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.     

Least-square regularized regression with non-iid sampling

We study the least-square regression learning algorithm generated by regularization schemes in reproducing kernel Hilbert spaces. A non-iid setting is considered: the sequence of probability measures for sampling is not identical and the sampling may be dependent. When the sequence of marginal distributions for sampling converges exponentially fast in the dual of a Hölder space and the sampling process satisfies a polynomial strong mixing condition, we derive learning rates for the learning algorithm.     

Rao and Wu's re-scaling bootstrap modified to achieve extended coverages

Horvitz and Thompson's (HT) [1952. A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47, 663–685] well-known unbiased estimator for a finite population total admits an unbiased estimator for its variance as given by [Yates and Grundy, 1953. Selection without replacement from within strata with probability proportional to size. J. Roy. Statist. Soc. B 15, 253–261], provided the parent sampling design involves a constant number of distinct units in every sample to be chosen. If the design, in addition, ensures uniform non-negativity of this variance estimator, Rao and Wu [1988. Resampling inference with complex survey data. J. Amer. Statist. Assoc. 83, 231–241] have given their re-scaling bootstrap technique to construct confidence interval and to estimate mean square error for non-linear functions of finite population totals of several real variables. Horvitz and Thompson's estimators (HTE) are used to estimate the finite population totals. Since they need to equate the bootstrap variance of the bootstrap estimator to the Yates and Grundy's estimator (YGE) for the variance of the HTE in case of a single variable, i.e., in the linear case the YG variance estimator is required to be positive for the sample usually drawn.     

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.     

中国制造业企业创新行为的观测和实证研究 ——基于浙江省制造业1454家企业问卷调查的分析

 本文在浙江省制造业1454家企业大样本调查问卷的基础上,在微观层面研究了企业创新行为以及影响因素。首先利用统计分析的方法考察了企业创新投入的特征、创新投入的行业分布以及创新投入的产出绩效,并且发现了企业规模与R&;D强度之间呈现倒U型关系,这与安同良等人发现的倾斜V型关系的结论不同。其次运用计量分析的方法在有效控制了企业所处行业的技术水平差异、企业产权结构、政府资金扶持、企业品牌和企业设备技术水平后,研究发现：企业规模与企业创新投入之间也呈现较明显的倒U型关系,并且存在规模“拐点”;企业知识产权战略对企业创新投入具有明显的促进作用;出口贸易对企业创新投入并不显著,但发达国家特别是针对发展中国家设置的技术性贸易壁垒的技术标准化要求对企业创新投入有比较明显的促进作用;而企业集聚对企业的创新投入的影响并不确定。     

对四川中等职业教育的现实思考

职业教育是我国人力结构和教育结构调整必须考虑的关键环节,它的发展关系到行业和企业的生存以及社会主义建设的全局。四川中等职业教育规模扩大与经济发展的变化趋势基本一致．扩大规模是现阶段的首要任务。但教师资源匮乏、实训基地不足、教材更新缓慢等现状,使四川职业教育在规模发展的同时面临着质量如何保障的问题。此外,在职教发展中资源分配应权衡“锦上添花”和“雪中送炭”策略,保证公办学校和民办学校的公平竞争,避免因此造成的教育不公平。     

关于中等师范学校布局调整的思考与实践

中国的改革开放带来的中国社会的高速发展以及知识经济呼之欲出的全球大背景,导致了中等师范学校必须转型的历史命运.中等师范学校如何转型?如何面对新的历史条件下的新情况?中等师范学校只有不等、不靠,积极主动地去思考与探索中师布局的调整,才能走出困境,获得新生.     

Saving behaviour and earnings uncertainty: Evidence from the British Household Panel Survey

For the first time, this paper uses a panel data set, the British Household Panel Survey, to analyse saving behaviour in Britain. One objective is to test the precautionary saving hypothesis, according to which households save to self-insure against uncertainty. Our results show that in accordance with this hypothesis, various measures of uncertainty based on earnings variability have a statistically significant effect on households' saving decisions. Moreover, in accordance with the life cycle model, households save more if they expect their financial situation to deteriorate. Received: 15 June 1999/Accepted: 4 January 2001