Likelihood-based cross-validation score for selecting the smoothing parameter in maximum penalized likelihood estimation

Maximum penalized likelihood estimation is applied in non(semi)-para-metric regression problems, and enables us exploratory identification and diagnostics of nonlinear regression relationships. The smoothing parameter A controls trade-off between the smoothness and the goodness-of-fit of a function. The method of cross-validation is used for selecting A, but the generalized cross-validation, which is based on the squared error criterion, shows bad be¬havior in non-normal distribution and can not often select reasonable A. The purpose of this study is to propose a method which gives more suitable A and to evaluate the performance of it.

A method of simple calculation for the delete-one estimates in the likeli¬hood-based cross-validation (LCV) score is described. A score of similar form to the Akaike information criterion (AIC) is also derived. The proposed scores are compared with the ones of standard procedures by using data sets in liter¬atures. Simulations are performed to compare the patterns of selecting A and overall goodness-of-fit and to evaluate the effects of some factors. The LCV-scores by the simple calculation provide good approximation to the exact one if λ is not extremeiy smaii Furthermore the LCV scores by the simple size it possible to select X adaptively They have the effect, of reducing the bias of estimates and provide better performance in the sense of overall goodness-of fit. These scores are useful especially in the case of small sample size and in the case of binary logistic regression.     

Approximating the risk score for disease diagnosis using MARS

In disease screening and diagnosis, often multiple markers are measured and combined to improve the accuracy of diagnosis. McIntosh and Pepe [Combining several screening tests: optimality of the risk score, Biometrics 58 (2002), pp. 657–664] showed that the risk score, defined as the probability of disease conditional on multiple markers, is the optimal function for classification based on the Neyman–Pearson lemma. They proposed a two-step procedure to approximate the risk score. However, the resulting receiver operating characteristic (ROC) curve is only defined in a subrange (L, h) of false-positive rates in (0,1) and the determination of the lower limit L needs extra prior information. In practice, most diagnostic tests are not perfect, and it is usually rare that a single marker is uniformly better than the other tests. Using simulation, I show that multivariate adaptive regression spline is a useful tool to approximate the risk score when combining multiple markers, especially when ROC curves from multiple tests cross. The resulting ROC is defined in the whole range of (0,1) and is easy to implement and has intuitive interpretation. The sample code of the application is shown in the appendix.     

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.     

数字机顶盒软件设计的研讨

在数字电视系统中,数字机顶盒是该系统的关键部件.随着用户需求的不断增加,机顶盒的功能也在不断地增多,对软件的开发和存储提出了更高的要求.F lash存储系统具有电可擦、可重写非易失性,存储量大,成本低,无机械故障等优点,在机顶盒软件中能够提供一个良好的存储环境.本文主要是通过利用F lash数据存储系统平台,探讨HDTV机顶盒软件的解决方案.     

前置中频放大器性能的改善

通信机中的收信前置中频放大器 ,其性能的高低将直接影响整机技术指标。但其性能很不稳定。通过多次实验 ,证实其原因为 :晶体管选择不合适 ;屏蔽盒尺寸选择不当。通过实验得出了改善前置中频放大器性能的方法     

激光选区熔化CoCrFeMnNi高熵合金微观组织及力学性能研究

为了探究采用金属增材制造技术对高熵合金的制备,课题组采用激光选区熔化技术（SLM）成功制备冶金结合良好的CoCrFeMnNi试样,并对成形件的微观组织及力学性能进行分析。研究发现SLM成形的CoCrFeMnNi高熵合金相为单一面心立方相（FCC）FeNi,样品微观组织在不同成形方向上呈各向异性：沿水平方向,内部以胞状晶为主;沿成形方向,内部以柱状晶为主,组织为“鱼鳞”状形貌,单个柱状晶粒最长为70 μm,穿过多个成形层并显现外延生长特征。EDS结果表明SLM成形件的各元素分布平均,未呈现显著的成分偏析。拉伸测试结果显示SLM成形件屈服强度和抗拉强度为569 MPa和690 MPa,延伸率为16％,相较于铸态件分别提高了145％和24％,而塑性比铸态件降低了63％。SLM成形件拉伸断口形貌呈现以脆性断裂为主的混合断裂特征。     

基于激光扫描和工业机器人的不锈钢保温杯自动焊接

由于激光焊接对保温杯的位置精度要求很高,依靠传统的工装夹具已无法满足要求,一种由笔者设计的激光扫描的视觉系统对保温杯杯口和杯底进行非接触扫描测量,对激光视觉采集的数据进行平滑处理,得到光滑的三维轨迹。焊缝轨迹平滑处理包括改进的RANSAC算法和均匀三次B样条曲线拟合相结合的方式。最后,设计模糊控制进行焊缝在线跟踪控制。实验结果表明,该方法可以获得平滑精确的保温杯杯口和杯底三维测量,明显提高了焊接质量。     

基于连续小线段曲率连续全局光顺算法

小线段所描述的曲线轨迹在相邻线段的转角处,由于在切线方向会发生突变,曲率也不连续,从而导致了刀具加工中,在通过转角处时会发生速度波动和加速度突变。笔者提出一种基于三次B样条插值拟合的连续小线段路径全局光顺算法,对满足转角和弓高误差约束的连续小线段进行插值拟合,使拟合刀具路径达到G2连续,从而有效提高进给速度,改善加工质量,提高加工效率。最后通过仿真实验验证了本文中算法是有效的。     

旋转电极式电除尘器清灰刷试验装置搭建

为了解决盘类转子的动平衡问题,研究设计一种立式的自动平衡机,建立三维机构模型。通过有限元分析的方 法,对其底面的箱体的框架和立柱进行载荷的力学性能分析,得到箱体的应力变化规律。结果验证了平衡机箱体在设计 上的合理性及可行性,表明箱体整体满足设计的标准要求,可为同类型动平衡机整体及部件的设计优化提供了理论依据 与参考,可有效降低产品开发的成本和缩短研发的周期。     

基于5次非均匀B样条曲线的4自由度机器人轨迹规划

为降低工业机器人工作过程中的振动与冲击,减少磨损以延长使用寿命,课题组以某公司SCARA 4自由度机器人为研究对象,采用5次非均匀B样条曲线对其关节空间进行轨迹规划研究。利用弦长参数法和平均值法选取节点,将离散路径点作为型值点反解出B样条控制点;在端点处进行运动约束,通过指定的端点导矢,在MATLAB中进行仿真实验,并与5次多项式函数插值进行对比分析。分析结果显示：5次非均匀B样条插值出的机器人运行轨迹平稳连续,且各关节速度、加速度以及加加速度轨迹曲线亦光滑连续无突变。该插值方法可有效提高机器人工作的平稳性,非常适合于精确抓取和搬运领域。