《计量经济学》课程中若干问题明晰

《计量经济学》课程是我国高等学校经济学类各专业八门共同核心课程之一,《计量经济学》方法的应用也向纵深发展。就该门课程中有关假设检验、一元回归与多元回归的区别以及利用时间序列数据建立回归模型应该注意的问题等进行梳理．以期为在这些问题上比较模糊的学习者提供一个理解的思路。     

以“回归工程”为导向的机械类专业计算方法课程教学实践

针对《计算方法》课程教学特点,建立以工程问题为导向的教学项目案例库,完成以项目驱动为导向的教学模式设计,构建以“回归工程”为导向的课程考核评价体系,实现工程教育“回归工程”的教学本质。     

蒙特卡罗模拟在《计量经济学》教学中的应用

在大学本科《计量经济学》教学过程中,运用蒙特卡罗模拟实验可以帮助学生轻易获得对相关知识的直接体验和理解,而不需要高级的数学知识。以存在违背经典假定的序列相关情形时为例,运用Stata软件进行编程,说明蒙特卡罗模拟实验在《计量经济学》教学中的应用。     

《计算机网络基础》课程改革的研究与探讨

根据《计算机网络基础》课程的特点,分析了当前教学中的存在的问题,结合教学实践对《计算机网络基础》课程的设计理念、教学手段及考核方式等方面的问题进行了研究和探讨,从而更好地提高《计算机网络基础》课程的教学质量。     

《计算机网络基础》课程改革的研究与探讨

根据《计算机网络基础》课程的特点,分析了当前教学中的存在的问题,结合教学实践对《计算机网络基础》课程的设计理念、教学手段及考核方式等方面的问题进行了研究和探讨,从而更好地提高《计算机网络基础》课程的教学质量。     

高职《高频电子技术》课程教学模式探讨

分析了高职《高频电子技术》课程中出现的各种问题,在此基础上探讨了具有职业教学特点的《高频电子技术》课程的建设,使该课程教学质量有所提高,并为职业教育课程改革提供参考。     

应用型《计算机网络》课程实验教学改革研究

实验教学是《计算机网络》课程改革的重要组成部分,针对目前《计算机网络》实验教学中存在的问题,结合《计算机网络》课程的特点,针对应用型网络人才的培养目标,探讨《计算机网络》实验教学的改进思路。     

《网络营销》课程的实战型教学尝试

作为现代营销重要课程,《网络营销》同时是电子商务专业专业基础课程之一。《网络营销》课程具有较强的实践性,如何加强实战型教学、指导学生学习会运用理论知识操作和实践网络营销,是当前《网络营销》教学必须面对的问题。本文主要从实战型教学模式相关概念入手,对《网络营销》课程的实战型教学尝试进行探讨。     

《操作系统》课程教学改革的研究与探讨

《操作系统》是一门涉及面很广的计算机专业基础课程,在计算机专业教学中占据十分重要的地位。本文分析了《操作系统》课程的特点,并对当前《操作系统》课程教学存在的问题进行了讨论,提出了若干关于操作系统课程教学方法和教学手段的改革措施,以适应教学改革的需要。     

《操作系统》课程教学改革初探

分析目前高等学校《操作系统》课程教学现状及存在的主要问题,并针对这些问题在教学方法、教学手段、实验教学、考核方式等方面提出相应的改革方法并应用于实践,拟探索出一条更加科学、合理、适用于当前《操作系统》课程教学的教学理念,以期对《操作系统》课程教学改革有所启发。     

支持向量机分类回归核函数

基于统计学习理论的支持向量机算法以其优秀的学习性能已广泛用于解决分类与回归问题。分类算法通过求两类样本之间的最大间隔来获得最优分离超平面,其几何意义相当直观,而回归算法的几何意义就不那么直观了。另外,有些适用于分类问题的快速优化算法岁不能用于回归算法中。研究了分类与回归算法之间的关系,为快速分类算法应用于回归模型提供了一定的理论依据。     

A graph approach to generate all possible regression submodels

A regression graph to enumerate and evaluate all possible subset regression models is introduced. The graph is a generalization of a regression tree. All the spanning trees of the graph are minimum spanning trees and provide an optimal computational procedure for generating all possible submodels. Each minimum spanning tree has a different structure and characteristics. An adaptation of a branch-and-bound algorithm which computes the best-subset models using the regression graph framework is proposed. Experimental results and comparison with an existing method based on a regression tree are presented and discussed.     

