Canonical correlation analysis for the vector AR(1) model with ARCH innovations

This paper extends the results of canonical correlation analysis of Anderson [2002. Canonical correlation analysis and reduced-rank regression in autoregressive models. Ann. Statist. 30, 1134–1154] to a vector AR(1) process with a vector ARCH(1) innovations. We obtain the limiting distributions of the sample matrices, the canonical correlations and the canonical vectors of the process. The extension is important because many time series in economics and finance exhibit conditional heteroscedasticity. We also use simulation to demonstrate the effects of ARCH innovations on the canonical correlation analysis in finite sample. Both the limiting distributions and simulation results show that overlooking the ARCH effects in canonical correlation analysis can easily lead to erroneous inference.     

Estimation of parameters in survey sampling: Optimality

Some recent works on estimation in survey sampling are analyzed and extended from the perspective of the theory of optimal estimating functions.     

On nonparametric profile analysis of several multivariate samples

A unified development is offered for asymptotically distribution-free profile analysis of several multivariate samples. This includes as special cases procedures based on generalized U-statistics and also those based on linear rank statistics. Furthermore, it includes as special cases analysis of location profiles and also scalar profiles. Finally, asymptotic power and consistency properties are discussed for tests of hypotheses and subhypotheses of interest.     

Agricultural policies and development: A socioeconomic investigation applied to Sri Lanka

This paper explores the interrelationship between agricultural policies and development by means of a dynamically recursive, computable general equilibrium model applied to Sri Lanka. The agricultural policies investigated include elimination of the food subsidy, land reform, and technical change in agriculture. The goals considered are the levels and growth rates of GNP and employment, the distribution of income, and the real income level of the lowest income group. The study provides a quantitative assessment of the association between policies and goals and identifies the key economic mechanisms in this association.     

Robustness of the linear discriminant function to nonnormality: Johnson's system

The asymptotic distribution of the Errors of Misclassification in using the Linear Discriminant Function is investigated here. The purpose is to study the effects of nonnormality on these errors. The class of distributions considered is the Johnson's system. Each of the three random variables can be transformed to normality. In one particular case numerical evaluations are made, based on which it is possible to recommend whether or not it is necessary to make the transformation prior to classification. In a parallel study, we present similar results for the Edgeworth Series distribution, where the random variables cannot be transformed to normality.     

Geostochastic calculus

A stochastic calculus for a family of continuous measure-valued Markov processes is developed. Such processes arise naturally in the construction of stochastic models of spatially distributed populations. The stochastic calculus is a tool whereby a class of density-dependent models can be studied in terms of the multiplicative measure diffusion process. In this paper the stochastic integral is introduced in the space-time setting and a Cameron-Martin-Girsanov theorem is established.     

马克思主义视阈下的后现代设计人文属性研究

以马克思主义理论为视角,结合时代语境探讨后现代设计的人文属性和发展趋势具有十分重要的意义。在设计思想中应协调好人与自然、人与社会、人与人等各方面的关系,体现出和谐发展、技术与经济互动和人文精神的吸纳等设计理念的"人文回归",为现今设计理论的发展提供思想依据,使设计成为生态和谐、人文交融、科技发展、经济腾飞的行为前提,从而推动人类科技创新和发展走上健康的轨道。     

《查泰莱夫人的情人》之生态女性主义伦理管窥

D·H·劳伦斯的名作《查泰莱夫人的情人》是一部颇受争议的书,然而其主题却是严肃而深刻的。本文从自然和社会两个方面挖掘书中所体现的生态女性主义伦理:在自然领域,主张人与自然和谐共处;在社会领域,反对阶级歧视,并声援处于边缘的弱势群体——女性。     

Multivariate Kendall's tau for change‐point detection in copulas

Statistical procedures for the detection of a change in the dependence structure of a series of multivariate observations are studied in this work. The test statistics that are proposed are $L_1$ , $L_2$ , and $L_{\infty }$ distances computed from vectors of differences of Kendall's tau; two multivariate extensions of Kendall's measure of association are used. Since the distributions of these statistics under the null hypothesis of no change depend on the unknown underlying copula of the vectors, a procedure based on the multiplier central limit theorem is used for the computation of p‐values; the method is shown to be valid both asymptotically and for moderate sample sizes. Alternative versions of the tests that take into account possible breakpoints in the marginal distributions are also investigated. Monte Carlo simulations show that the tests are powerful under many scenarios of change‐point. In addition, two estimators of the time of change are proposed and their efficiency is carefully studied. The methodologies are illustrated on simulated series from the Canadian Regional Climate Model. The Canadian Journal of Statistics 41: 65–82; 2013 © 2012 Statistical Society of Canada