Two-stage generalized moment method approach for bidimensional random coefficient autoregressive models

ABSTRACT

A two-dimensionally indexed random coefficients autoregressive models (2D ? RCAR) and the corresponding statistical inference are important tools for the analysis of spatial lattice data. The study of such models is motivated by their second-order properties that are similar to those of 2D ? (G)ARCH which play an important role in spatial econometrics. In this article, we study the asymptotic properties of two-stage generalized moment method (2S ? GMM) under general asymptotic framework for 2D ? RCA models. So, the efficiency, strong consistency, the asymptotic normality, and hypothesis tests of 2S ? GMM estimation are derived. A simulation experiment is presented to highlight the theoretical results.     

Ultrastructural elliptical models

Dolby's (1976) ultrastructural model with no replications is investigated within the class of the elliptical distributions. General asymptotic results are given for the sample covariance matrix S in the presence of incidental parameters. These results are used to study the asymptotic behaviour of some estimators of the slope parameter, unifying and extending existing results in the literature. In particular, under some regularity conditions they are shown to be consistent and asymptotically normal. For the special case of the structural model, some asymptotic relative efficiencies are also reported which show that generalized least squares and the method of moment estimators can be highly inefficient under nonnormality.     

汉语言的二维指向与中国古代文论的简与繁

汉字的“象”性构造和多样性外衍指向,使古代文论的表达具有简约和繁复两种风格。对于深刻的义理用以少总多或具象隐喻“一语而尽”;对于经验世界的多样性,则上下左右反复详言之,使“情貌无遗”。这两种风格,使文论述说表现出虚实相生、繁简结合的诗性之美。     

Tests for a difference in timing of physiological response between two brain regions measured by using functional magnetic resonance imaging

Summary.  Functional magnetic resonance imaging (FMRI) measures the physiological response of the human brain to experimentally controlled stimulation. In a periodically designed experiment it is of interest to test for a difference in the timing (phase shift) of the response between two anatomically distinct brain regions. We suggest two tests for an interregional difference in phase shift: one based on asymptotic theory and one based on bootstrapping. Whilst the two procedures differ in some of their assumptions, both tests rely on employing the large number of voxels (three-dimensional pixels) in non-activated brain regions to take account of spatial autocorrelation between voxelwise phase shift observations within the activated regions of interest. As an example we apply both tests, and their counterparts assuming spatial independence, to FMRI phase shift data that were acquired from a normal young woman during performance of a periodically designed covert verbal fluency task. We conclude that it is necessary to take account of spatial autocovariance between voxelwise FMRI time series parameter estimates such as the phase shift, and that the most promising way of achieving this is by modelling the spatial autocorrelation structure from a suitably defined base region of the image slice.     

推翻表征思想 建立游牧思想——精神分裂症分析方法研究

德勒兹和加塔里精神分裂症分析理论与精神分析理论之间是一种在断裂的基础上发展的关系。聚焦于二者的指导思想和方法的差异,特别关照精神分裂症分析的独特思想和思维主张及其生成过程,可详细解读其游牧式或曰“根茎式”的新理念。     

性别身份与舞台表现——从《欲望号街车》到《美梦重圆》

性别身份,尤其是同性恋身份所具有的社会政治色彩决定了不同时代的剧作家在处理这一题材时采用不同的舞台表现形式。《欲望号街车》采用了象征手法,充分利用舞台上的资源———灯光、音乐、枪声、回忆、幻觉等来暗示自杀的同性恋丈夫对妻子留下的道义上的内疚与自责。对《欲望号街车》的重写剧作《美梦重圆》则采用“异装”、戏中戏等形式大胆直白地表达这一主题。两剧对同性恋身份舞台表现形式的不同不仅带有特定历史时期的烙印,而且渗透了剧作家不同的审美价值取向。     

缺失历史的艺术再现——解读美国少年历史小说《龙翼》

美国少年历史小说《龙翼》通过叙述视角、场景、人物塑造等艺术手段描写了以一对华人父子为主要人物的华人早期移民克服困难,最终实现飞行梦想的故事,从而忠实再现了20世纪初美国华人移民艰苦奋斗的经历,补写了一段美国历史中一度缺失的历史;同时也为华人移民后代了解先辈的历史,为美国这一多民族移民的国家处理各民族的关系提供了生动且真实的素材。     

A class of partially linear single‐index survival models

The authors define a class of “partially linear single‐index” survival models that are more flexible than the classical proportional hazards regression models in their treatment of covariates. The latter enter the proposed model either via a parametric linear form or a nonparametric single‐index form. It is then possible to model both linear and functional effects of covariates on the logarithm of the hazard function and if necessary, to reduce the dimensionality of multiple covariates via the single‐index component. The partially linear hazards model and the single‐index hazards model are special cases of the proposed model. The authors develop a likelihood‐based inference to estimate the model components via an iterative algorithm. They establish an asymptotic distribution theory for the proposed estimators, examine their finite‐sample behaviour through simulation, and use a set of real data to illustrate their approach.     

Using a Markov Chain to Construct a Tractable Approximation of an Intractable Probability Distribution

Abstract.  Let π denote an intractable probability distribution that we would like to explore. Suppose that we have a positive recurrent, irreducible Markov chain that satisfies a minorization condition and has π as its invariant measure. We provide a method of using simulations from the Markov chain to construct a statistical estimate of π from which it is straightforward to sample. We show that this estimate is 'strongly consistent' in the sense that the total variation distance between the estimate and π converges to 0 almost surely as the number of simulations grows. Moreover, we use some recently developed asymptotic results to provide guidance as to how much simulation is necessary. Draws from the estimate can be used to approximate features of π or as intelligent starting values for the original Markov chain. We illustrate our methods with two examples.     

On the Distance Between Cumulative Sum Diagram and Its Greatest Convex Minorant for Unequally Spaced Design Points

Abstract.  The supremum difference between the cumulative sum diagram, and its greatest convex minorant (GCM), in case of non-parametric isotonic regression is considered. When the regression function is strictly increasing, and the design points are unequally spaced, but approximate a positive density in even a slow rate ( n ^−1/3), then the difference is shown to shrink in a very rapid (close to n ^−2/3) rate. The result is analogous to the corresponding result in case of a monotone density estimation established by Kiefer and Wolfowitz, but uses entirely different representation. The limit distribution of the GCM as a process on the unit interval is obtained when the design variables are i.i.d. with a positive density. Finally, a pointwise asymptotic normality result is proved for the smooth monotone estimator, obtained by the convolution of a kernel with the classical monotone estimator.