Identifying Unusual Observations in Ridge Regression Linear Model Using Box-Cox Power Transformation Technique

The use of [1] Box-Cox power transformation in regression analysis is now common;in the last two decades there has been emphasis on diagnostics methods for Box-Cox power transformation, much of which has involved deletion of influential data cases. The pioneer work of [2] studied local influence on constant variance perturbation in the Box-Cox unbiased regression linear mode. Tsai and Wu [3] analyzed local influence method of [2] to assess the effect of the case-weights perturbation on the transformation-power estimator in the Box-Cox unbiased regression linear model. Many authors noted that the influential observations on the biased estimators are different from the unbiased estimators. In this paper I describe a diagnostic method for assessing the local influence on the constant variance perturbation on the transformation in the Box-Cox biased ridge regression linear model. Two real macroeconomic data sets are used to illustrate the methodologies.

A Geometric View on Inner Transformation between the Variables of a Linear Regression Model

In the teaching and researching of linear regression analysis, it is interesting and enlightening to explore how the dependent variable vector can be inner-transformed into regression coefficient estimator vector from a visible geometrical view. As an example, the roadmap of such inner transformation is presented based on a simple multiple linear regression model in this work. By applying the matrix algorithms like singular value decomposition (SVD) and Moore-Penrose generalized matrix inverse, the dependent variable vector lands into the right space of the independent variable matrix and is metamorphosed into regression coefficient estimator vector through the three-step of inner transformation. This work explores the geometrical relationship between the dependent variable vector and regression coefficient estimator vector as well as presents a new approach for vector rotating.

Support Vector Regression for Bus Travel Time Prediction Using Wavelet Transform

In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to extract important information of data at different levels and enhances the forecasting ability of the model. After wavelet transform different components are forecasted by their corresponding SVR predictors. The final prediction result is obtained by the summation of the predicted results for each component. The proposed hybrid model is examined by the data of bus route No.550 in Nanjing, China. The performance of WT-SVR model is evaluated by mean absolute error(MAE), mean absolute percent error(MAPE) and relative mean square error(RMSE), and also compared to regular SVR and ANN models. The results show that the prediction method based on wavelet transform and SVR has better tracking ability and dynamic behavior than regular SVR and ANN models. The forecasting performance is remarkably improved to obtain within 6% MAPE for testing section Ⅰ and 8% MAPE for testing section Ⅱ, which proves that the suggested approach is feasible and applicable in bus travel time prediction.

Combined Method of Datum Transformation Between Different Coordinate Systems

The similarity transformation model between different coordinate systems is not accurate enough to describe the discrepancy of them.Therefore,the coordinate transformation from the coordinate frame with poor accuracy to that with high accuracy cannot guarantee a high precision of transformation.In this paper,a combined method of similarity transformation and regressive approximating is presented.The local error accumulation and distortion are taken into consideration and the precision of coordinate system is improved by using the recommended

Spatial Interpolation of Soil Texture Using Compositional Kriging and Regression Kriging with Consideration of the Characteristics of Compositional Data and Environment Variables

The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they （especially the categorical variables） are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging （CK） approaches. The root mean squared error （RMSE）, the relative improvement value of RMSE and Aitchison＇s distance （DA） were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.

Industrial transformation and green production to reduce environmental emissions: Taking cement industry as a case

Industrial transformation and green production(ITGP) is a new 10-year international research initiative proposed by the Chinese National Committee for Future Earth. It is also an important theme for adapting and responding to global environmental change. Aiming at a thorough examination of the implementation of ITGP in China, this paper presents its objectives, its three major areas, and their progress so far. It also identifies the key elements of its management and proposes new perspectives on managing green transformation. For instance, we introduce a case study on cement industry that shows the positive policy effects of reducing backward production capacity on PCDD/Fs emissions. Finally,to develop different transformation scenarios for a green future, we propose four strategies: 1) policy integration for promoting green industry, 2)system innovation and a multidisciplinary approach, 3) collaborative governance with all potential stakeholders, and 4) managing uncertainty,risks, and long-time horizons.

Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.

A Class of Estimators for Population Ratio in Simple Random Sampling Using Variable Transformation

This paper is an extension and generalization of the study carried out by [1] on the estimation of the population ratio (R) of the population means of two variables (y and x) under Simple Random Sampling (SRS) scheme, using a variable transformation of the auxiliary variable, x. All the six estimators proposed by [1] are easily identified as special cases of the proposed class of estimators. Asymptotic properties of the proposed class of estimators are derived theoretically and subsequently verified using empirical illustrations. Some of the proposed estimators are found to have relatively large gains in efficiency over the customary ratio estimator, ?for the given data set.

