Modelling heterogeneous space–time occurrences of earthquakes and its residual analysis

Summary. Earthquake intensities are modelled as a function of previous activity whose specific form is based on established empirical laws in seismology, but whose parameter values can vary from place to place. This model is used for characterizing regional features of seismic activities in and around Japan, and also for exploring regions where the actual seismicity rate systematically deviates from that of the modelled rate.     

Adaptive tests of regression functions via multiscale generalized likelihood ratios

Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. She derives the asymptotic joint distribution of the component tests and that of the proposed test under the null hypothesis. She also develops a simple method of selecting the smoothing parameters for the proposed test and presents two approximate methods for obtaining its P‐value. Finally, she evaluates the proposed test through simulations and illustrates its application to a set of real data.     

论美术新课程教学过程的特征

新的《全日制义务教育美术课程标准(实验稿)》,要求美术教育以发展学生的艺术素养、人文素养及促进学生的发展为主要目标。新标准要求下的美术教育与传统美术教育相比,教学过程具有学生中心性、综合性、过程性、实践性、开放性、创造性、师生互动性、美育性、兴趣性和个性性等新的特征。     

如何行成关系数据模型到面向对象数据模型的映射

关系数据模型是基于记录的基本数据模型;面向对象数据模型是基于对象的基本数据模型;两种数据模型在某种程度上应存在相互映射和相互转变的可能性,它们的相互映射和相互转变将使关系数据库和面向对象数据库之间的转变成为可能。     

神性、人性和理性--略论艺术形态与时代精神

艺术的本质在于其精神性,从艺术和时代精神的相互关系中去确立划分艺术形态的标准是符合艺术本质的.按照艺术所包含的精神内容的不同,艺术形态可分为神性艺术、人性艺术和理性艺术,以丰富的艺术史例可以说明其特征.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables, Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.     

名词性复合词的意义建构诸理论述评

名词性复合词的意义建构是一个看似简单,实际上非常复杂的过程。传统语法把这一意义建构过程简单化,认为名词性复合词的意义等于各组成部分名词的意义总和。转换生成语言学家 意义特征理论学家也都提出了各自的理论解释,但都有很大的缺陷。概念合成语言学理论对名词性复合词的意义建构做出了比较完整的理论解释,但也有待于进一步的改进。     

ROBUST METHODS FOR DEPENDENT CELL PEDIGREE DATA

This paper studies a robust approach to the analysis of cell pedigree data, building on the work of Huggins & Marschner (1991) which discussed M-estimation for the so-called bifurcating autoregressive process. The study allows for incomplete observation of the pedigree, and incorporates the possibility of additive effects outliers, as discussed in the time series literature. Some properties of the proposed estimation procedure are studied, including a Monte Carlo investigation of robustness in the presence of contamination.     

Generalized Laplacian approximations in Bayesian inference

This paper presents a new Laplacian approximation to the posterior density of η = g(θ). It has a simpler analytical form than that described by Leonard et al. (1989). The approximation derived by Leonard et al. requires a conditional information matrix R_η to be positive definite for every fixed η. However, in many cases, not all R_η are positive definite. In such cases, the computations of their approximations fail, since the approximation cannot be normalized. However, the new approximation may be modified so that the corresponding conditional information matrix can be made positive definite for every fixed η. In addition, a Bayesian procedure for contingency-table model checking is provided. An example of cross-classification between the educational level of a wife and fertility-planning status of couples is used for explanation. Various Laplacian approximations are computed and compared in this example and in an example of public school expenditures in the context of Bayesian analysis of the multiparameter Fisher-Behrens problem.