Assessing continuous bivariate effects among different groups through nonparametric regression models: An application to breast cancer detection

In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer.     

Self-controlled case series analyses: Small-sample performance

Second-order expressions for the asymptotic bias and variance of the log relative incidence estimator are derived for the self-controlled case series model in a simplified scenario. The dependence of the bias and variance on factors such as the relative incidence and ratio of risk to observation period are studied. Small-sample performance of the estimator in realistic scenarios is investigated using simulations. It is found that, in scenarios likely to arise in practice, asymptotic methods are valid for numbers of cases in excess of 20-50 depending on the ratio of the risk period to the observation period and on the relative incidence. The application of Monte Carlo methods to self-controlled case series analyses is also discussed.     

Influence of Two Salicylate Components on the Particle Size of an Oil-in-Water Emulsion with Nonionic Surfactants

This paper reports a study undertaken using techniques of static and dynamic light scattering to investigate the influence of sodium salicylate and methyl salicylate on droplet size of oil-in-water emulsions. The rates of changes were measured by determining the size and distribution of the oil droplet in the material. All emulsions showed a bimodal size distribution; the mean diameters and polydispersity were calculated from intensity. These data were analyzed with nonlinear regressions and bootstrap methodology. An amount of methyl salicylate component induced a decrease of mean diameter and standard deviation. On the contrary, sodium salicylate entailed the growth of all droplet populations and coalescence for the highest concentration.     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.     

Gas-liquid mass transfer in a circulating jet-loop nitrifying MBR

Based on airlift configuration, a novel circulating jet-loop submerged membrane bioreactor (JLMBR) adapted to ammonium partial oxidation has been developed. Membrane technology and combined air and water forced circulation are adopted to obtain a high biomass retention time and to achieve a separate control of mixing and aeration. This study is intended to determine how gas-liquid mass transfer is affected by operating conditions. In a first approximation, liquid was assumed to be perfectly mixed. A classical non-steady state clean water test, known as the “gas out-gas in” method, was used to determine the gas-liquid mass transfer coefficient k_La. Air and recirculated liquid superficial velocities were gradually increased from 0.013 to and 0.0056 to , respectively. Subsequently, the gas-liquid mass transfer coefficient k_La varied from 0.01 to . It appears to be influenced by the combined action of air and recirculated liquid flowrates in the range and , respectively, for air and liquid. Correlations are proposed to describe this double influence. Experiments were performed on tap water and a culture medium used for the autotrophic growth of nitrifying bacteria, respectively. Oxygen transfer appeared to be not significantly affected by the mineral salt encountered in this medium.     

BF粒子滤波算法在快速目标跟踪中应用的研究

为了对快速目标进行跟踪,在高斯加性白噪声的条件下(AGWN),文章把BF粒子滤波算法和迭代的Kalman滤波器方法结合起来对快速目标状态进行处理,状态估计用BF粒子滤波算法,能减小方差和运算量,同时用Kalman滤波器能提高跟踪精度和对目标状态的估计。仿真结果表明,文中所设计的快速和精确的BF算法能真正地解决快速目标跟踪问题。     

GEE type inference for clustered zero-inflated negative binomial regression with application to dental caries

Use of zero-inflated count data models is common in applications where the number of zero counts exceeds that predicted from a traditional count data model such as Poisson or negative binomial. When count data exhibiting inflated zero counts are correlated among subjects, a natural approach will be to fit a marginal model with the help of generalized estimating equations (GEE) that can incorporate subject-to-subject correlations. A GEE based zero-inflated negative binomial (ZINB) model is proposed to fit clustered counts with excessive zeros. However, the corresponding sandwich variance estimator appears to underestimate the true variance. The theoretical reasons for its failure are explained and a correction under additional modeling assumptions is offered. In addition, a clustered resampling (bootstrap) procedure is proposed to estimate the variance and it is shown that the bootstrap procedure captures the correct variance under no additional model assumptions. Utility of this marginal GEE based ZINB model over two other competing models has been assessed using a thorough simulation study. The resulting inference procedure is applied to study the association between the dental caries and fluoride exposures using a dataset extracted from the Iowa Fluoride Study. A number of risk factors of clinical significance are reliably identified using the proposed model.     

Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems

If the production process, production equipment, or material changes, it becomes necessary to execute pilot runs before mass production in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Although, artificial neural networks are widely utilized to extract management knowledge from acquired data, sufficient training data is the fundamental assumption. Unfortunately, this is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize bootstrap to generate virtual samples to fill the information gaps of sparse data. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.     

关系数据库近似匹配查询方法研究

针对传统海量数据精确查询负载过大的问题,引入基于仿真的置信区间自动抽样方法(Bootstrap)对数据库提供支持。通过对部分或采样数据进行查询,将查询简化到基础数据上,在对整个数据集查询一次的时间内,完成对多个样本重复多次的查询,得到数据库查询的置信区间;再进行基础SQL查询,得到符合用户要求的近似结果。实验结果表明,引入Bootstrap方法进行数据查询是有效的。     

Corrective classification: Learning from data imperfections with aggressive and diverse classifier ensembling

Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer.