深圳十年规划建设理论与实践初探

本文根据深圳十年规划与建设经验,对市场经济条件下的城市规划理论、乡镇城市化、开放型城市的交通与电讯网络、城市生态环境的保护、投资软硬环境的建设,以及如何提高城市规划的科学水平等问题作了探讨。     

单李代数不可约表示的标记

对于n秩单李代数g,当采用Dynkin关于素根的分类时,其不可约表示可以用n个非负整数Λ_(αi)标记,也可通过初等表示的权用数组li来标记。利用Cartan逆矩阵,我们给出了计算Λ_(α_i)与li之间关系的方法。对于B_n、C_n、D_n和F_4,利用Cartan逆矩阵证明了这些li是与采用Cartan关于素根的分类时的Λi是一致的。     

1998年狮子座流星暴的一些预测

1998年狮子座流星暴是本年度最重要的天象之一。而广东省处于观测的最有利位置,为了不错过这次十分难得的机会,本文叙述了狮子座流量暴的本质及其观测工作的意义,求出了该流星暴母体的部分轨道根数;对1998年狮子座流星暴作了一些预测;对观测方法提出了一些建议,本次预测虽用的是天文专业的方法,但为了使更多的观测员理解,本文尽可能使用最基础的知识。     

多方发挥物理实验教学的职能作用

当前我国医学教育中物理实验教学存在弱点:对物理教育不重视,实验条件差,仪器、设备陈旧,实验选项过时,实验课时少.针对存在的问题及现状,在现有的条件之内,我们提出了物理实验课的改进措施:认真做好实验准备,发挥教师的主导作用,督查学生亲自动手,利用现有仪器设备,不断改善实验条件,重视教学情况的反馈信息.     

税费运用形式的国际比较及借鉴

探讨与研究各国对税费形式运用的理论原则与操作方式,对指导我国税费形式运用选择、实现政府提出的"费改税"目标具有一定的借鉴作用和启示.目前应加大税收在政府收入以及GDP中所占比例,建立以税收为主、以收费为辅的政府分配机制,清理各种不合理收费,把必要的具有税收性质的收费纳入规范化的税收轨道,并不断完善现行税制.     

Weighted local linear composite quantile estimation for the case of general error distributions

It is known that for nonparametric regression, local linear composite quantile regression (local linear CQR) is a more competitive technique than classical local linear regression since it can significantly improve estimation efficiency under a class of non-normal and symmetric error distributions. However, this method only applies to symmetric errors because, without symmetric condition, the estimation bias is non-negligible and therefore the resulting estimator is inconsistent. In this paper, we propose a weighted local linear CQR method for general error conditions. This method applies to both symmetric and asymmetric random errors. Because of the use of weights, the estimation bias is eliminated asymptotically and the asymptotic normality is established. Furthermore, by minimizing asymptotic variance, the optimal weights are computed and consequently the optimal estimate (the most efficient estimate) is obtained. By comparing relative efficiency theoretically or numerically, we can ensure that the new estimation outperforms the local linear CQR estimation. Finite sample behaviors conducted by simulation studies further illustrate the theoretical findings.     

A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials

The objective of this research was to demonstrate a framework for drawing inference from sensitivity analyses of incomplete longitudinal clinical trial data via a re‐analysis of data from a confirmatory clinical trial in depression. A likelihood‐based approach that assumed missing at random (MAR) was the primary analysis. Robustness to departure from MAR was assessed by comparing the primary result to those from a series of analyses that employed varying missing not at random (MNAR) assumptions (selection models, pattern mixture models and shared parameter models) and to MAR methods that used inclusive models. The key sensitivity analysis used multiple imputation assuming that after dropout the trajectory of drug‐treated patients was that of placebo treated patients with a similar outcome history (placebo multiple imputation). This result was used as the worst reasonable case to define the lower limit of plausible values for the treatment contrast. The endpoint contrast from the primary analysis was ? 2.79 (p = .013). In placebo multiple imputation, the result was ? 2.17. Results from the other sensitivity analyses ranged from ? 2.21 to ? 3.87 and were symmetrically distributed around the primary result. Hence, no clear evidence of bias from missing not at random data was found. In the worst reasonable case scenario, the treatment effect was 80% of the magnitude of the primary result. Therefore, it was concluded that a treatment effect existed. The structured sensitivity framework of using a worst reasonable case result based on a controlled imputation approach with transparent and debatable assumptions supplemented a series of plausible alternative models under varying assumptions was useful in this specific situation and holds promise as a generally useful framework. Copyright © 2012 John Wiley & Sons, Ltd.     

Linear models that allow perfect estimation

The general Gauss–Markov model, Y = Xβ + e, E(e) = 0, Cov(e) = σ ² V, has been intensively studied and widely used. Most studies consider covariance matrices V that are nonsingular but we focus on the most difficult case wherein C(X), the column space of X, is not contained in C(V). This forces V to be singular. Under this condition there exist nontrivial linear functions of Q′Xβ that are known with probability 1 (perfectly) where ${C(Q)=C(V)^\perp}$ . To treat ${C(X) \not \subset C(V)}$ , much of the existing literature obtains estimates and tests by replacing V with a pseudo-covariance matrix T = V + XUX′ for some nonnegative definite U such that ${C(X) \subset C(T)}$ , see Christensen (Plane answers to complex questions: the theory of linear models, 2002, Chap. 10). We find it more intuitive to first eliminate what is known about Xβ and then to adjust X while keeping V unchanged. We show that we can decompose β into the sum of two orthogonal parts, β = β ₀ + β ₁, where β ₀ is known. We also show that the unknown component of X β is ${X\beta_1 \equiv \tilde{X} \gamma}$ , where ${C(\tilde{X})=C(X)\cap C(V)}$ . We replace the original model with ${Y-X\beta_0=\tilde{X}\gamma+e}$ , E(e) = 0, ${Cov(e)=\sigma^2V}$ and perform estimation and tests under this new model for which the simplifying assumption ${C(\tilde{X}) \subset C(V)}$ holds. This allows us to focus on the part of that parameters that are not known perfectly. We show that this method provides the usual estimates and tests.