检索结果-内蒙古大学图书馆

Simultaneous selection of predictors and responses for high dimensional multivariate linear regression

STATISTICS & PROBABILITY LETTERS 2017年 127卷 173-177页

作者： An, Baiguo Zhang, Beibei Capital Univ Econ & Business Sch Stat Beijing 100070 Peoples R China

Most existing variable selection methods for multivariate linear models focus only on predictor selection. In this article, we propose a two-step (double group lasso step and sparse canonical correlation step) method to conduct variable selection for predictors and responses simultaneously. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Canonical correlation Group lasso High dimensional multivariate linear regression Variable selection

来源：评论

学校读者我要写书评

暂无评论

On the oracle property of a generalized adaptive elastic-net for multivariate linear regression with a diverging number of parameters

引用

JOURNAL OF multivariate ANALYSIS 2017年 162卷 16-31页

作者： Xin, Xin Hu, Jianhua Liu, Liangyuan Henan Univ Sch Math & Stat Kaifeng Henan Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Shanghai Univ Finance & Econ Key Lab Math Econ Shanghai Peoples R China

In this paper, we examine the problem of variable selection and coefficient estimation in multivariate linear regression with a diverging number of parameters. We propose a generalized adaptive elastic-net method that integrates elastic-net regularization, adaptively weighted penalty, and covariance of errors. Under some weak regularity conditions, we establish the oracle property of generalized adaptive elastic-net estimators. We also present an algorithm based on the subgradient method to solve the generalized adaptive elastic-net estimation. The numerical computation results show that the proposed method has variable selection accuracy that is comparable to those of the existing methods but that it also has more correct-fitting, less over-fitting, and is closest to the oracle estimator in terms of mean squares error. Moreover, we analyze a chemometrics data set to illustrate our methodology. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Generalized adaptive elastic-net Group variable selection multivariate linear regression Oracle property

来源：评论

学校读者我要写书评

暂无评论

Investigation of Weather Influence in Day-Ahead Hourly Electric Load Power Forecasting with New Architecture Realized in multivariate linear regression & Artificial Neural Network Techniques

Investigation of Weather Influence in Day-Ahead Hourly Elect...

引用

IEEE India International Conference on Power Electronics

作者： M Vetri Selvi Sukumar Mishra Electrical Engineering Department Indian Institute of Technology Delhi India

ISBN: (纸本)9781538649978;9781538649961

In a liberalized deregulated environment, Electric Load Power Forecasting (ELPF) is an important process in any power system network to make it an efficient, secure and customer friendly network. The proposed day-ahead ELPF models are built on a new multiple parallel inputs and output architecture with selected calendar, weather and lagged load power features. In these, the calendar and lagged load power features are kept as constants and weather as a variable feature so that influence of weather on accuracy of day-ahead ELPF can be investigated by different combinational selection of weather variables. The real time data used for this research work were collected from Tata Power Delhi Distribution Limited (TPDDL). The models are developed using both multivariate linear regression (MvLR) and feed forward back propagation artificial neural network (ANN) techniques which are implemented using Matlab programming environment. The performance evaluation criteria for ELPF are presented with determination of various statistical errors. The performance of each model is analyzed and the results are presented. The proposed models yield an average forecast error of 2.9% to 3.5% for day-ahead hourly forecast, throughout the year, irrespective of variations in weather. The proposed day-ahead ELPF models are evident for its simplicity, less measurement requirement and easiness in implementation with higher accuracy.

关键词： Load forecasting Day-ahead forecasting multivariate linear regression regression Artificial intelligence Ann Elpf

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Confidence Tubes in multivariate linear regression

引用

SCANDINAVIAN JOURNAL OF STATISTICS 2016年第3期43卷 879-885页

作者： Liu, Wei Han, Yang Wan, Fang Bretz, Frank Hayter, Anthony J. Univ Southampton S3RI Sch Math Southampton Hants England Univ Exeter Sch Med Exeter EX4 4QJ Devon England Univ Lancaster Dept Math Stat Lancaster LA1 4YW England Novartis Pharma AG Basel Switzerland Shanghai Univ Finance Econ Shanghai Peoples R China Univ Denver Dept Stat Operat Technol Denver CO 80208 USA

Simultaneous confidence bands have been shown in the statistical literature as powerful inferential tools in univariate linear regression. While the methodology of simultaneous confidence bands for univariate linear regression has been extensively researched and well developed, no published work seems available for multivariate linear regression. This paper fills this gap by studying one particular simultaneous confidence band for multivariate linear regression. Because of the shape of the band, the word tube' is more pertinent and so will be used to replace the word band'. It is shown that the construction of the tube is related to the distribution of the largest eigenvalue. A simulation-based method is proposed to compute the 1 - quantile of this eigenvalue. With the computation power of modern computers, the simultaneous confidence tube can be computed fast and accurately. A real-data example is used to illustrate the method, and many potential research problems have been pointed out.

