Characterizations based on order statistics under sampling without replacement

In this paper, we consider finite populations and investigate their characterizations by regressions of order statistics under sampling without replacement. We also investigate some asymptotic results when the size of the population goes to infinity.     

Estimation in capture–recapture models when covariates are subject to measurement errors and missing data

For capture–recapture models when covariates are subject to measurement errors and missing data, a set of estimating equations is constructed to estimate population size and relevant parameters. These estimating equations can be solved by an algorithm similar to the EM algorithm. The proposed method is also applicable to the situation when covariates with no measurement errors have missing data. Simulation studies are used to assess the performance of the proposed estimator. The estimator is also applied to a capture–recapture experiment on the bird species Prinia flaviventris in Hong Kong. The Canadian Journal of Statistics 37: 645–658; 2009 © 2009 Statistical Society of Canada     

Characteristic function estimation of non-Gaussian Ornstein–Uhlenbeck processes

Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given their flexibility in modelling stylized features of financial series such as asymmetry, heavy tails and jumps. The use of non-Gaussian marginal distributions makes likelihood analysis of these processes unfeasible for virtually all cases of interest. This paper exploits the self-decomposability of the marginal laws of OU processes to provide explicit expressions of the characteristic function which can be applied to several models as well as to develop efficient estimation techniques based on the empirical characteristic function. Extensions to OU-based stochastic volatility models are provided.     

Flexible univariate and multivariate models based on hidden truncation

A broad spectrum of flexible univariate and multivariate models can be constructed by using a hidden truncation paradigm. Such models can be viewed as being characterized by a basic marginal density, a family of conditional densities and a specified hidden truncation point, or points. The resulting class of distributions includes the basic marginal density as a special case (or as a limiting case), but also includes an array of models that may unexpectedly include many well known densities. Most of the well known skew-normal models (developed from the seed distribution popularized by Azzalini [(1985). A class of distributions which includes the normal ones. Scand. J. Statist. 12(2), 171–178]) can be viewed as being products of such a hidden truncation construction. However, the many hidden truncation models with non-normal component densities undoubtedly deserve further attention.     

Semiparametric estimation for count data through weighted distributions

This paper is concerned with semiparametric discrete kernel estimators when the unknown count distribution can be considered to have a general weighted Poisson form. The estimator is constructed by multiplying the Poisson estimate with a nonparametric discrete kernel-type estimate of the Poisson weight function. Comparisons are then carried out with the ordinary discrete kernel probability mass function estimators. The Poisson weight function is thus a local multiplicative correction factor, and is considered as the uniform measure to detect departures from the equidispersed Poisson distribution. In this way, the effects of dispersion and zero-proportion with respect to the standard Poisson distribution are also minimized. This method of estimation is also applied to the weighted binomial form for the count distribution having a finite support. The proposed estimators, in addition to being simple, easy-to-implement and effective, also outperform the competing nonparametric and parametric estimators in finite-sample situations. Two examples illustrate this new semiparametric estimation.     

中国"十五"计划经济发展速度的引证

论证"十五"计划经济发展速度的前提是要正确分析和判断经济发展趋势、经济现象与经济规律的吻合程度,经济运行的工作质量和寻求解决问题的途径和办法。影响国内生产总值增长速度的因素有全社会固定资产投资总额、社会消费品零售总额、价格指数、进出口总额、实际利用外资、财政收入、居民储蓄和城镇居民人均可支配收入及农民人均纯收入八项。固定资产投资、消费品零售总额、进出口总额是经济增长可持续发展的关键因素,必须重视价格指数的正面影响,关注资本的投入量和资本的使用效率,关注财政投资对宏观调控能力的影响,增加城镇中低收入居民的可支配收入和增加农民收入是启动消费市场带动经济全面回升的重要步骤。     

Ecosystem resilience, specialized adaptation and population decline: A modern Malthusian theory

The purpose of this article is to construct a theoretical framework characterizing the interactions among economic development, ecosystem equilibrium and possible population decline, and to discuss the population dynamics in the very long run. In our framework, economic activities bridge population and environment. On the one hand, human beings reform the environment through economic activities; on the other hand, economic activities decrease environmental resilience and increase the possibility of an environmental change in a discontinuous and irreversible pattern, as described in Arrow et al. (1995). Furthermore, a highly developed economy also causes over-specialization of human adaptation, which tends to exaggerate the impact of an environmental change on human population size. Received: 19 January 1999/Accepted: 3 July 1999