一类n阶非线性微分方程多点边值问题解的唯一性

本文利用Leray-Schauder度理论建立了n阶非线性微分方程y(n)=f(x,y,y′,…y(n-1)),0     

分析对流-扩散方程显格式稳定性的一种方法

给出了分析二维对流-扩散方程FTCS显格式（时间向前,空间中心的差分格式）稳定条件的一种方法.这种方法可用于分析一维到高维对流-扩散方程差分格式的稳定性.     

半线性热方程解的爆破率

考虑半线性方程ｕｔ－△ｕ＝（１＋ｕ）ｌｎ２＋ｒ（１＋｜ｕ｜）的初边值问题，对解ｕ在爆破时刻Ｔ的爆破率进行估计．     

薄环形相对论电子注介质切伦可夫脉塞

轴向电子注通过介质慢波系统，当波的相速略低于电子注的速度时就可产生相干的切伦可夫辐射，这一现象是一切行波管的基础。本文利用线性理论，通过自洽求解粒子连续性方程和相对论运动方程与麦克斯韦方程组，分析了薄环形相对论电子注介质切伦可夫脉塞的注波互作用，得到了完全描述其薄环形电子注作用的跳变条件，导出了描述其不稳定性的色散方程和注波互作用的同步条件，并求得了波增长率。     

现代木质建筑在中国发展前景分析

目前我国在木结构建筑的现代应用上无论是对材料的认识、选择和加工,还是对木结构建筑的技术要点、设计方法和技术规范等方面,同西方发达国家相比有相当大的差距。本文以南京"爱涛·漪水园"木质别墅为例,介绍了现代木质建筑通常可分为四类:壁式框架类型、预制类型、原木类型和大跨度类型。现代木质住宅建筑具有建设周期短,可移动性强;保温节能,消费合理;防火防潮,隔绝噪声;抗震抗风,持久耐用;设计合理,选择多样;地域不拘,建造灵活等优越性。这些特性加上良好的市场前景,必然会促进现代木质建筑在中国得到高速发展和普及。     

Semiparametric Inference Methods for General Time Scale Models

In this paper we consider semiparametric inference methods for the time scale parameters in general time scale models (Oakes, 1995, Duchesne and Lawless, 2000). We use the results of Robins and Tsiatis (1992) and Lin and Ying (1995) to derive a rank-based estimator that is more efficient and robust than the traditional minimum coefficient of variation (min CV) estimator of Kordonsky and Gerstbakh (1993) for many underlying models. Moreover, our estimator can readily handle censored samples, which is not the case with the min CV method.     

宏观经济指标与上证综指长短期走势的预测

上证综合指数和宏观经济指标,如广义货币供应量、国内生产总值、出口与进口的比值,以及外商直接投资之间存在长期的均衡关系,可以通过宏观经济指标来预测上证综合指数的长期走势。误差修正模型的估计结果表明,上证综合指数具备经济"晴雨表"的功能。     

Marginal Regression of Gaps Between Recurrent Events

Recurrent event data typically exhibit the phenomenon of intra-individual correlation, owing to not only observed covariates but also random effects. In many applications, the population may be reasonably postulated as a heterogeneous mixture of individual renewal processes, and the inference of interest is the effect of individual-level covariates. In this article, we suggest and investigate a marginal proportional hazards model for gaps between recurrent events. A connection is established between observed gap times and clustered survival data with informative cluster size. We subsequently construct a novel and general inference procedure for the latter, based on a functional formulation of standard Cox regression. Large-sample theory is established for the proposed estimators. Numerical studies demonstrate that the procedure performs well with practical sample sizes. Application to the well-known bladder tumor data is given as an illustration.     

Fast and Accurate Inference for the Smoothing Parameter in Semiparametric Models

A fast and accurate method of confidence interval construction for the smoothing parameter in penalised spline and partially linear models is proposed. The method is akin to a parametric percentile bootstrap where Monte Carlo simulation is replaced by saddlepoint approximation, and can therefore be viewed as an approximate bootstrap. It is applicable in a quite general setting, requiring only that the underlying estimator be the root of an estimating equation that is a quadratic form in normal random variables. This is the case under a variety of optimality criteria such as those commonly denoted by maximum likelihood (ML), restricted ML (REML), generalized cross validation (GCV) and Akaike's information criteria (AIC). Simulation studies reveal that under the ML and REML criteria, the method delivers a near‐exact performance with computational speeds that are an order of magnitude faster than existing exact methods, and two orders of magnitude faster than a classical bootstrap. Perhaps most importantly, the proposed method also offers a computationally feasible alternative when no known exact or asymptotic methods exist, e.g. GCV and AIC. An application is illustrated by applying the methodology to well‐known fossil data. Giving a range of plausible smoothed values in this instance can help answer questions about the statistical significance of apparent features in the data.     

一阶非线性中立型微分方程解的振动定理

研究了一类具有变系数和变偏差的一阶非线性中立型方程，建立了方程的所有解振动的Ｓｈａｒｐ条件，并给出了方程的线性化极限振动准则。