The indirect method for stochastic logistic growth models

We developed the indirect method for stochastic logistic growth models involving both birth and death rates in the drift and diffusion coefficients, and not only propose two indirect estimators, but also construct a likelihood ratio-type indirect statistic for testing hypotheses concerning parameters. Simulations show that the proposed two indirect estimators can correct the discretization bias, and the proposed indirect test possesses very good estimated power and size.     

中国需求管理政策研究

介绍需求管理政策内涵,总结中国需求管理政策的特点,设计评价需求管理政策效果的三个指标,即财政政策效果系数、货币政策效果系数和需求管理政策效果系数,依据这些指标作出中国需求管理政策效果越来越差的判断。根据财政政策乘数和货币政策乘数明确影响需求管理政策效果的因素并分析这些因素对需求管理政策效果的影响,最后提出新常态下中国需求管理政策的改进意见。     

Estimation of the failure rate-a survey of nonparametric methods Part I: Non-Bayesian Methods

In this paper several nonparametric, non-Bayesian methods for estimating the failure rate function on which no monotonicity conditions have been imposed are surveyed. The survey attempts to consolidate and synthesize literature from several diverse areas of application, and endeavors to be as up-to-date as is feasible.     

未来中国经济:如何跨越“中等收入陷阱”

在全球经济危机影响下,中国经济面临外部条件恶化、国内问题凸显的考验,有关中国经济高速发展可持续性的争议引人关注。学术界对于"中等收入陷阱"的研究和分析表明,无论命题真伪,确实在一部分发展中国家的经济发展过程中,出现了"中等收入陷阱"的现象和事实。经济发展不仅仅是突破"贫困陷阱"和"人口陷阱"的问题,到一定阶段还会面临"中等收入陷阱"的挑战。中国未来5~10年是一个非常关键的发展时期,关系到是否可以顺利地跨越"中等收入陷阱",进入高收入国家行列。中国必须通过制度创新,彻底转变经济发展方式,这才是未来成功跨越"中等收入陷阱"的战略选择和必由之路。     

实践知识管理：反思与改善教师专业成长的新视点

实践知识是教师在教学的实践经验中获得的知识,它指导着教师如何处理教学事件。但在现实中教师存在着对实践知识不正确的认知或行为,阻碍了教师专业成长。知识管理就是要有效揭示与合理组织教师的实践知识,是反思与改善教师专业成长的新视点。     

福建省人力资本与经济增长关系的研究

根据协整理论，利用福建省1989--2008年的年度统计数据，对福建省财政教育经费和地区生产总值之间的关系进行实证检验。研究表明，要实现福建省经济又好又快发展，必须坚持教育优先发展的战略地位，为建设海峡西岸经济区提供坚实的基础。     

Estimation of a probability density function using interval aggregated data

ABSTRACT

In economics and government statistics, aggregated data instead of individual level data are usually reported for data confidentiality and for simplicity. In this paper we develop a method of flexibly estimating the probability density function of the population using aggregated data obtained as group averages when individual level data are grouped according to quantile limits. The kernel density estimator has been commonly applied to such data without taking into account the data aggregation process and has been shown to perform poorly. Our method models the quantile function as an integral of the exponential of a spline function and deduces the density function from the quantile function. We match the aggregated data to their theoretical counterpart using least squares, and regularize the estimation by using the squared second derivatives of the density function as the penalty function. A computational algorithm is developed to implement the method. Application to simulated data and US household income survey data show that our penalized spline estimator can accurately recover the density function of the underlying population while the common use of kernel density estimation is severely biased. The method is applied to study the dynamic of China's urban income distribution using published interval aggregated data of 1985–2010.     

光照时间、CaO浓度和底质对大瓶螺（Ampullaria gigas）生长繁殖的影响

通过水族缸设置不同因子水平养殖大瓶螺，研究结果表明：光照时间对大瓶螺产卵量有极显著影响；CaO浓度、底质对雄螺的生长速度有显著影响。光照时间对雌螺产卵量起主导因子作用。每天光照时间为12h、CaO浓度为0.12g/L、底质为泥土时大瓶螺产卵量最大、生长速度最快。     

A monte carlo study of variable selection and inferences in a two stage random coefficient linear regression model with unbalanced data

A growth curve analysis is often applied to estimate patterns of changes in a given characteristic of different individuals. It is also used to find out if the variations in the growth rates among individuals are due to effects of certain covariates. In this paper, a random coefficient linear regression model, as a special case of the growth curve analysis, is generalized to accommodate the situation where the set of influential covariates is not known a priori. Two different approaches for seleaing influential covariates (a weighted stepwise selection procedure and a modified version of Rao and Wu’s selection criterion) for the random slope coefficient of a linear regression model with unbalanced data are proposed. Performances of these methods are evaluated by means of Monte-Carlo simulation. In addition, several methods (Maximum Likelihood, Restricted Maximum Likelihood, Pseudo Maximum Likelihood and Method of Moments) for estimating the parameters of the selected model are compared Proposed variable selection schemes and estimators are appliedtotheactualindustrial problem which motivated this investigation.     

Longitudinal data analysis of animal growth via multivariate dynamic models

We propose models to analyze animal growth data with the aim of estimating and predicting quantities of biological and economical interest such as the maturing rate and asymptotic weight. It is also studied the effect of environmental factors of relevant influence in the growth process. The models considered in this paper are based on an extension and specialization of the dynamic hierarchical model (Gamerman & Migon, 1993) to a non–linear growth curve setting, where some of the growth curve parameters are considered exchangeable among the units. The inference for these models are approximate conjugate analysis based on Taylor series expansions and linear Bayes procedures