Analysis of the Invariance and Generalizability of Multiple Linear Regression Model Results Obtained from Maslach Burnout Scale through Jackknife Method

The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach Burnout Scale with Jackknife Method in terms of validity and generalizability. To do this, a questionnaire was given to 11 research assistants working at Ondokuz Mayis University and the burnout scores of this questionnaire were taken as the dependent variable of the multiple linear regression model. The variable of burnout was explained with the variables of age, weekly hours of classes taught, monthly average credit card debt, numbers of published articles and reports, gender, marital status, number of children and the departments of the research assistants. Dummy variables were assigned to the variables of gender, marital status, number of children and the departments of the research assistants and thus, they were made quantitative. The significance of the model as a result of multiple linear regressions was examined through backward elimination method. After this, for the five explanatory variables which influenced the variable of burnout, standardized model coefficients and coefficients of determination, and 95% confidence intervals of these values were estimated through Jackknife Method and the generalizability of the parameter estimation results of these variables on population was researched.

Evaluation of Various Linear Regression Methods for Downscaling of Mean Monthly Precipitation in Arid Pichola Watershed

In this paper, downscaling models are developed using various linear regression approaches namely direct, forward, backward and stepwise regression for downscaling of GCM output to predict mean monthly precipitation under IPCC SRES scenarios to watershed-basin scale in an arid region in India. The effectiveness of these regression approaches is evaluated through application to downscale the predictand for the Pichola lake region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. The selection of important predictor variables becomes a crucial issue for developing downscaling models since reanalysis data are based on wide range of meteorological measurements and observations. Direct regression was found to yield better performance among all other regression techniques explored in the present study. The results of downscaling models using both approaches show that precipitation is likely to increase in future for A1B, A2 and B1 scenarios, whereas no trend is discerned with the COMMIT.

Study on risk factors of hemorrhagic transformation in patients with acute cerebral infarction after non thrombolysis

Objective:To study the risk factors of hemorrhagic transformation in patients with acute cerebral infarction and to analyze the risk factors.Methods: A total of 96 patients with acute cerebral infarction after the thrombolysis in our hospital from June 2016 to December 2017 were selected as the research object. And they were divided into bleeding group 48 cases and hemorrhage transformation group 48 cases according to whether with hemorrhage occurs transformation. Then the lipid metabolism, atrial fibrillation, history of smoking and drinking, history of hypertension and diabetes, blood pressure, treatment time after onset and infarction area of two groups were compared, and the relationship between those factors and the disease were analyzed by the multi-factor Logistic regression analysis.Results: The atrial fibrillation, history of smoking and drinking of two groups had significant differences;The hospital fasting plasma glucose and LDL-C level of two groups had significant differences;the treatment time after onset and infarction area of two groups had significant differences;The multi-factor Logistic regression analysis showed that atrial fibrillation, blood glucose on admission, LDL-C and large area of infarction are the factors affecting the risk of bleeding in patients with acute cerebral infarction transformation.Conclusion:Atrial fibrillation, blood glucose on admission, LDL-C, treatment time after onset and large area of infarction belongs to the patients with acute cerebral infarction after the thrombolysis transformation of bleeding risk factors.

基于Transformer的面部动画生成

在面部动画生成领域,克服人脸几何形状的复杂性是一项极具挑战性的任务。为了更好地应对这一挑战,文章采用了一种创新的方法,即将经过一维卷积堆叠和自注意力提取后的音频特征作为输入,通过Transformer模型从音频信号中生成面部动画。这个过程采用时间自回归模型逐步合成面部运动。使用BIWI数据集开展实验证明,该方法成功地将唇部顶点误差率缩小至令人满意的6.123%,同步率超过MeshTalk79.64%,这意味该方法在口型同步和面部表情生成方面表现出色,在完成面部动画生成任务中表现出很高的潜力,可为未来相关研究提供方向和参考。

Estimation of Population Ratio in Post-Stratified Sampling Using Variable Transformation

Extending the work carried out by [1], this paper proposes six combined-type estimators of population ratio of two variables in post-stratified sampling scheme, using variable transformation. Properties of the proposed estimators were obtained up to first order approximations,(on–1), both for achieved sample configurations (conditional argument) and over repeated samples of fixed size n (unconditional argument). Efficiency conditions were obtained. Under these conditions the proposed combined-type estimators would perform better than the associated customary combined-type estimator. Furthermore, optimum estimators among the proposed combined-type estimators were obtained both under the conditional and unconditional arguments. An empirical work confirmed the theoretical results.

