基于CAS理论的企业动态能力构建机理研究

复杂适应系统(Complex Adaptive System,简称CAS)理认强调其内部主体的主动性和对环境的适应性,而企业作为复杂适应系统,其一些行为比如动态能力的构建过程恰好体现了企业主动适应快速变化的环境、获取新一层次竞争优势的过程,因此CAS理论为研究企业行为提供了新的范式框架.本文应用CAS理论和涌现机理,从企业内部的自组织和企业同环境的相互作用两方面详细探讨了企业动态能力的构建机理.     

Penalized contrast estimator for adaptive density deconvolution

The authors consider the problem of estimating the density g of independent and identically distributed variables X_I, from a sample Z₁,… Z_n such that Z_I = X_I + σ? for i = 1,…, n, and E is noise independent of X, with σ? having a known distribution. They present a model selection procedure allowing one to construct an adaptive estimator of g and to find nonasymptotic risk bounds. The estimator achieves the minimax rate of convergence, in most cases where lower bounds are available. A simulation study gives an illustration of the good practical performance of the method.     

Adaptive Gaussian Markov random fields with applications in human brain mapping

Summary.  Functional magnetic resonance imaging has become a standard technology in human brain mapping. Analyses of the massive spatiotemporal functional magnetic resonance imaging data sets often focus on parametric or non-parametric modelling of the temporal component, whereas spatial smoothing is based on Gaussian kernels or random fields. A weakness of Gaussian spatial smoothing is underestimation of activation peaks or blurring of high curvature transitions between activated and non-activated regions of the brain. To improve spatial adaptivity, we introduce a class of inhomogeneous Markov random fields with stochastic interaction weights in a space-varying coefficient model. For given weights, the random field is conditionally Gaussian, but marginally it is non-Gaussian. Fully Bayesian inference, including estimation of weights and variance parameters, can be carried out through efficient Markov chain Monte Carlo simulation. Although motivated by the analysis of functional magnetic resonance imaging data, the methodological development is general and can also be used for spatial smoothing and regression analysis of areal data on irregular lattices. An application to stylized artificial data and to real functional magnetic resonance imaging data from a visual stimulation experiment demonstrates the performance of our approach in comparison with Gaussian and robustified non-Gaussian Markov random-field models.     

不可压缩管流的广义有限元方法

本文将广义有限元法用于求解不可压缩管流.通过泛函的变域变分理论推导出严格的网格自适应性条件,可以在给定网格节点数目的条件下,在计算时自动调整网格的分布,使泛函取得极值,同时也使流场参数和网格节点位置取得最优值.对一维喷管的计算结果表明,广义有限元法理论上严密,在计算中是可行的,是常规有限元法向高精度的自适应网格方向的合理推广.     

Inference about permutation parameter in large samples

The estimation problem of a permutation parameter on the basis of a random sample of increasing size is considered. A necessary and sufficient condition for the existence of an estimator, asymptotically fully efficient for two different distributions families, is derived. We also study the application of this result to cyclic groups of order two and three.     

Some large trial properties of minimum likelihood allocation

The method of minimum likelihood allocation (MLA) for allocating subjects to treatments in a clinical trial amounts to checking at each stage which allocation would lead an outside observer to find the least evidence of a relationship between treatment and factors of prognostic significance, assuming that the observer would use a linear exponential model. One advantage of MLA is that results from game theory and likelihood theory can be used to prove it has desirable long run properties. Two of these demonstrated here are (1) ‘consistency’, in the sense that the average likelihood ratio which measures design imbalance tends to zero, and (2) ‘efficiency’ in the sense that the variance estimates of treatment effects will tend to be minimized in the long run.     

《我妻我女》:一个教育与影视人类学的研究展示

家庭教育、学校教育和独生子女教育是目前比较热门的话题,人类学有其独特的考察视角。影片《我妻我女》通过捕捉一对母女生活中的交往过程,展示了独生子女家庭在应对急剧变化的外在世界时所面临的矛盾和困惑。这一作品展示了教育人类学观察世界的独特视角、独特维度。     

A Multivariate Adaptive Exponentially Weighted Moving Average Control Chart

A multivariate extension of the adaptive exponentially weighted moving average (AEWMA) control chart is proposed. The new multivariate scheme can detect small and large shifts in the process mean vector effectively. The proposed scheme can be viewed as a smooth combination of a multivariate exponentially weighted moving average (MEWMA) chart and a Shewhart χ²-chart. The optimal design of the proposed chart is given according to a pre-specified in-control average run length and two shift sizes; a small and large shift each measured in terms of the non centrality parameter. The signal resistance of the newly proposed multivariate chart is also given. Comparisons among the new chart, the MEWMA chart, and the combined Shewhart-MEWMA (S-MEWMA) chart in terms of the standard and worst-case average run length profiles are presented. In addition, the three charts are compared with respect to their worst-case signal resistance values. The proposed chart gives somewhat better worst-case ARL and signal resistance values than the competing charts. It also gives better standard ARL performance especially for moderate and large shifts. The effectiveness of our proposed chart is illustrated through an example with simulated data set.     

Generalized Estimating Equations for Symmetric Distributions Observed with Admixture

A semiparametric two-component mixture model is considered, in which the distribution of one (primary) component is unknown and assumed symmetric. The distribution of the other component (admixture) is known. Generalized estimating equations are constructed for the estimation of the mixture proportion and the location parameter of the primary component. Asymptotic normality of the estimates is demonstrated and the lower bound for the asymptotic covariance matrix is obtained. An adaptive estimation technique is proposed to obtain the estimates with nearly optimal asymptotic variances.     

Combining an Internal Pilot with an Interim Analysis for Single Degree of Freedom Tests

An internal pilot with interim analysis (IPIA) design combines interim power analysis (an internal pilot) with interim data analysis (two-stage group sequential). We provide IPIA methods for single df hypotheses within the Gaussian general linear model, including one and two group t tests. The design allows early stopping for efficacy and futility while also re-estimating sample size based on an interim variance estimate. Study planning in small samples requires the exact and computable forms reported here. The formulation gives fast and accurate calculations of power, Type I error rate, and expected sample size.