“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。     

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.