Choice of Parametric Accelerated Life and Proportional Hazards Models for Survival Data: Asymptotic Results

We discuss the impact of misspecifying fully parametric proportional hazards and accelerated life models. For the uncensored case, misspecified accelerated life models give asymptotically unbiased estimates of covariate effect, but the shape and scale parameters depend on the misspecification. The covariate, shape and scale parameters differ in the censored case. Parametric proportional hazards models do not have a sound justification for general use: estimates from misspecified models can be very biased, and misleading results for the shape of the hazard function can arise. Misspecified survival functions are more biased at the extremes than the centre. Asymptotic and first order results are compared. If a model is misspecified, the size of Wald tests will be underestimated. Use of the sandwich estimator of standard error gives tests of the correct size, but misspecification leads to a loss of power. Accelerated life models are more robust to misspecification because of their log-linear form. In preliminary data analysis, practitioners should investigate proportional hazards and accelerated life models; software is readily available for several such models.     

Semiparametric Inference Methods for General Time Scale Models

In this paper we consider semiparametric inference methods for the time scale parameters in general time scale models (Oakes, 1995, Duchesne and Lawless, 2000). We use the results of Robins and Tsiatis (1992) and Lin and Ying (1995) to derive a rank-based estimator that is more efficient and robust than the traditional minimum coefficient of variation (min CV) estimator of Kordonsky and Gerstbakh (1993) for many underlying models. Moreover, our estimator can readily handle censored samples, which is not the case with the min CV method.     

货币政策规则的理论发展与启示

货币政策规则(尤其是"泰勒规则")自被提出以来,就深受关注,并在与"相机抉择"的政策决策原则的交锋中不断得到发展。现在的货币政策规则不再局限于货币当局设定固定的货币工具变量的狭隘涵义,而是指货币政策行为的一种指令性的向导,具体包括工具规则与目标规则。经济学家相信,与相机决策相比,规则性的政策能有效减少货币政策的动态不一致性。但是,现阶段在中国建立货币政策规则,尤其是实行通货膨胀目标规则的货币政策的条件并不成熟,而加快金融、财政和计划体制的改革,提高中央银行的独立性,增强政策的透明度,努力提高央行货币政策的一致性的信誉,则是我国的当务之急。     

论章太炎经学诠释思想渊源--《齐物论释》与佛学的关联意义

作为从晚清走向近代意义上的国学殿军领袖,章太炎既继承了传统古文经学的朴学精神,又不拘泥于传统的束缚,在传统经学的诠释中展现了其独特的思想方法.本文以<齐物论释>为研究文本,讨论了章太炎以<庄子>为传统资源对佛学更高境界--"随顺成俗"的标新,通过披检章氏对<齐物论>术语的重新诠释,展现其对庄子思想的时间维度上的关怀与重视,并关注由此引发的章氏子学向以<答问>为代表的经学(史学)转向,试图廓清其经、史著述思想的体用关系.     

“社会必要劳动时间”的歧义性研究——认识分歧的客观原因探讨之一

"社会必要劳动时间"本身具有多种语义;马克思在<资本论>中也是从多个语义的角度使用"社会必要劳动时间"的;马克思使用多语义的"社会必要劳动时间"有六个方面的原因.     

马克思主义在中国与时俱进

马克思主义和中国革命实践相结合 ,开拓了中国革命的新阶段。中国共产党以马克思主义为指导 ,从建党、领导革命战争到建国后的社会主义革命和建设 ,始终在实践中不断前进 ,在理论上不断创新 ,最高成果是产生了中国的马克思主义———毛泽东思想、邓小平理论和江泽民“三个代表”重要思想。马克思主义在中国与时俱进 ,发展着的马克思主义是中国社会主义事业不断进步的思想武器。     

试论时间价值观

时间是一种重要的资源。时间价值观是个体以自身的需要为尺度对时间重要性的稳定态度和观念。时间价值观含有时间资源观、时间效率观、时机观、时间顺序观等方面的意义。时间价值观的结构可以有不同的划分。时间价值观受社会文化传统、个体的年龄因素、个体性格、社会环境等因素的影响。心理学对时间价值观的研究在理论上和应用中都有着重要的意义     

The densest <Emphasis Type="Italic">k</Emphasis>-subgraph problem on clique graphs

The Densest k-Subgraph (DkS) problem asks for a k-vertex subgraph of a given graph with the maximum number of edges. The problem is strongly NP-hard, as a generalization of the well known Clique problem and we also know that it does not admit a Polynomial Time Approximation Scheme (PTAS). In this paper we focus on special cases of the problem, with respect to the class of the input graph. Especially, towards the elucidation of the open questions concerning the complexity of the problem for interval graphs as well as its approximability for chordal graphs, we consider graphs having special clique graphs. We present a PTAS for stars of cliques and a dynamic programming algorithm for trees of cliques. M.L. is co-financed within Op. Education by the ESF (European Social Fund) and National Resources. V.Z. is partially supported by the Special Research Grants Account of the University of Athens under Grant 70/4/5821.     

An efficient polynomial space and polynomial delay algorithm for enumeration of maximal motifs in a sequence

In this paper, we consider the problem of enumerating all maximal motifs in an input string for the class of repeated motifs with wild cards. A maximal motif is such a representative motif that is not properly contained in any larger motifs with the same location lists. Although the enumeration problem for maximal motifs with wild cards has been studied in Parida et al. (2001), Pisanti et al. (2003) and Pelfrêne et al. (2003), its output-polynomial time computability has been still open. The main result of this paper is a polynomial space polynomial delay algorithm for the maximal motif enumeration problem for the repeated motifs with wild cards. This algorithm enumerates all maximal motifs in an input string of length n in O(n ³) time per motif with O(n) space, in particular O(n ³) delay. The key of the algorithm is depth-first search on a tree-shaped search route over all maximal motifs based on a technique called prefix-preserving closure extension. We also show an exponential lower bound and a succinctness result on the number of maximal motifs, which indicate the limit of a straightforward approach. The results of the computational experiments show that our algorithm can be applicable to huge string data such as genome data in practice, and does not take large additional computational cost compared to usual frequent motif mining algorithms. This work is done during the Hiroki Arimura’s visit in LIRIS, University Claude-Bernard Lyon 1, France.     

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。