法国彝学研究述评

法国彝学是在天主教的传播和殖民势力的扩张共同作用下促成的学科。它以彝文化一致性为前提,遵循如下原则进行研究:以汉籍为主要依据,结合彝文本和碑文来识别族群,推测历史变迁;对语言进行共时比较研究;以复述传说的方式探讨族源;观察和记录社会风俗习惯;使用体质人类学方法测量人种特征。法国彝学的族群识别,有着早期殖民利益介入的企图,考虑到彝族群内部的文化趋同,隐含了一种文化融合研究重于种族甄别的描述性研究模式。     

瑶族招郎仪式中族群文化认同的建构——以南岭走廊中段西岭山FMP瑶族村为例

在经济社会迅速发展、族群互动日益频繁的当今世界,生存在同一区域的不同族群,既要维持族内的文化认同,又要接纳强势的外族文化,才能确保本族的正常生存与发展。如何抉择并保证两种甚至多种认同互不冲突,招郎仪式对此做出了明确回答。仪式中展示的建构族内、族际文化认同的方式方法,体现出瑶族群众充分利用各种场所和可能建构族群文化认同。它是瑶族在主动适应社会变迁的同时,又灵活保存本族文化的重要手段之一。     

Adaptive Estimation of Periodic First-Order Threshold Autoregressive Model

This paper focuses on the adaptive estimation problem of a Periodic Self-Exciting Threshold Autoregressive (PSETAR) model. The adapted sufficient conditions of Swensen (1985 Swensen, A. R. 1985. The asymptotic distribution of the likelihood ratio for autoregressive time series with a regression trend. Journal of Multivariate Analysis, 16: 54–70. [Crossref], [Web of Science ®] , [Google Scholar]) to our model, are verified and then explored to establish the Local Asymptotic Normality (LAN), the Local Asymptotic Quadratic (LAQ) and the Local Asymptotic properties satisfied by its central sequence. Using these results, we construct adaptive estimators for the parameter model where the innovation density is unspecified but symmetric, while satisfying only some general conditions. The performances of these adaptive estimations are shown via simulation studies and an application on the modeling of the Fraser River data.     

On Infinitely Divisible Exponential Dispersion Model Related to Poisson-Exponential Distribution

We construct a univariate exponential dispersion model comprised of discrete infinitely divisible distributions. This model emerges in the theory of branching processes. We obtain a representation for the Lévy measure of relevant distributions and characterize their laws as Poisson mixtures and/or compound Poisson distributions. The regularity of the unit variance function of this model is employed for the derivation of approximations by the Poisson-exponential model. We emphasize the role of the latter class. We construct local approximations relating them to properties of special functions and branching diffusions.     

Nonparametric Estimation of Multiplicative Counting Process Intensity Functions with an Application to the Beijing SARS Epidemic

The local polynomial methods and martingale estimating equations are used to develop closed form estimators of the intensity function and its derivatives for multiplicative counting process models. The consistency and asymptotic normality of the estimators are established. The estimator generalizes that proposed by Ramlau-Hansen (1983 Ramlau-Hansen , H. ( 1983 ). Smoothing counting process intensities by means of kernel function . Ann. Statist. 11 ( 2 ): 453 – 466 . [Google Scholar]) with a smaller bias than the Ramlau-Hansen intensity estimator. The derivative estimators give smoother estimates than the Ramlau-Hansen derivative estimators. The proposed estimators are applied to analyze the infection rate and its derivatives of the 2003 Severe Acute Respiratory Syndrome (SARS) epidemic in Beijing, China.     

Open-Set Nearest Shrunken Centroid Classification

Nearest Shrunken Centroid (NSC) classification has proven successful in ultra-high-dimensional classification problems involving thousands of features measured on relatively few individuals, such as in the analysis of DNA microarrays. The method requires the set of candidate classes to be closed. However, open-set classification is essential in many other applications including speaker identification, facial recognition, and authorship attribution. The authors review closed-set NSC classification, and then propose a diagnostic for whether open-set classification is needed. The diagnostic involves graphical and statistical comparison of posterior predictions of the test vectors to the observed test vectors. The authors propose a simple modification to NSC that allows the set of classes to be open. The open-set modification posits an unobserved class with a distribution of features just barely consistent with the test sample. A tuning constant reflects the combined considerations of power, specificity, multiplicity, number of features, and sample size. The authors illustrate and investigate properties of the diagnostic test and open-set NSC classification procedure using several example data sets. The diagnostic and the open-set NSC procedures are shown to be useful for identifying vectors that are not consistent with any of the candidate classes.     

Estimation in Partially Linear Single-Index Models with Missing Covariates

In this article, we consider a partially linear single-index model Y = g(Z ^τθ₀) + X ^τβ₀ + ? when the covariate X may be missing at random. We propose weighted estimators for the unknown parametric and nonparametric part by applying weighted estimating equations. We establish normality of the estimators of the parameters and asymptotic expansion for the estimator of the nonparametric part when the selection probabilities are unknown. Simulation studies are also conducted to illustrate the finite sample properties of these estimators.     

Local Influence Diagnostics in the Modified Ridge Regression

Calculations of local influence curvatures have been well developed when the ordinary least squares method is applied. In this article, we discuss the assessment of local influence under the modified ridge regression. Using a pseudo-likelihood function, we express the normal curvatures of local influence for three useful perturbation schemes in interpretable forms. Two illustrative examples are analyzed by the methodology developed in the article.     

Diagnostics for a Linear Model with First-Order Autoregressive Symmetrical Errors

In this article, we focus on some diagnostics for linear regression model with first-order autoregressive and symmetrical errors. The symmetrical class includes both light- and heavy-tailed univariate symmetrical distributions, which offers a more flexible framework for modeling. Maximum likelihood estimates are computed via the Fisher-score method. Score statistic and its adjustment are proposed for testing autocorrelation of the random errors. Local influence diagnostics are also derived for the model under some usual perturbation schemes. The performances of the test statistics are investigated through Monte Carlo simulations. Finally, a real data set is used to illustrate our diagnostic methods.     

Nonparametric Estimation of Volatility Function with Variable Bandwidth Parameter

In this article, we introduce a new method for the volatility function estimation of continuous-time diffusion process dX _t  = μ(X _t )dt + σ(X _t )dW _t , which is based on combining the idea of local linear smoother and variable bandwidth. We give the expressions for the conditional MSE and MISE of the estimator and obtain the optimal variable bandwidth. An explicit formula for the optimal variable bandwidth is presented by minimizing the MISE, which extends the related results in Fan and Gijbels (1992 Fan , J. Q. , Gijbels , I. ( 1992 ). Variable bandwidth and local linear regression smoother . Ann. Statist. 20 ( 4 ): 2008 – 2036 .[Crossref], [Web of Science ®] , [Google Scholar]), etc. Finally, some simulations show that the performance of the proposed estimator with optimal variable bandwidth is often much better than that of the local linear estimator with invariable bandwidth.