“毛泽东精神”研究四题

毛泽东思想体系具有层次性,毛泽东精神是毛泽东的智慧、情感、意志、理想、信念、人格的结晶,是毛泽东的思想、理论、诗词、书品、生活和实践中蕴含的最深层的本质。毛泽东精神是中华民族的民族精神在20世纪的集中体现,是振兴中华民族强大的精神动力。加强对毛泽东精神的研究、宣传和弘扬,是人民的呼声,时代的需要,是理论工作者责无旁贷的职责。     

Inégalités et mondialisation financière: le rapport qui tombe à pic

Cette note présente la nouvelle publication annuelle de l'Institut international d'études sociales de l'OIT, le Rapport mondial sur le travail 2008, dont le thème est les inégalités de revenu à l'épreuve de la mondialisation financière. Cette publication tombe à point nommé au moment où l'on s'inquiète de l'ampleur, de la durée et des conséquences économiques et sociales de la crise financière. Le rapport traite des déterminants de l'accroissement des inégalités de revenu, et de ses liens avec la mon‐dialisation financière, les institutions du marché du travail, les emplois – création et caractéristiques –, et les politiques de redistribution.     

The foundations of econometrics-are there any

The conclusions of alogically consistent economic theory which strictly adheres to Aristotle's axioms of logic are factually true if its sufficient conditions are all factually true. Alternatively, if a conclusion of such a theory is false, then at- least one of its assumptions is false. Unfortunately, the factual truth of sufficient conditions cannot be established because the problem of induction i s impossible t o solve. It is algo true that the falsity of a conclusion cannot be established in the presence of uncertainty. While the philosophy of instrumentalism applied to sufficient and logically consistent explanations may provide useful solutions to immediate practical problems, the principles of simplicity, parsimony and profligacy--all of them requiring conditional deductive arguments--are useless as criteria for model choice.     

GOES-8 X-ray sensor variance stabilization using the multiscale data-driven Haar–Fisz transform

Summary.  We consider the stochastic mechanisms behind the data that were collected by the solar X-ray sensor (XRS) on board the GOES-8 satellite. We discover and justify a non-trivial mean–variance relationship within the XRS data. Transforming such data so that their variance is stable and its distribution is taken closer to the Gaussian distribution is the aim of many techniques (e.g. Anscombe and Box–Cox). Recently, new techniques based on the Haar–Fisz transform have been introduced that use a multiscale method to transform and stabilize data with a known mean–variance relationship. In many practical cases, such as the XRS data, the variance of the data can be assumed to increase with the mean, but other characteristics of the distribution are unknown. We introduce a method, the data-driven Haar–Fisz transform, which uses the Haar–Fisz transform but also estimates the mean–variance relationship. For known noise distributions, the data-driven Haar–Fisz transform is shown to be competitive with the fixed Haar–Fisz methods. We show how our data-driven Haar–Fisz transform method denoises the XRS series where other existing methods fail.     

Feature significance in generalized additive models

This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete. We consider the problem of determining the significance of features such as peaks or valleys in observed covariate effects both for the case of additive modeling where the main predictor of interest is univariate as well as the problem of studying the significance of features such as peaks, inclines, ridges and valleys when the main predictor of interest is geographical location. We work with low rank radial spline smoothers to allow to the handling of sparse designs and large sample sizes. Reducing the problem to a Generalised Linear Mixed Model (GLMM) framework enables derivation of simulation-based critical value approximations and guards against the problem of multiple inferences over a range of predictor values. Such a reduction also allows for easy adjustment for confounders including those which have an unknown or complex effect on the outcome. A simulation study indicates that our method has satisfactory power. Finally, we illustrate our methodology on several data sets.     

NONPARAMETRIC AUTOCOVARIANCE FUNCTION ESTIMATION

Nonparametric estimators of autocovariance functions for non-stationary time series are developed. The estimators are based on straightforward nonparametric mean function estimation ideas and allow use of any linear smoother (e.g. smoothing spline, local polynomial). The paper studies the properties of the estimators, and illustrates their usefulness through application to some meteorological and seismic time series.     

Measuring the complexity of generalized linear hierarchical models

Measuring a statistical model's complexity is important for model criticism and comparison. However, it is unclear how to do this for hierarchical models due to uncertainty about how to count the random effects. The authors develop a complexity measure for generalized linear hierarchical models based on linear model theory. They demonstrate the new measure for binomial and Poisson observables modeled using various hierarchical structures, including a longitudinal model and an areal‐data model having both spatial clustering and pure heterogeneity random effects. They compare their new measure to a Bayesian index of model complexity, the effective number p_D of parameters (Spiegelhalter, Best, Carlin & van der Linde 2002); the comparisons are made in the binomial and Poisson cases via simulation and two real data examples. The two measures are usually close, but differ markedly in some instances where p_D is arguably inappropriate. Finally, the authors show how the new measure can be used to approach the difficult task of specifying prior distributions for variance components, and in the process cast further doubt on the commonly‐used vague inverse gamma prior.     

Informing the Public About Uncertain Risks

Many problems arise in connection with the communication of risk information. In this article the content of the information communicated is taken as a starting-point for analyzing the risk communication process. We studied the way in which authorities communicated health risks to local residents in Dutch soil pollution situations and found that communication problems were characterized more by misunderstanding and conflict between parties with different views and interests than by a lack of understanding. Therefore, it is important to look more closely at the way the communicators of information (in our cases, officials) select risk information from risk assessments, and to study the effects that the information selected has on the receivers (in our cases, residents). Both the process of selection by the officials and the process of interpretation by the residents will be shown to be influenced by the different institutional backgrounds. The article presents a new approach to risk communication. Both the risk information presented by the authorities and the public reactions to this risk information are considered to reflect the institutional background of authorities and residents, and can be analyzed in these terms. Such an approach has consequences for the study of risk communication and the manner in which it is practiced.