组织学习对员工—组织匹配的影响——知识惯性调节作用的实证研究

本文实证研究组织学习对员工—组织匹配的影响及知识惯性的调节作用。研究发现学习承诺、分享愿景和开放心智对一致性匹配和互补性匹配具有显著正向影响;经验惯性和学习惯性在学习承诺、分享愿景和开放心智对一致性匹配的影响有负向调节作用。     

基于决策者满意度的区间DEA的求解

定义了一种反映决策者满意度的区间数序关系,基于此将区间DEA中的区间不等式约束转化为确定型约束。研究了区间DEA模型向确定型DEA模型转化过程中的数据一致性问题。在保持数据一致性的前提下,根据决策者给出的满意度水平,将区间DEA转化为确定型DEA并进行求解。最后给出算例,并总结和分析了决策单元的DEA效率值随满意度水平变化的规律。     

Posterior consistency for the spectral density of non-Gaussian stationary time series

Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation involving a nonparametric correction of a parametric likelihood has been proposed in the literature with a proof of posterior consistency for spectral density estimation in combination with the Bernstein–Dirichlet process prior for Gaussian time series. In this article, we will extend the posterior consistency result to non-Gaussian time series by employing a general consistency theorem for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood is also extended to non-Gaussian time series. Small sample properties of this approach are illustrated with several examples of non-Gaussian time series.     

正确处理一致性和多样性关系———习近平统战工作中的辩证思维

统战工作涉及到各个领域、各个方面，不掌握工作方法、抓不住主要矛盾，就做不好这项工作。习近平同志站在辩证唯物主义的高度，指出做好新形势下统战工作， 必须正确处理一致性和多样性的关系。通过学习研究发现，正确处理多样性和一致性的关系体现了历史和逻辑、矛盾的普遍性和特殊性的统一，把握好“度”是关键。     

Asymptotics of M-estimator in multivariate linear regression models for a class of random errors

It is known that linear regression models have immense applications in various areas such as engineering technology, economics and social sciences. In this paper, we investigate the asymptotic properties of M-estimator in multivariate linear regression model based on a class of random errors satisfying a generalised Bernstein-type inequality. By using the generalised Bernstein-type inequality, we obtain a general result on almost sure convergence for a class of random variables and then obtain the strong consistency for the M-estimator in multivariate linear regression models under some mild conditions. The result extends or improves some existing ones in the literature. Moreover, we also consider the case when the dimension $p$ tends to infinity by establishing the rate of almost sure convergence for a class of random variables satisfying generalised Bernstein-type inequality. Some numerical simulations are also provided to verify the validity of the theoretical results.     

Continuous-time threshold autoregressions with jumps: Properties,estimation, and application to electricity markets

Continuous-time autoregressive processes have been applied successfully in many fields and are particularly advantageous in the modeling of irregularly spaced or high-frequency time series data. A convenient nonlinear extension of this model are continuous-time threshold autoregressions (CTAR). CTAR allow for greater flexibility in model parameters and can represent a regime switching behavior. However, so far only Gaussian CTAR processes have been defined, so that this model class could not be used for data with jumps, as frequently observed in financial applications. Hence, as a novelty, we construct CTAR processes with jumps in this paper. Existence of a unique weak solution and weak consistency of an Euler approximation scheme is proven. As a closed form expression of the likelihood is not available, we use kernel-based particle filtering for estimation. We fit our model to the Physical Electricity Index and show that it describes the data better than other comparable approaches.     

Priority Rules and Other Asymmetric Rationing Methods

In a rationing problem, each agent demands a quantity of a certain commodity and the available resources fall short of total demand. A rationing method solves this problem at every level of resources and individual demands. We impose three axioms: Consistency—with respect to variations of the set of agents—Upper Composition and Lower Composition—with respect to variations of the available resources. In the model where the commodity comes in indivisible units, the three axioms characterize the family of priority rules, where individual demands are met lexicographically according to an exogeneous ordering of the agents. In the (more familiar) model where the commodity is divisible, these three axioms plus Scale Invariance—independence of the measurement unit—characterize a rich family of methods. It contains exactly three symmetric methods, giving equal shares to equal demands: these are the familiar proportional, uniform gains, and uniform losses methods. The asymmetric methods in the family partition the agents into priority classes; within each class, they use either the proportional method or a weighted version of the uniform gains or uniform losses methods.     

Bayesian neural tree models for nonparametric regression

Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.     

What is the best model of grit among junior high students: Model selection,measurement invariance,and group difference

Grit is a non-cognitive construct based on personality theory that fosters individuals to perform better and achieve their goals. Previous researchers validated the psychometric properties of grit for adults and older adolescents but ignored that for early adolescents. An inappropriate measurement model may mislead researchers and teachers to misapprehend grit measures. This study aimed to identify the most appropriate grit model for early adolescents. Three factorial models (i.e., two-factor, one-factor, and bi-factor models) were evaluated and compared. Subsequently, we tested the measurement invariance (MI) and mean differences of grit across grade and gender groups. Participants were 672 junior high students (51% boys, aged 12–15) in Taiwan. The results showed that the two-factor model was the best grit model. This finding revealed that adolescents tend to comprehend grit with two distinct concepts, consistency in interest (CI) and perseverance of effort (PE), instead of a single compound. In addition, the results showed that the two-factor model retained strong invariance for grade and gender, justifying the comparison of grit scores over grade and gender groups for junior high students. Moreover, we did not find any group difference in the facets of grit by gender and grades. This result showed that grit is a stable personality for junior high students. The relationships between grit constructs and criteria, such as conscientiousness and academic achievements, were tested and discussed.