Adaptive Free-Knot Splines

This article proposes a function estimation procedure using free-knot splines as well as an associated algorithm for implementation in nonparametric regression. In contrast to conventional splines with knots confined to distinct design points, the splines allow selection of knot numbers and replacement of knots at any location and repeated knots at the same location. This exibility leads to an adaptive spline estimator that adapts any function with inhomogeneous smoothness, including discontinuity, which substantially improves the representation power of splines. Due to uses of a large class of spline functions, knot selection becomes extremely important. The existing knot selection schemes—such as stepwise selection—suffer the difficulty of knot confounding and are unsuitable for our purpose. A new knot selection scheme is proposed using an evolutionary Monte Carlo algorithm and an adaptive model selection criterion. The evolutionary algorithm locates the optimal knots accurately, whereas the adaptive model selection strategy guards against the selection error in searching through a large candidate knot space. The performance of the procedure is examined and illustrated via simulations. The procedure provides a significant improvement in performance over the other competing adaptive methods proposed in the literature. Finally, usefulness of the procedure is illustrated by an application to actual dataset.     

Ein dichteunabhängiges Neutronenfeuchtemeßgerät

Voruntersuchungen ergaben, daß sich der Dichteeinfluß bei der Neutronenfeuchtemessung kompensieren läßt. Eine Meßanordnung muß dazu zwei Zählraten liefern, die jede für sich linear nur vom Wassergehalt je Volumeneinheit bzw. von der Dichte abhängt. Mit Hilfe eines Datenverarbeitungsgerätes sind dann Angaben der Feuchte in Gewichtsprozenten in bestimmten Zeitabständen möglich. Es wurden ein kombinierter Feuchte-Dichte-Meßkopf und ein Datenverarbeitungsgerät aufgebaut und an Modellsubstanzen erprobt. Es konnte gezeigt werden, daß die Meßwerte weitgehend dichteunabhängig sind. Allerdings sind häufige Eichkontrollen notwendig.     

Monotonically Overrelaxed EM Algorithms

We explore the idea of overrelaxation for accelerating the expectation-maximization (EM) algorithm, focusing on preserving its simplicity and monotonic convergence properties. It is shown that in many cases, a trivial modification in the M-step results in an algorithm that maintains monotonic increase in the log-likelihood, but can have an appreciably faster convergence rate, especially when EM is very slow. The method is applicable to more general fixed point algorithms. Its simplicity and effectiveness are illustrated with several statistical problems, including probit regression, least absolute deviations regression, Poisson inverse problems, and finite mixtures. This article has supplemental materials available online.     

Characterizing nonconstant instrumental variance in emerging miniaturized analytical techniques

Measurement variance is a crucial aspect of quantitative chemical analysis. Variance directly affects important analytical figures of merit, including detection limit, quantitation limit, and confidence intervals. Most reported analyses for emerging analytical techniques implicitly assume constant variance (homoskedasticity) by using unweighted regression calibrations. Despite the assumption of constant variance, it is known that most instruments exhibit heteroskedasticity, where variance changes with signal intensity. Ignoring nonconstant variance results in suboptimal calibrations, invalid uncertainty estimates, and incorrect detection limits. Three techniques where homoskedasticity is often assumed were covered in this work to evaluate if heteroskedasticity had a significant quantitative impact—naked-eye, distance-based detection using paper-based analytical devices (PADs), cathodic stripping voltammetry (CSV) with disposable carbon-ink electrode devices, and microchip electrophoresis (MCE) with conductivity detection. Despite these techniques representing a wide range of chemistries and precision, heteroskedastic behavior was confirmed for each. The general variance forms were analyzed, and recommendations for accounting for nonconstant variance discussed. Monte Carlo simulations of instrument responses were performed to quantify the benefits of weighted regression, and the sensitivity to uncertainty in the variance function was tested. Results show that heteroskedasticity should be considered during development of new techniques; even moderate uncertainty (30%) in the variance function still results in weighted regression outperforming unweighted regressions. We recommend utilizing the power model of variance because it is easy to apply, requires little additional experimentation, and produces higher-precision results and more reliable uncertainty estimates than assuming homoskedasticity.     

基于粗糙回归的股票指数变化趋势研究

运用粗糙集结合回归分析预测上证指数变化趋势,首先对预测日前1个月、前1星期、前1天等8个不同时间段进行回归分析,然后根据获得的趋势建立决策表,然后运用粗糙集获取决策规则,最后利用得到的规则预测上证指数的变化趋势.测试结果表明,该方法准确率良好.     

某型飞机燃料油消耗模型的建立及诊断

为确定某型飞机的燃料油消耗量同其它因素之间的相关关系,借助SPSS统计分析软件,运用逐步回归分析方法,对某场站近三年燃料油消耗的实际数据进行研究.首先,运用逐步回归分析的方法,分别挑选出影响飞机在起飞滑跑、空中飞行及降落滑跑等三个阶段燃油消耗量的变量,并建立对应的回归模型.然后,对回归模型作统计诊断,检验模型的有效性及可能存在的异常点.最后,根据回归分析及统计诊断的结果,得出结论.     

不良资产处置方式及影响因素分析

本文使用LossMetrics数据库中处置结束的不良资产,研究不同处置方式下不良资产回收率差异及影响因素。研究发现,不同处置方式之下不良资产回收率存在比较大的差异;不同处置方式的选择主要取决于不良资产的质量特征,但处置方式的选择对不良资产的回收也有显著的影响,资产管理公司在不良资产处置的实践中更多地采用了相对好的处置方式。     

Capability of Urine Analysis for the Disease Blastomycosis Using Infrared Spectroscopy with Eigenvector Signal to Noise Assessment

ABSTRACT

Blastomyces dermatitidis is the etiological agent of the relatively common disease blastomycosis found in certain regions of the United States. In order to discern the presence of blastomycosis, microorganisms present in biofluids of infected individuals must be incubated and cultured and then identified by microscopic observation. This procedure is time consuming due to difficult incubation conditions and necessitates special safeguards. An enzyme-linked immunosorbent assay (ELISA) was developed in an attempt to quicken diagnosis. The immunological assay, however, also requires incubations as well as special reagents. This paper explores the potential of using infrared spectroscopy for determination of blastomycosis in urine samples from infected dogs. The regression methods of principal component regression (PCR) and partial least squares (PLS) are used to develop models. Recently introduced regression diagnostics in conjunction with new diagnostic tools described in this paper are used to determine final models. One of the new tools allows for an estimate of the signal (information) to noise ratio, i.e., reliability, for each eigenvector's contribution to the model. Based on diagnostic information, it is concluded that PCR and PLS are generating the same models.     

职业认同、团队认同对员工建言行为影响的实证研究

基于社会认同理论,试图考察职业认同和团队认同对员工建言行为的影响及其交互作用.研究共收集347份有效问卷,数据处理主要运用了相关性分析、内部一致性信度分析、验证性因子分析以及层次回归分析.结果表明,在控制人口统计学变量和持续承诺后,职业认同和团队认同的提高均能有效促进员工做出建言行为;同时职业认同和团队认同对建言行为存在交互作用:在低职业认同水平下,团队认同与建言行为的正相关关系比高职业认同水平下更强.     

Regression models based on new local strategies for near infrared spectroscopic data

In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.