马哲视角的人存在方式探研

人以社会实践活动方式而存在,从中形成人与自然、人与人、人与社会、人与人自身诸矛盾及人的生产与消费的矛盾,这后一矛盾是人存在方式中的根本矛盾。近代工业文明以来,由于人对自我存在方式的科学性、社会性、预见性和终极价值维度不能给予唯物辩证把握,而陷入存在方式的误区。直面生存危机,以马克思主义哲学为指导,探索建施人全面持续发展为宗旨的存在方式是时代课题。     

劳动力密集地区非均衡流动——对河南省固始县劳动力流动及县域经济发展的调查

在劳动力密集的河南省,大量输出劳动力可以迅速促进当地经济的发展和人民生活水平的提高,但人们往往忽视从长远看过量输出导致的非均衡流动却可能束缚输出地经济的发展,笔者通过对河南省信阳市固始县为期13天的调研,对这一问题进行了研究。本文描述了劳动力的大量输出对当地经济发展的促进,着重分析了当地劳动力流动的原因,并分析了非均衡流动给当地经济未来发展带来的三个问题。文章将在前半部分从劳动力流动的环境动力和劳动力流动去向的角度,结合劳动力流动的相关理论,对谷围村劳动力流动情况进行分析,并用"吸出模型"解释劳动力非均衡流动带来的问题和产生的原因。文章后半部分从劳动力大量输出对当地县域经济影响的角度分析了在劳动力密集的固始县企业招工难且劳动力成本高于外地的奇怪现象。最后,文章对固始县为实现劳动力均衡流动而采取的措施进行了研究,总结出劳动力过量输出地区的经济发展模式即"固始模式",并对该模式进行了分析。     

A partial score test for difference among heterogeneous populations

In event time data analysis, comparisons between distributions are made by the logrank test. When the data appear to contain crossing hazards phenomena, nonparametric weighted logrank statistics are usually suggested to accommodate different-weighted functions to increase the power. However, the gain in power by imposing different weights has its limits since differences before and after the crossing point may balance each other out. In contrast to the weighted logrank tests, we propose a score-type statistic based on the semiparametric-, heteroscedastic-hazards regression model of Hsieh [2001. On heteroscedastic hazards regression models: theory and application. J. Roy. Statist. Soc. Ser. B 63, 63–79.], by which the nonproportionality is explicitly modeled. Our score test is based on estimating functions derived from partial likelihood under the heteroscedastic model considered herein. Simulation results show the benefit of modeling the heteroscedasticity and power of the proposed test to two classes of weighted logrank tests (including Fleming–Harrington's test and Moreau's locally most powerful test), a Renyi-type test, and the Breslow's test for acceleration. We also demonstrate the application of this test by analyzing actual data in clinical trials.     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

Markov Properties for Acyclic Directed Mixed Graphs

We consider acyclic directed mixed graphs, in which directed edges ( x → y ) and bi-directed edges ( x ↔ y ) may occur. A simple extension of Pearl's d -separation criterion, called m -separation, is applied to these graphs. We introduce a local Markov property which is equivalent to the global property resulting from the m -separation criterion for arbitrary distributions.     

Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.     

科学与人文整合的一种模式

遵循整体观和相对性理念，在“认知思维模式”基础上，提出了一种无学科界面、自然融合型的科学与人文整合模式，从根本上解决了科学与人文长期以来的分割与对立的局面。并依据该模式提出了“‘组织结构’决定‘性能＇”法则、“更大层次系统对系统制约的第一性”的“伦理”法则、“子系统之间和谐共存”法则等思想将引导人类走上“天人合一”的和谐永续的文明道路。     

Bayesian locally optimal design of knockout tournaments

The elimination or knockout format is one of the most common designs for pairing competitors in tournaments and leagues. In each round of a knockout tournament, the losers are eliminated while the winners advance to the next round. Typically, the goal of such a design is to identify the overall best player. Using a common probability model for expressing relative player strengths, we develop an adaptive approach to pairing players each round in which the probability that the best player advances to the next round is maximized. We evaluate our method using simulated game outcomes under several data-generating mechanisms, and compare it to random pairings, to the standard knockout format, and to two variants of the standard format.