Stochastic analysis of covariance when the error distribution is long-tailed symmetric

In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

中国城市化进程效率估算及影响因素分析

本文基于2008—2012年中国285个地级市的数据,运用双边随机前沿模型测算了中国城市化进程的前沿水平以及实际水平相对前沿水平的偏离程度。结果发现,下偏效应大于上偏效应,即中国城市化进程整体表现为滞后,并且发现投资作用和政府作用在影响城市化进程中的表现为负向,另外城市化进程也表现出地区间的不平衡。最后根据本文的发现,提出相应的建议。     

汉语方言田野调查与音韵学

从高本汉最早的著作开始就把中国音韵学和方言田野调查紧密地结合起来。在中古音的构拟和间接的上古音研究方面,方言资料是相当重要的角色。早期的方言田野调查以字音为主,后来认识到俗传词才是探索方言历史的关键。     

上市公司存货计量方法选择与决策相关性分析

通过分析新存货准则中引入的存货计量属性,即历史成本计量、公允价值计量、可变现净值和未来现金流量的现值的本质特征,分析了其与决策的相关性,并通过对我国部分上市公司存货计价方法选择的深层分析,揭示了决策相关性在存货计价方法应用中所传递的信息和表达的目的。     

制造型中小区域中创新体系研究

大多数区域创新体系的研究关注具有良好条件的大都市圈,而对创新过程中缺乏战略参与者的中小区域研究较少。本文通过对区域创新体系研究成果的回顾和评论,探讨了制造型中小区域的创新体系构建及其区域创新政策。     

Asymptotic Bias in Parameter Estimation of AR-Processes Using Recursive Least Squares with Exponential Forgetting

The recursive least squares technique is often extended with exponential forgetting as a tool for parameter estimation in time-varying systems. The distribution of the resulting parameter estimates is, however, unknown when the forgetting factor is less than one. In this paper an approximative expression for bias of the recursively obtained parameter estimates in a time-invariant AR( n_a ) process with arbitrary noise is given, showing that the bias is non-zero and giving bounds on the approximation errors. Simulations confirm the approximation expressions.     

Decision settings analysis – a tool for analysis and design of human activity systems

The paper describes a methodology to be used for analysis and design of human activity systems. The methodology is based on an analysis of the decision settings whereas most other decision analysis methodologies are analysing the process. The decision concept is analysed and discussed. A distinction between programmed and programmable as well as non-programmed and non-programmable decisions is proposed. A classification of different information types for decision making is presented. A methodology based on a systemic and systematic analysis of the information requirements of an organization is proposed. This methodology also indicates organizational discrepancies and information imbalances. The methodology focuses the settings of the decisions on all levels of organizations. The methodology can be regarded as a dynamic, learning system. The author proposes further research on the individuals decision making abilities.     

Bayesian estimation of the efficient frontier

In this paper, we consider the estimation of the three determining parameters of the efficient frontier, the expected return, and the variance of the global minimum variance portfolio and the slope parameter, from a Bayesian perspective. Their posterior distribution is derived by assigning the diffuse and the conjugate priors to the mean vector and the covariance matrix of the asset returns and is presented in terms of a stochastic representation. Furthermore, Bayesian estimates together with the standard uncertainties for all three parameters are provided, and their asymptotic distributions are established. All obtained findings are applied to real data, consisting of the returns on assets included into the S&P 500. The empirical properties of the efficient frontier are then examined in detail.     

A systematic literature review of the etiology of borderline personality disorder from an ecological systems PERSPECTIVE

Borderline Personality Disorder (BPD) is one of the most common, complex, costly, and severely impairing personality disorders, affecting an estimated 2% to 9% of the general population and 40% to 44% of the inpatient psychiatric population. A review of the literature was conducted using a systematic methodology. By incorporating an ecological systems perspective, a holistic and comprehensive critique of the literature surrounding the etiology of BPD is presented. The findings reveal that the etiology of BPD is a complex integration of psychological, biological, and social factors. More specifically, however, this review found that: (1) the etiology of BPD is complex and has many factors, (2) the dominant discourse about the etiology of BPD is based primarily in the psychological and biological literature, and (3) the examination of BPD etiology has focused solely on the individual and microsystems levels, neglecting to consider systemic factors such as the impact of discriminatory health and mental healthcare practices. Findings and future directions are explored through the ecological systems theory lens.