Beta-Bernstein Smoothing for Regression Curves with Compact Support

ABSTRACT. The problem of boundary bias is associated with kernel estimation for regression curves with compact support. This paper proposes a simple and uni(r)ed approach for remedying boundary bias in non-parametric regression, without dividing the compact support into interior and boundary areas and without applying explicitly different smoothing treatments separately. The approach uses the beta family of density functions as kernels. The shapes of the kernels vary according to the position where the curve estimate is made. Theyare symmetric at the middle of the support interval, and become more and more asymmetric nearer the boundary points. The kernels never put any weight outside the data support interval, and thus avoid boundary bias. The method is a generalization of classical Bernstein polynomials, one of the earliest methods of statistical smoothing. The proposed estimator has optimal mean integrated squared error at an order of magnitude n ^−4/5, equivalent to that of standard kernel estimators when the curve has an unbounded support.     

On the Rounded Integer-Valued Autoregressive Process

In recent years, there has been a growing interest in modelling integred-valued time series. In this article, we propose a modified and generalized version of the first order rounded integer-valued autoregressive RINAR(1) model, originally introduced by Kachour and Yao (2009 Kachour , M. , Yao , J. F. ( 2009 ). First-order rounded integer-valued autoregressive (RINAR(1)) process . Journal of Time Series Analysis 30 ( 4 ): 417 – 448 .[Crossref], [Web of Science ®] , [Google Scholar]). Indeed, this class can be considered as an alternative of classical models based on the thinning operators. Using a Markov chain method, conditions for stationarity and the existence of moments are investigated. Least squares estimator of the model parameters is considered and its consistence is established. Finally, we describe the price change data using a model of the new class.     

Assessment of supplier performance based on product-development strategy by applying multi-granularity linguistic term sets

Measuring supplier performance possesses the properties of multi-dimension and multi-scale, it increases the complexity of the aggregation and assessment process. A focal company must develop an effective procedure for overcoming the complexity of performance measurement. Furthermore, the assessment of supplier performance must conform to the product-developed strategy of the focal company. This study is a further research deeper into the concept already introduced in Chang et al. [1] [Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. International Journal of Production Economics 2006; 100(2):348–59]; using multi-granularity linguistic variable and numerical ration scale to represent the overall supply performance. By unifying the derived information, the measurement complexity could be resolved. The fuzzy preference was constructed to adjust the consistent direction and transform information into fuzzy relationship. Finally, fuzzy linguistic quantifier guided ordered weighted aggregation (FLQG-OWA) operator with maximal entropy was computed and aggregated with all indicators to meet the current policy of the focal company.     

语言构式的部分能产性问题再探——汉语“很+NP”构式的认知解释

汉语中"很+NP"结构是一个典型的高程度性状义构式。构式的部分能产性基于范畴属性的归纳,其认知机制是非范畴化的。某个具体名词一旦进入"很+NP"构式,这个名词的意义就会遵循它所嵌入的构式的意义,条件是该名词具有可被激活的表示程度性状义的语义因子。构式指示语义类型、构式强制提供了词汇项产生语义类型转变的条件,以保证相应的语义因子获得合适的论元。进入"很+NP"构式的名词都含有该构式所在的具体语境的语义因子,整个过程是以认知语言学的凸显原则为基础的。     

一类标签细胞种群系统的适定性与稳定性

研究一类标签细胞系统的解,使用泛函分析方法,特别是Banach 上的线性算子理论和半群理论,证明了该系统解的适定性、线性稳定性和指数稳定性.     

基于非均匀性语言标度和DLWA算子的动态绩效模糊群决策研究

基于企业人力资源管理(HRM)系统中职能管理部门年度绩效考核决策评价和选优序列生成过程中的数据的不确定性、随机性和模糊性,通过设计季度绩效多维度模糊评价体系,采用非均匀加性语言评估标度定性的多维度评价,构建动态加性语言判断矩阵模型,利用DLWA算子等语言信息集成算子,集成多个决策者不同时期绩效指标评价数据,并整合不同决策者的偏好度,综合呈现年度部门业绩选优序列,优化提升职能管理部门绩效考核决策的精确性、公平性和可信度。     

Adaptive lasso for accelerated hazards models

The important feature of the accelerated hazards (AH) model is that it can capture the gradual effect of treatment. Because of the complexity in its estimation, few discussion has been made on the variable selection of the AH model. The Bayesian non-parametric prior, called the transformed Bernstein polynomial prior, is employed for simultaneously robust estimation and variable selection in sparse AH models. We first introduce a naive lasso-type accelerated hazards model, and later, in order to reduce estimation bias and improve variable selection accuracy, we further consider an adaptive lasso AH model as a direct extension of the naive lasso-type model. Through our simulation studies, we obtain that the adaptive lasso AH model performs better than the lasso-type model with respect to the variable selection and prediction accuracy. We also illustrate the performance of the proposed methods via a brain tumour study.     

Large sample behavior of the Bernstein copula estimator

Bernstein polynomial estimators have been used as smooth estimators for density functions and distribution functions. The idea of using them for copula estimation has been given in Sancetta and Satchell (2004). In the present paper we study the asymptotic properties of this estimator: almost sure consistency rates and asymptotic normality. We also obtain explicit expressions for the asymptotic bias and asymptotic variance and show the improvement of the asymptotic mean squared error compared to that of the classical empirical copula estimator. A small simulation study illustrates this superior behavior in small samples.     

情态操作词的值的变化及意义

对于情态操作词的值的体现传统语法的解释是规范性的,现代语言学虽然提出了不同意见但未形成系统的理论.针对情态操作词的值在政治演讲、劝说文两种语类中的使用,说明变化是意义生成的手段,使得语类之间有相同和不同,也使得同一语类里有变化,这种变化反映了意义是选择系统这一特征.     

基于复杂网络和语言信息的交互式大规模群体评价方法

针对现有基于语言信息的交互式群体评价方法大多仅适用于中小规模群体的情况,提出一种新的基于复杂网络和语言信息的交互式大规模群体评价方法。首先,将评价者视为网络节点,并依据节点之间的距离构建复杂网络;其次,设计节点紧密度和子群紧密度,并确定节点权重和子群权重;再次,定义群体稳定性指标和群体满意度指标,并以此来判断交互终止和确定阶段权重;最后,在节点密度算子和节点加权算子的基础上,分别对单轮群体意见和多轮评价结果进行集结。文末通过一个实际例子验证了所提方法的有效性与合理性。实例分析表明,该方法能够较为全面、准确地集结交互式大规模群体评价意见,且其结果的满意度亦较为理想。