关于Directly-Riemann积分的中值定理

本文在研究Directly－Riemann积分~[1，2]的基础上，得到了Directly—Riemann积分的中值定理。     

基于海陆运整体成本的集装箱空箱调运优化研究

现有集装箱空箱调运研究分为陆上调运系统研究和海上调运系统研究,分别建立以成本最小为目标的调运策略优化模型。由于通过海运到某港口的空箱量作为该港口范围内陆运决策的初始条件,两系统分别求解无法获得整体成本最优的调运策略。本文在分析空箱流转过程的基础上,提出建立基于海陆运整体成本最优的集装箱空箱调运模型,并通过算例揭示海运、陆运成本中不同因素对整体成本的影响。     

科学发展观与深化政府综合统计体制改革

 内容提要：目前我国政府综合统计存在难以与时俱进地满足政府调控管理需求、抗干扰能力不够强、基层数据失真风险越来越高等一系列问题，已不能很好地适应在全党、全国贯彻落实科学发展观中担负的使命要求。究其原因，本文认为其核心问题是相关管理体制和制度设计不合理、不规范、缺乏与时俱进的能力。要解决这些问题必须以科学发展观为指引，依法治统为重点，治标与治本相结合，在建立国家权威性协调组织、推进政绩考核评估方法完善、缩减基层工作量以及依法规范国家统计需求和统计体系设计管理方面做切实有效的工作。     

一阶非线性常微分方程可积类型的推广

文中提出新的四类非线性常微分方程，借助某些方法与技巧，论证了这几类方程可用积分法求解，获得的结论推广了文献中相应的结果。     

根据46种唐诗选本统计出的唐代著名诗人

以选本统计的方法，选出唐代主要诗人448名。并列出其中著名诗人名单及主要统计结果，以供学界相关人员参考。     

Theory & Methods: Krige,Smooth, Both or Neither?

Both kriging and non-parametric regression smoothing can model a non-stationary regression function with spatially correlated errors. However comparisons have mainly been based on ordinary kriging and smoothing with uncorrelated errors. Ordinary kriging attributes smoothness of the response to spatial autocorrelation whereas non-parametric regression attributes trends to a smooth regression function. For spatial processes it is reasonable to suppose that the response is due to both trend and autocorrelation. This paper reviews methodology for non-parametric regression with autocorrelated errors which is a natural compromise between the two methods. Re-analysis of the one-dimensional stationary spatial data of Laslett (1994) and a clearly non-stationary time series demonstrates the rather surprising result that for these data, ordinary kriging outperforms more computationally intensive models including both universal kriging and correlated splines for spatial prediction. For estimating the regression function, non-parametric regression provides adaptive estimation, but the autocorrelation must be accounted for in selecting the smoothing parameter.     

Regression Modelling of Disease Risk in Relation to Point Sources

We describe a class of models for the investigation of possible raised risk of disease around putative sources of environmental pollution. An adaptation of the point process method suggested by Diggle and Rowlingson is presented, allowing the use of routinely available aggregated data and incorporating the more general distance–risk model suggested by Elliott and co-workers. An application to data on cancers of the stomach around municipal solid waste incinerators is presented.     

Spatially varying dynamic coefficient models

In this work we present a flexible class of linear models to treat observations made in discrete time and continuous space, where the regression coefficients vary smoothly in time and space. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial heterogeneity in both dimensions. We describe how to perform inference for this class of models and also how to perform forecasting in time and interpolation in space, using simulation techniques. The performance of the algorithm to estimate the parameters of the model and to perform prediction in time is investigated with simulated data sets. The proposed methodology is used to model pollution levels in the Northeast of the United States.     

Exact inference for a simple step-stress model with competing risks for failure from exponential distribution under Type-II censoring

In reliability analysis, accelerated life-testing allows for gradual increment of stress levels on test units during an experiment. In a special class of accelerated life tests known as step-stress tests, the stress levels increase discretely at pre-fixed time points, and this allows the experimenter to obtain information on the parameters of the lifetime distributions more quickly than under normal operating conditions. Moreover, when a test unit fails, there are often more than one fatal cause for the failure, such as mechanical or electrical. In this article, we consider the simple step-stress model under Type-II censoring when the lifetime distributions of the different risk factors are independently exponentially distributed. Under this setup, we derive the maximum likelihood estimators (MLEs) of the unknown mean parameters of the different causes under the assumption of a cumulative exposure model. The exact distributions of the MLEs of the parameters are then derived through the use of conditional moment generating functions. Using these exact distributions as well as the asymptotic distributions and the parametric bootstrap method, we discuss the construction of confidence intervals for the parameters and assess their performance through Monte Carlo simulations. Finally, we illustrate the methods of inference discussed here with an example.     

Bayesian hierarchical regression models for detecting QTLs in plant experiments

Quantitative trait loci (QTL) mapping is a growing field in statistical genetics. In plants, QTL detection experiments often feature replicates or clones within a specific genetic line. In this work, a Bayesian hierarchical regression model is applied to simulated QTL data and to a dataset from the Arabidopsis thaliana plants for locating the QTL mapping associated with cotyledon opening. A conditional model search strategy based on Bayesian model averaging is utilized to reduce the computational burden.