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models – Multiple regression, Random forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply random forest or quantile regression techniques to the machining domain. The performance of these models was compared to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).     

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models.     

A weighted combination of stacking and dynamic integration

In this paper we present a novel method that fuses the ensemble meta-techniques of stacking and dynamic integration for regression problems. We detail an introductory experimental analysis of the technique referred to as wMetaComb and compare its performance to single model linear regression, stacking and the dynamic integration technique of dynamic weighting with selection, where in the case of the ensembles the base models were also created using linear regression. The evaluation showed that wMetaComb returned the strongest performance.     

An improved strategy for regression of biophysical variables and Landsat ETM+ data

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent woody canopy cover, and leaf area index (LAI). Traditional methods of regression are not sufficient when resulting biophysical surfaces derived from remote sensing are subsequently used to drive ecosystem process models. Most regression analyses in remote sensing rely on a single spectral vegetation index (SVI) based on red and near-infrared reflectance from a single date of imagery. There are compelling reasons for utilizing greater spectral dimensionality, and for including SVIs from multiple dates in a regression analysis. Moreover, when including multiple SVIs and/or dates, it is useful to integrate these into a single index for regression modeling. Selection of an appropriate regression model, use of multiple SVIs from multiple dates of imagery as predictor variables, and employment of canonical correlation analysis (CCA) to integrate these multiple indices into a single index represent a significant strategic improvement over existing uses of regression analysis in remote sensing.To demonstrate this improved strategy, we compared three different types of regression models to predict LAI for an agro-ecosystem and live tree canopy cover for a needleleaf evergreen boreal forest: traditional (Y on X) ordinary least squares (OLS) regression, inverse (X on Y) OLS regression, and an orthogonal regression method called reduced major axis (RMA). Each model incorporated multiple SVIs from multiple dates and CCA was used to integrate these. For a given dataset, the three regression-modeling approaches produced identical coefficients of determination and intercepts, but different slopes, giving rise to divergent predictive characteristics. The traditional approach yielded the lowest root mean square error (RMSE), but the variance in the predictions was lower than the variance in the observed dataset. The inverse method had the highest RMSE and the variance was inflated relative to the variance of the observed dataset. RMA provided an intermediate set of predictions in terms of the RMSE, and the variance in the observations was preserved in the predictions. These results are predictable from regression theory, but that theory has been essentially ignored within the discipline of remote sensing.     

Linear regression models of floor surface parameters on friction between Neolite and quarry tiles

For slips and falls, friction is widely used as an indicator of surface slipperiness. Surface parameters, including surface roughness and waviness, were shown to influence friction by correlating individual surface parameters with the measured friction. A collective input from multiple surface parameters as a predictor of friction, however, could provide a broader perspective on the contributions from all the surface parameters evaluated. The objective of this study was to develop regression models between the surface parameters and measured friction. The dynamic friction was measured using three different mixtures of glycerol and water as contaminants. Various surface roughness and waviness parameters were measured using three different cut-off lengths. The regression models indicate that the selected surface parameters can predict the measured friction coefficient reliably in most of the glycerol concentrations and cut-off lengths evaluated. The results of the regression models were, in general, consistent with those obtained from the correlation between individual surface parameters and the measured friction in eight out of nine conditions evaluated in this experiment. A hierarchical regression model was further developed to evaluate the cumulative contributions of the surface parameters in the final iteration by adding these parameters to the regression model one at a time from the easiest to measure to the most difficult to measure and evaluating their impacts on the adjusted R² values. For practical purposes, the surface parameter R_a alone would account for the majority of the measured friction even if it did not reach a statistically significant level in some of the regression models.     

Comparative study of ROC regression techniques—Applications for the computer-aided diagnostic system in breast cancer detection

The receiver operating characteristic (ROC) curve is the most widely used measure for statistically evaluating the discriminatory capacity of continuous biomarkers. It is well known that, in certain circumstances, the markers’ discriminatory capacity can be affected by factors, and several ROC regression methodologies have been proposed to incorporate covariates in the ROC framework. An in-depth simulation study of different ROC regression models and their application in the emerging field of automatic detection of tumour masses is presented. In the simulation study different scenarios were considered and the models were compared to each other on the basis of the mean squared error criterion. The application of the reviewed ROC regression techniques in evaluating computer-aided diagnostic (CAD) schemes can become a major factor in the development of such systems in Radiology.     

A semi-supervised regression model for mixed numerical and categorical variables

In this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-mode clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-mode parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least-square errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method.