Separate-Type Estimators for Estimating Population Ratio in Post-Stratified Sampling Using Variable Transformation

The study proposes, along the line of [1], six separate-type estimators for estimating the population ratio of two variables in post-stratified sampling, using variable transformation. Properties of the proposed estimators were obtained up to first order approximations, both for achieved sample configurations (conditional argument) and over repeated samples of fixed size n (unconditional argument). Efficiency conditions, under which the proposed separate-type estimators would perform better than the associated customary separate-type estimators in terms of having smaller mean squared errors, were obtained. Furthermore, conditions under which some of the proposed separate-type estimators would perform better than other proposed separate-type estimators were also obtained. The optimum estimators among the proposed separate-type estimators were obtained and an empirical illustration confirmed the theoretical results.

数字化转型对审计费用的影响研究——基于企业商誉的中介效应

依据2010—2022年中国A股创业板上市公司数据,运用多元线性回归模型探究数字化转型对审计费用的影响。结果显示,企业数字化转型进程与审计费用的增加呈正相关关系,这一关系在重污染行业尤为显著。进一步分析表明,商誉在企业数字化转型对审计费用的提升过程中具有中介效应。鉴于此,企业应加强精细化审计成本预见性与规划性,全面考虑数字化投资与审计成本之间的平衡,适时调整审计策略与资源配置,监管层需适时调整审计行业规则应对数字化转型。

城市交通网络对工业用地隐性转型的影响机理研究——以武汉市为例

研究目的:依据城市工业用地转型的隐性内涵及其驱动理论,探究城市交通网络对工业用地隐性转型的影响机理,以助推城市交通结构优化和工业新型化发展。研究方法:工业用地隐性形态评价模型、核密度估计、地理加权回归模型和对数衰减函数。研究结果:(1)武汉市主城区工业用地隐性转型发展非均衡性较强;(2)武汉市主城区高速路、主干道、次干道、支路对工业用地隐性转型驱动作用显著并存在明显空间异质性;(3)不同等级道路对工业用地隐性转型廊道效应的吸引力差异表现为主干道最高,次干道次之,而铁路、高速公路、支路对其具有排斥作用,工业用地隐性转型受主干道、次干道影响的效应偏好显著。研究结论:为纾困城市工业用地内部结构优化与转型升级,应重点实施内涵建设和差异化空间管控,协同路网结构优化和工业用地隐性转型,以引导经济要素流动为目标优化城市交通网络,创新城市工业用地管控方式。

数字化转型对企业新质生产力的影响

本文以2011~2022年上市公司为研究样本,基于数字化转型赋能新质生产力提升的深层逻辑,考察了数字化转型对企业新质生产力的影响、作用机制及异质性特征。研究发现:(1)数字化转型对企业新质生产力的发展具有显著的正向影响,数字化转型带来的技术创新与管理创新满足了新质生产力的催生条件;(2)不同特征的企业数字化转型对新质生产力的发展存在异质性,相对于其他类型企业,数字化转型更加有利于提升技术密集型企业、东部企业、国有企业的新质生产力,这有助于企业深刻理解新质生产力的特征并助力企业提升新质生产力;(3)机制检验结果显示,数字化转型主要通过技术创新与管理创新两个渠道促进企业新质生产力的发展,技术创新是新质生产力发展的底层支撑,而管理创新为技术创新提供了制度保障。

尾矿坝位移分级预警阈值研究及规律分析

针对尾矿坝在线监测重建设、轻利用的现状,基于尾矿坝位移在线监测时间序列,通过多步逆向云变换算法(Multi-step Backward Cloud Transformation Algorithm Based on Sampling with Replacement,MBCT-SR)改进云模型,根据“3E_(n)原则”和内外包络曲线确定在线监测位移的正常运行值,从而建立尾矿坝位移分级预警阈值模型,并利用某尾矿坝全球导航卫星(Global Navigation Satellite System,GNSS)技术表面位移在线监测数据进行实例验证。结果表明:该尾矿坝水平方向位移的黄、橙、红预警阈值分别为8.41 mm/d、12.94 mm/d、19.41 mm/d,呈现出坝体中间预警阈值最大、并由中间向两侧减小的空间变化规律;尾矿坝垂直方向位移的黄、橙、红预警阈值分别为16.56 mm/d、25.48 mm/d、38.22 mm/d,且随着子坝的堆积,预警阈值逐渐增大。