以思想、情感、意趣等生命现象的高级形式反映在作品中.作者根据主观的审美理想和自我生命的节律加以重新创造,使其固定为物化状态,给人以美的滋养.     

新疆能源强度变动的影响因素探析

本文采用Refined Laspeyres指数分解模型，对1985～2005年新疆能源强度进行分解，寻求新疆能源强度变动的主要影响因素。实证结果显示，工业部门内部能源强度改进是新疆能源利用效率提高的主因，而产业结构变动并未对其产生积极的影响。此外，工业部门能源强度的改进主导了新疆能源利用效率改进的强度效应。重工业行业内部能源强度的改进扭转了其比重上升对能源利用效率的不利影响，使得新疆工业部门能源利用效率提升。     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

Modal non‐linear regression in the presence of Laplace measurement error

In this paper, we propose a robust estimation procedure for a class of non‐linear regression models when the covariates are contaminated with Laplace measurement error, aiming at constructing an estimation procedure for the regression parameters which are less affected by the possible outliers, and heavy‐tailed underlying distribution, as well as reducing the bias introduced by the measurement error. Starting with the modal regression procedure developed for the measurement error‐free case, a non‐trivial modification is made so that the modified version can effectively correct the potential bias caused by measurement error. Large sample properties of the proposed estimate, such as the convergence rate and the asymptotic normality, are thoroughly investigated. A simulation study and real data application are conducted to illustrate the satisfying finite sample performance of the proposed estimation procedure.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

劳务输出与脱贫致富

长期以来 ,有关方面均把贫困地区脱贫致富寄望于劳务输出。短期看来 ,劳务输出确能在一定程度上缓解贫困压力 ,但长期而言 ,劳务输出并不能从根本上解决贫困地区的脱贫致富问题。这是由劳力输出的限度、制约条件、实际效果及贫困的根本原因决定的。贫困地区脱贫的根本出路在于 :立足并开发贫困地区 ,走政府推进型脱贫致富之路。为此 ,当务之急须认真解决好三个问题 :( 1 )制定并实施开发贫困地区的战略 ;( 2 )国家加大对贫困地区的投入 ;( 3)地方政府真抓实干领导脱贫     

加入WTO背景下江苏流通企业的发展

我国将成为ＷＴＯ成员国,并在未来较短的时间逐步取消对外开放和外商市场准入方面的种种政策与法规限制。这意味着国内流通企业将面临日趋激烈的竞争环境,江苏的流通企业也必须及早做好准备,制定在竞争的发展对策。 根据当前流通业存在的问题,我认为省内流通企业必须完成一些战略性转变。 一、转变依赖政府保护的观念 流通业,特别是商业,属于竞争性行业,属于国有经济“有进有退”战略安排中“退”的部分。换言之,即使是在未加入ＷＴＯ的条件下,在这一领域,国家对国有商业均未给予政策倾斜和投资扶持,全凭国有商业企业自己的素质…     

A comparative study of confidence intervals to assess biosimilarity from analytical data

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods.     

A primal dual active set with continuation algorithm for high-dimensional nonconvex SICA-penalized regression

The smooth integration of counting and absolute deviation (SICA) penalty has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection and parameter estimation. However, solving the nonconvex optimization problem associated with SICA penalty in high-dimensional setting remains to be enriched, mainly due to the singularity at the origin and the nonconvexity of the SICA penalty function. In this paper, we develop a fast primal dual active set (PDAS) with continuation algorithm for solving the nonconvex SICA-penalized least squares in high dimensions. Upon introducing the dual variable, the PDAS algorithm iteratively identify and update the active set in the optimization using both the primal and dual information, and then solve a low-dimensional least square problem on the active set. When combined with a continuation strategy and a high-dimensional Bayesian information criterion (BIC) selector on the tuning parameters, the proposed algorithm is very efficient and accurate. Extensive simulation studies and analysis of a high-dimensional microarray gene expression data are presented to illustrate the performance of the proposed method.     

Measuring servitization progress and outcome: the case of ‘advanced services’

The purpose of this paper is to establish a framework for assessing the progress and outcome of a manufacturer’s transformation towards becoming a provider of ‘advanced services’ – a complex bundling of products and services, whereby manufacturers offer capabilities and outcomes instead of products alone. ‘Advanced services’ represent the most complex offering in the current servitization trend amongst manufacturers. However, current performance measures lack the breadth and focus to assess progress or outcomes, and so support research and practice of organisational transformation efforts required. To address this gap the paper investigates how a manufacturer’s efforts to become an ‘advanced services’ provider can be comprehensively measured, and develops a framework for assessing the transformation journey towards becoming an ‘advanced services’ provider. The research method is based on (1) a systematic literature review process to create a comprehensive set of service-related performance measures that are available to assess a manufacturer’s servitization efforts, followed by (2) an engagement with an expert panel to synthesise the identified measures and create a set of ‘advanced services’ performance measures. The proposed framework is presented as a scorecard that can be used in practice to assess the progress and outcome of a manufacturer’s transformation towards becoming a provider of ‘advanced services’.