弘扬自然辩证法传统 建设科学技术学学科群

本文回顾了我国自然辩证法学科和学科群的发展过程,分析了其间的广义理论和狭义理解、分歧和共性,探讨了终将从广义上延拓成科学技术学一级学科,从狭义上形成二级学科群的合理性和现实可能性.基于坚定自然辩证法原理作为科学技术学基础理论及学科建设指南的必然性,建议扩大<自然辩证法研究>副标题的内涵,将目前冗长的副标题改变成简洁的"关于科学技术学的理论性杂志".     

Merging information for semiparametric density estimation

Summary.  The density ratio model specifies that the likelihood ratio of m −1 probability density functions with respect to the m th is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well-established techniques to choose the smoothing parameter for the density estimators proposed.     

关于建立“成都学”的思考

通过一个城市的历史,去透视整个人类文明发展的进程、值得趋避的经验与教训,是城市学研究的根本目的.把具有两千多年历史的成都作为一个聚焦点,可以涉猎城市规模、城市生态系统、社会结构和社会文化,以及地理、历史、人才、经济、技术,城市管理机制、生态机制、监督机制和应变机制等诸多问题,这也都是当今世界性"城市化"浪潮中人们普遍关心的话题.     

Data tilting for time series

Summary. We develop a general methodology for tilting time series data. Attention is focused on a large class of regression problems, where errors are expressed through autoregressive processes. The class has a range of important applications and in the context of our work may be used to illustrate the application of tilting methods to interval estimation in regression, robust statistical inference and estimation subject to constraints. The method can be viewed as 'empirical likelihood with nuisance parameters'.     

理论实证相印语言文化相融--胡正武《训诂阐微集》评议

胡正武新著《训诂阐微集》在训诂方面所展现的,具有理论与实证相得益彰,语言与文化水乳交融,语料丰富内容广博等特点,这是与其深厚扎实的学术功底,别具一格的研究方法相为表里的。胡君甘于寂寞的治学态度,该书朴实无华的语言风格在当今急功近利、充满躁动的商品经济社会里,尤其显示其现实意义。     

Real nonparametric regression using complex wavelets

Summary.  Wavelet shrinkage is an effective nonparametric regression technique, especially when the underlying curve has irregular features such as spikes or discontinuities. The basic idea is simple: take the discrete wavelet transform of data consisting of a signal corrupted by noise; shrink or remove the wavelet coefficients to remove the noise; then invert the discrete wavelet transform to form an estimate of the true underlying curve. Various researchers have proposed increasingly sophisticated methods of doing this by using real-valued wavelets. Complex-valued wavelets exist but are rarely used. We propose two new complex-valued wavelet shrinkage techniques: one based on multiwavelet style shrinkage and the other using Bayesian methods. Extensive simulations show that our methods almost always give significantly more accurate estimates than methods based on real-valued wavelets. Further, our multiwavelet style shrinkage method is both simpler and dramatically faster than its competitors. To understand the excellent performance of this method we present a new risk bound on its hard thresholded coefficients.     

A simple procedure for the selection of significant effects

Summary.  Given a large number of test statistics, a small proportion of which represent departures from the relevant null hypothesis, a simple rule is given for choosing those statistics that are indicative of departure. It is based on fitting by moments a mixture model to the set of test statistics and then deriving an estimated likelihood ratio. Simulation suggests that the procedure has good properties when the departure from an overall null hypothesis is not too small.     

对幼儿园男教师作用的初步研究

在学前教育领域,教师性别倾斜仍然是一个不争的事实。对教师性别比例不平衡的关注导致出现了许多关于在学前教育领域需加大男教师比例的呼吁。但是,有关学前教育领域男教师比例高低的影响的论述一直受实证证据少的限制。采用观察法、访谈法和实物研究等多种方法的对照,有助于获得对这一问题的新资料与新思考。     

An empirical likelihood goodness-of-fit test for time series

Summary. Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model. When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empirical likelihood for an α -mixing process to formulate a test statistic that measures the goodness of fit of a parametric regression model. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. We apply the test to a discretized diffusion model which has recently been considered in financial market analysis.     

The efficacy of using judgmental versus quantitative forecasting methods in practice

In an era where forecasts drive entire supply chains forecasting is seen as an increasingly critical organizational capability. However, business forecasting continues to rely on judgmental methods despite large advancements in information technology and quantitative method capability, prompting calls for research to help understand the reasons behind this practice. Our study is designed to contribute to this knowledge by profiling differences between firms identified as primary users of either judgmental or quantitative forecasting methods. Relying on survey data from 240 firms we statistically analyzed differences between these categories of users based on a range of organizational and forecasting issues. Our study finds large differences in forecast error rates between the two groups, with users of quantitative methods significantly outperforming users of judgmental methods. The former are found to be equally prevalent regardless of industry, firm size, and product positioning strategy, documenting the benefits of quantitative method use in a variety of settings. By contrast, the latter are found to have significantly lower access to quantifiable data and to use information and technology to a lesser degree.