Optimal Detection of a Change Point in a Poisson Process for Different Observation Schemes

Abstract.  Change point problems are considered where at some unobservable time the intensity of a point process ( T_n ), n ∈  N , has a jump. For a given reward functional we detect the change point optimally for different information schemes. These schemes differ in the available information. We consider three information levels, namely sequential observation of ( T_n ), ex post decision after observing the point process up to a fixed time t * and a combination of both observation schemes. In all of these cases the detection problem is viewed as an optimal stopping problem which can be solved by deriving a semimartingale representation of the gain process and applying tools from filtering theory.     

双曲型绝对风险厌恶函数的最优消费与投资组合的显示解

对于资产价格服从几何Brown运动的连续时间消费投资组合问题,在假设个人的效用函数属于双曲型绝对风险厌恶函数族的条件下,简化了模型,得到了最优消费投资组合策略的显示解.并证明了几个关于最优解的重要定理.     

Goodness-of-fit Tests Based on the Kernel Density Estimator

Abstract.  Given an i.i.d. sample drawn from a density f on the real line, the problem of testing whether f is in a given class of densities is considered. Testing procedures constructed on the basis of minimizing the L ₁-distance between a kernel density estimate and any density in the hypothesized class are investigated. General non-asymptotic bounds are derived for the power of the test. It is shown that the concentration of the data-dependent smoothing factor and the 'size' of the hypothesized class of densities play a key role in the performance of the test. Consistency and non-asymptotic performance bounds are established in several special cases, including testing simple hypotheses, translation/scale classes and symmetry. Simulations are also carried out to compare the behaviour of the method with the Kolmogorov-Smirnov test and an L ₂ density-based approach due to Fan [ Econ. Theory 10 (1994) 316].     

Kernel spline regression

The authors propose «kernel spline regression,» a method of combining spline regression and kernel smoothing by replacing the polynomial approximation for local polynomial kernel regression with the spline basis. The new approach retains the local weighting scheme and the use of a bandwidth to control the size of local neighborhood. The authors compute the bias and variance of the kernel linear spline estimator, which they compare with local linear regression. They show that kernel spline estimators can succeed in capturing the main features of the underlying curve more effectively than local polynomial regression when the curvature changes rapidly. They also show through simulation that kernel spline regression often performs better than ordinary spline regression and local polynomial regression.     

Gaining acceptability for the Bayesian decision‐theoretic approach in dose‐escalation studies

There has recently been increasing demand for better designs to conduct first‐into‐man dose‐escalation studies more efficiently, more accurately and more quickly. The authors look into the Bayesian decision‐theoretic approach and use simulation as a tool to investigate the impact of compromises with conventional practice that might make the procedures more acceptable for implementation. Copyright © 2005 John Wiley & Sons, Ltd.     

基于神经网络的超声医学图像自动分割

图像分割是多维超声医学图像重建中最重要和最困难的问题。文中将传统的最近邻分类方法与自组织神经网络相结合，提出了一种超声医学图像的自动分割方法。实验表明，与传统的Ｋ平均方法相比，该方法除具有自动分割优点外，还具有稳定性好，自适应强，分割准确等优点。     

用于地形跟踪回避的一种最佳路径算法

提出了一套用于数字地形图中求取最佳路径的算法。算法的核心是改进的动态规划算法，同时还用到图像和图形处理中的一些算法，如图像的膨胀，图形的扫描线转换。分析了算法的计算复杂度，包括时间和空间复杂度。该算法的适应性和稳定性较好，在大多数的情况下能给出符合实际的结果算法还加入了最大爬升角和最小转弯半径这两个飞机机动参数。作为一套完整的考虑飞机机动参数的算法，具有高效稳定和符合实际的特点。     

DDS阵列频率源技术研究

杂散抑制差和输出频率低是DDS的两大缺点,并且随着输出带宽的增加杂散性能更加恶化。传统的解决方法是采用更好的算法和改进DDS芯片本身的结构。该文在分析了DDS杂散性能和输出带宽相互制约的DDS阵列方法基础上,尝试了一种新的解决办法棗DDS阵列方法。同时还分析了这种方法的性能,并通过实验验证了其可行性。     

Data Sharpening for Hazard Rate Estimation

Data sharpening is a general tool for enhancing the performance of statistical estimators, by altering the data before substituting them into conventional methods. In one of the simplest forms of data sharpening, available for curve estimation, an explicit empirical transformation is used to alter the data. The attraction of this approach is diminished, however, if the formula has to be altered for each different application. For example, one could expect the formula for use in hazard rate estimation to differ from that for straight density estimation, since a hazard rate is a ratio–type functional of a density. This paper shows that, in fact, identical data transformations can be used in each case, regardless of whether the data involve censoring. This dramatically simplifies the application of data sharpening to problems involving hazard rate estimation, and makes data sharpening attractive.     

公交网络最优路径的一种改进求解算法

通过对多种公交网络中求解最优路径算法的分析,提出了一种考虑公交线路票价变化,并以总行程时间最短与换乘次数最少相结合为原则的公交路径寻优新算法.同时对公交换乘中换乘点的选择、步行时间及等车时间作了较详细的分析.以一个算例对新算法的有效性进行了验证.