社会主义核心价值体系与多元文化时代价值观培育

改革开放的历史进程客观上导致中国社会多元文化碰撞，一元化价值体系在一定程度上被颠覆和解构，而建立在多元利益结构之上的多元价值观却尚未真正形成。确立社会主义核心价值体系是多元文化时代价值观培育的关键。以社会主义核心价值体系引领多元文化时代价值观，要坚持不懈地用马克思列宁主义、毛泽东思想、邓小平理论和“三个代表”重要思想武装全党，使充满差异性和多样性的多元文化价值现在主导文化下整合，并进而创建出符合社会主义初级阶段国情和全面建设小康社会需要的文化，形成新的文化体系。     

Structured kernel quantile regression

Quantile regression can provide more useful information on the conditional distribution of a response variable given covariates while classical regression provides informations on the conditional mean alone. In this paper, we propose a structured quantile estimation methodology in a nonparametric function estimation setup. Through the functional analysis of variance decomposition, the optimization of the proposed method can be solved using a series of quadratic and linear programmings. Our method automatically selects relevant covariates by adopting a lasso-type penalty. The performance of the proposed methodology is illustrated through numerical examples on both simulated and real data.     

Non-parametric Variance Estimation from Ergodic Samples

It is shown that a kernel estimate for the variance function is uniformly strongly consistent, under the ergodic condition     

A Non-stationary Cox Model

The purpose of this paper is to consider the problem of statistical inference about a hazard rate function that is specified as the product of a parametric regression part and a non-parametric baseline hazard. Unlike Cox's proportional hazard model, the baseline hazard not only depends on the duration variable, but also on the starting date of the phenomenon of interest. We propose a new estimator of the regression parameter which allows for non-stationarity in the hazard rate. We show that it is asymptotically normal at root- n and that its asymptotic variance attains the information bound for estimation of the regression coefficient. We also consider an estimator of the integrated baseline hazard, and determine its asymptotic properties. The finite sample performance of our estimators are studied.     

Nonparametric regression for threshold data

Consider a detector which records the times at which the endogenous variable of a nonparametric regression model exceeds a certain threshold. If the error distribution is known, the regression function can still be identified from these threshold data. The author constructs estimators for the regression function that are transformations of kernel estimators. She determines the bandwidth that minimizes the asymptotic mean average squared error. Her investigation was motivated by recent work on stochastic resonance in neuroscience and signal detection theory, where it was observed that detection of a subthreshold signal is enhanced by the addition of noise. The author compares her model with several others that have been proposed in the recent past.     

Generalizations of the δ-Corrected Kolmogorov-Smirnov Goodness-of-Fit Test

This paper generalizes the δ-corrected Kolmogorov–Smirnov (K–S) test into a family of tests, and investigates the behaviour of some of the members of the family, comparing them with the usual K–S test, as well as the δ-corrected K–S tests suggested by Harter et al . (1984) and Khamis (1990, 1992, 1993). These investigations and power studies suggest which tests are most powerful for which alternative hypotheses.     

Tracking Edges, Corners and Vertices in an Image

Abstract.  In a range of imaging problems, particularly those where the images are of man-made objects, edges join at points which comprise three or more distinct boundaries between textures. In such cases the set of edges in the plane forms what a mathematician would call a planar graph. Smooth edges in the graph meet one another at junctions, called 'vertices', the 'degrees' of which denote the respective numbers of edges that join there. Conventional image reconstruction methods do not always draw clear distinctions among different degrees of junction, however. In such cases the algorithm is, in a sense, too locally adaptive; it inserts junctions without checking more globally to determine whether another configuration might be more suitable. In this paper we suggest an alternative approach to edge reconstruction, which combines a junction classification step with an edge-tracking routine. The algorithm still makes its decisions locally, so that the method retains an adaptive character. However, the fact that it focuses specifically on estimating the degree of a junction means that it is relatively unlikely to insert multiple low-degree junctions when evidence in the data supports the existence of a single high-degree junction. Numerical and theoretical properties of the method are explored, and theoretical optimality is discussed. The technique is based on local least-squares, or local likelihood in the case of Gaussian data. This feature, and the fact that the algorithm takes a tracking approach which does not require analysis of the full spatial data set, mean that it is relatively simple to implement.     

Semiparametric Bayesian inference for regression models

This paper presents a method for Bayesian inference for the regression parameters in a linear model with independent and identically distributed errors that does not require the specification of a parametric family of densities for the error distribution. This method first selects a nonparametric kernel density estimate of the error distribution which is unimodal and based on the least-squares residuals. Once the error distribution is selected, the Metropolis algorithm is used to obtain the marginal posterior distribution of the regression parameters. The methodology is illustrated with data sets, and its performance relative to standard Bayesian techniques is evaluated using simulation results.     

Bootstrap Bandwidth Selection Using an h-Dependent Pilot Bandwidth

Abstract.  The problem of choosing the bandwidth h for kernel density estimation is considered. All the plug-in-type bandwidth selection methods require the use of a pilot bandwidth g . The usual way to make an h -dependent choice of g is by obtaining their asymptotic expressions separately and solving the two equations. In contrast, we obtain the asymptotically optimal value of g for every fixed h , thus making our selection 'less asymptotic'. Exact error expressions show that some usually assumed hypotheses have to be discarded in the asymptotic study in this case. Two versions of a new bandwidth selector based on this idea are proposed, and their properties are analysed through theoretical results and a simulation study.