First‐order bias correction for fractionally integrated time series

Most of the long memory estimators for stationary fractionally integrated time series models are known to experience non‐negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this article, the authors propose bias reduction methods for a lag‐one sample autocorrelation‐based moment estimator. In order to reduce the bias of the moment estimator, the authors explicitly obtain the exact bias of lag‐one sample autocorrelation up to the order n⁻¹. An example where the exact first‐order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. The authors show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation. The proposed methods are applied to the northern hemisphere data. The Canadian Journal of Statistics 37: 476–493; 2009 © 2009 Statistical Society of Canada     

The Collapsing Choice Theory: Dissociating Choice and Judgment in Decision Making

Decision making theory in general, and mental models in particular, associate judgment and choice. Decision choice follows probability estimates and errors in choice derive mainly from errors in judgment. In the studies reported here we use the Monty Hall dilemma to illustrate that judgment and choice do not always go together, and that such a dissociation can lead to better decision-making. Specifically, we demonstrate that in certain decision problems, exceeding working memory limitations can actually improve decision choice. We show across four experiments that increasing the number of choice alternatives forces people to collapse choices together, resulting in better decision-making. While choice performance improves, probability judgments do not change, thus demonstrating an important dissociation between choice and probability judgments. We propose the Collapsing Choice Theory (CCT) which explains how working memory capacity, probability estimation, choice alternatives, judgment, and regret all interact and effect decision quality.      

An Empirical Strategy to Detect Spurious Effects in Long Memory and Occasional-Break Processes

Long-range dependence and structural changes in level are intimely related phenomena and it is very difficult to separate the two effects. In this article, we present an empirical procedure to distinguish between long-memory and occasional-break processes. An extensive Monte Carlo experiment illustrates the performance of the procedure and an application to real data is also included.     

Statistical properties of detrended fluctuation analysis

The main goal of this work is to consider the detrended fluctuation analysis (DFA), proposed by Peng et al. [Mosaic organization of DNA nucleotides, Phys. Rev. E. 49(5) (1994), 1685–1689]. This is a well-known method for analysing the long-range dependence in non-stationary time series. Here we describe the DFA method and we prove its consistency and its exact distribution, based on the usual i.i.d. assumption, as an estimator for the fractional parameter d. In the literature it is well established that the nucleotide sequences present long-range dependence property. In this work, we analyse the long dependence property in view of the autoregressive moving average fractionally integrated ARFIMA(p, d, q) processes through the analysis of four nucleotide sequences. For estimating the fractional parameter d we consider the semiparametric regression method based on the periodogram function, in both classical and robust versions; the semiparametric R/S(n) method, proposed by Hurst [Long term storage in reservoirs, Trans. Am. Soc. Civil Eng. 116 (1986), 770–779] and the maximum likelihood method (see [R. Fox and M.S. Taqqu, Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series, Ann. Statist. 14 (1986), 517–532]), by considering the approximation suggested by Whittle [Hypothesis Testing in Time Series Analysis (1953), Hafner, New York].     

Head Cocoons

ABSTRACT

Listening with the aid of headphones was at the heart of the early radio culture of the 1920s. After those years, however, earphones became the exception as loudspeakers became the norm. For quite some time thereafter, the wiring of hearing was limited to the use of monaural earbuds in situations where the use of loudspeakers would have disturbed others. In West Germany, the binaural headphone started to make a comeback in the late 1960s due to the growing fragmentation of family life along with the widening range of electronic leisure possibilities and the rise of hi-fi culture. It was only at the end of the twentieth century, however, that binaural headphone listening emerged as the dominant culture of listening and caught up with the burgeoning mobile urban life style.

Taking the case of West Germany, this article asks: What is the impact of ear-wiring on social life? And, what new kinds of perception result from headphone listening? It traces how the use of earphones turned from being a static “technique of listening” into a mobile “technique of acoustic privatization.” Ultimately, this article interprets today's portable headphones as “head cocoons” that enable mobile listeners to actively carve out sonic privacy while on the move.     

哲学的新方向与终极关怀新解──兼就拙著《进入澄明之境》答梁志学先生问

西方自苏格拉底—柏拉图到黑格尔两千多年间的哲学追问方式,就其占统治地位的方面而言,是以超感性、超时空的抽象概念为万事万物之根底的“在场形而上学”。现当代人文主义思潮的哲学家们实行了哲学追问方式的转向,认为任何当前“在场的东西”都以其背后无限与之相联系的、“不在场的东西”为根底,无限的不在场者也是现实的东西而非抽象的概念。这一哲学新方向带来了对终极关怀的新理解：人生的最高意义和价值在于不断超越有限的在场,与不在场的无限性融为一体。     

书写绝望与反抗绝望--浙江三位先锋小说家创作评述

浙江三位先锋小说家余华、王彪、艾伟分别从人性的层面、欲望的层面、记忆的层面对存在进行了相同的极端书写,描绘出了一幅相同的绝望的生存景观,又都以各自的方式对这"绝望"进行着反抗.     

论维柯创造说及其对古希腊摹仿说的发展

维柯《新科学》在西方思想史上占据着特殊地位。维柯在《新科学》中提出一套关于创造的学说:(一)创造活动与摹仿、想象、记忆等功能密不可分,它们的整体构成"诗性智慧"。(二)以想象力为标准,可划分出两种创造:隐喻的想象与"重聚的"想象,后一种创造活动完全据有并呈显创造的本质。维柯创造说继承并发展了古希腊的摹仿说,正确揭示出摹仿与想象在创造中的关系。     

《史记》研究资料整理和索引编制概述

历代有关<史记>研究的资料汗牛充栋,既多且杂.明代以来,人们通过辑评、辑佚、旧籍重刊等方法,对之进行搜集整理,取得了很大的成绩.为查检各种资料而编制的索引,也多种多样,值得重视.文章对这两方面的成就,作了全面地梳理概述.     

基于LSTM神经网络的金融时间序列预测

本文提出将小波分析与纳入时间序列依赖特征的长短期记忆（LSTM）神经网络相结合，构建金融时间序列数据预测模型，以克服现有模型对金融时间序列数据非平稳、非线性、序列相关等复杂特征以及数据间非线性交互关系无法反映的缺陷。同时，以道琼斯工业指数日收盘价为例，探究LSTM神经网络对实际金融时间序列数据的预测能力，比较其与多层感知机、支持向量机、K近邻、GARCH四种模型的预测效果。实证结果表明LSTM神经网络具有更高的预测精度，能够有效预测金融时间序列数据的长短期动态变化趋势，说明了其对金融时间序列数据预测的适用性与有效性。此外，对金融时间序列数据进行小波分解与重构，可有效提高LSTM预测模型的泛化能力，以及对长短期动态趋势的预测精度。