Random partition indicator and spacing statistics

Several conventional and new goodness-of-fit techniques are developed to assess random partitioning of a finite population into disjoint subsets. These nonparametric methods focus on indicator, spacing, and placement statistics. Mathematical development reminiscent of Fourier analysis yields distribution-free orthogonal indicator and spacing component goodness-of-fit procedures for simple random sampling assessment. Providing directional and omnibus criteria to detect between-subset differences, a main contribution of this survey is a unified treatment of the rank indicator procedures in Boos (1986) and the rank spacing methods in Kaigh (1994) with immediate applications to the nonparametric K-sample problem     

中国树文化内涵及渊源探析

作为一种文化载体,树木具有着丰富的文化内涵,并对中国整个古代文化的形成与发展产生了重大的影响。探讨树文化内涵及其形成过程对研究古代文化、促进现代文明有重要意义。该文从树文化的渊源、内涵及其意义方面对此进行了论述。     

太阳神树、太阳崇拜与太阳历法

从三星堆遗址中出土的许多考古材料都与古蜀人的太阳崇拜和历法有关,而作为典型器物之一的青铜“太阳神树”,就是古蜀人太阳崇拜和使用太阳历法的最佳体现。它不仅具有“日、鸟、天、龙、树”等宗教崇拜的象征意义和神话色彩,又是古蜀时期历法等科学知识的结晶,它充分反映了三星堆文化中辩证、和谐的思维和审美意识,内涵极为丰富,极具研究价值。     

母语与外语"知识-认知双元结构"在FLT中的理论解析

本文从心理语言学的角度,就成年人学习和习得外语,并针对诺姆·乔姆斯基的"语言习得装置"(language acquisition device,即LAD)理论中的不足部分,提出了"语言种子与语言习得树"(language seed and language acquisition tree,即LS&LAT)、"外语习得中母语与外语的认知与知识双元结构理论"和"成年人习得外语在心智认知活动上存在有三种认知结构系统的观点"等观点,并就此进行了解析.文章指出,以上学术观点是就成年人学习和习得外语在理论上对乔姆斯基理论的补充和深化.     

心灵在神圣的远古之乡流淌——试论吕德安诗歌的美学意蕴

吕德安的诗歌有着＂简单＂与＂纯净＂的品格,显现出单纯而自然的乡村谣曲气息。在口语语态和纯净心态合一的直接呈现里,体现出个体灵魂的澄明和对平和温蔼优美人性的抒写。他的创作回避了沉重的话题和丑恶的事物,以对世界的善良与诗意的感觉方式,超越着庸俗与病态,在古朴的乡土和本真的世俗中寄寓着自己的人生理想。他是一个渴望借助诗歌的翅膀实现飞离现实之丑的诗人。     

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.