关键词： multivariate linear regression multivariate normal distribution simultaneous confidence band simultaneous confidence tube statistical inference statistical simulation Wishart distribution

来源：评论

学校读者我要写书评

暂无评论

Prediction of gas hydrate saturation throughout the seismic section in Krishna Godavari basin using multivariate linear regression and multi-layer feed forward neural network approach

引用

ARABIAN JOURNAL OF GEOSCIENCES 2016年第5期9卷 1-10页

作者： Singh, Yudhvir Nair, Rajesh R. Singh, Harmandeep Datta, Prattya Jaiswal, Priyank Dewangan, Pawan Ramaprasad, T. IIT Madras Dept Ocean Engn Chennai 600036 Tamil Nadu India IIT Kharagpur Dept Geol & Geophys Kharagpur W Bengal India Oklahoma State Univ Noble Res Ctr Boone Pickens Sch Geol Stillwater OK 74078 USA Natl Inst Oceanog Panaji 403004 Goa India

Stepwise linear regression, multi-layer feed forward neural (MLFN) network method was used to predict the 2D distribution of P-wave velocity, resistivity, porosity, and gas hydrate saturation throughout seismic section NGHP-01 in the Krishna-Godavari basin. Log prediction process, with uncertainties based on root mean square error properties, was implemented by way of a multi-layer feed forward neural network. The log properties were merged with seismic data by applying a non-linear transform to the seismic attributes. Gas hydrate saturation estimates show an average saturation of 41 % between common depth point (CDP) 600 and 700 and an average saturation of 35 % for CDP 300-400 and 700-800, respectively. High gas hydrate saturation corresponds to high P-wave velocity and high resistivity except in a few spots, which could be due to local variation of permeability, temperature, fractures, etc.

关键词： Gas hydrate saturation multivariate linear regression Multi-layer feed forward neural network

来源：评论

学校读者我要写书评

暂无评论

PREDICTING LOWER STRATOSPHERIC WATER VAPOR FROMCHEMISTRY-CLIMATE MODELS USING A multivariate linear regression

PREDICTING LOWER STRATOSPHERIC WATER VAPOR FROMCHEMISTRY-CLI...

引用

作者： KEVIN SMALLEY Texas A&M University

学位级别：硕士

Climate models predict that tropical lower stratospheric humidity will increase as the climate warms, with important implications for the chemistry and climate of the at- mosphere. We analyze this trend in 21st-century simulations from 12 state-of-the-art chemistry-climate models (CCMs) using a linear regression model to determine the fac- tors driving the trends. The trend in humidity in the CCMs is driven by warming of the troposphere. This is partially offset in most CCMs by an increase in the strength of the Brewer-Dobson circulation, which tends to cool the tropopause layer. We also apply the regression model to individual decades from the 21st century CCM runs and compared them to the results from a regression of a decade of lower stratospheric humidity observa- tions. Many of the CCMs, but not all, compare well with observations, lending credibility to their predictions. One notable deficiency in most CCMs is that they underestimate the impact of the quasi-biennial oscillation on lower stratospheric humidity. Our analysis pro- vides a new way to evaluate model trends in lower stratospheric humidity.

关键词： Chemistry-Climate Models Climate Change Lower Stratosphere Water Vapor multivariate linear regression Thesis

来源：评论

学校读者我要写书评

暂无评论

Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 143卷 122-129页

作者： Andrada, Matias F. Vega-Hissi, Esteban G. Estrada, Mario R. Garro Martinez, Juan C. Univ Nacl San Luis Fac Quim Bioquim & Farm Area Quim Fis RA-5700 San Luis Argentina Consejo Nacl Invest Cient & Tecn Ctr Cient Tecnol San Luis RA-5700 San Luis Argentina

In this work, we performed a quantitative structure activity relationship (QSAR) model for a family of 5-lipoxygenase (5-LOX) inhibitors using k-means clustering and linear discriminant analysis (LDA) for the selection of training and test sets and multivariate linear regression (MLR) for the independent variable selection. With the k-means clustering method, the total set of compounds (58 derivatives of 5-Benzylidene-2-phenylthiazolinones) was divided in two clusters according to a simple discriminant function. We found that pilD (conventional bond order ID number) molecular descriptor discriminates correctly 100% of the compounds of each clusters. Thirty different models divided in three series were analyzed and the series with representative training and test sets (series 3) had the most predictive models. The statistical parameters of the best model are R-train = 0.811 and R-test = 0.801. We found that a rational selection in the setting-up of training and test sets allows to obtain the most predictive models and the random selection is sometimes unsuitable, especially, when the total set of compounds can be classified in different clusters according to structural features. (C) 2015 Elsevier B.V. All rights reserved.

关键词： QSAR 5-Lipoxygenase inhibitors k-Means clustering linear discriminant analysis multivariate linear regression

来源：评论

学校读者我要写书评

暂无评论

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy

引用

JOURNAL OF CLINICAL NEUROSCIENCE 2015年第9期22卷 1444-1449页

作者： Hoffman, Haydn Lee, Sunghoon I. Garst, Jordan H. Lu, Derek S. Li, Charles H. Nagasawa, Daniel T. Ghalehsari, Nima Jahanforouz, Nima Razaghy, Mehrdad Espinal, Marie Ghavamrezaii, Amir Paak, Brian H. Wu, Irene Sarrafzadeh, Majid Lu, Daniel C. Univ Calif Los Angeles Dept Neurosurg Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Anesthesiol Los Angeles CA 90095 USA Univ Calif Los Angeles Wireless Hlth Inst Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Orthoped Surg Los Angeles CA 90095 USA Univ Calif Los Angeles David Geffen Sch Med Neuromotor Recovery & Rehabil Ctr Los Angeles CA 90095 USA Univ Calif Los Angeles Brain Res Inst Los Angeles CA 90095 USA

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R-2) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R-2 = 0.452;MAD = 0.0887;p = 1.17 x 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R-2 = 0.932;MAD = 0.0283;p = 5.73 x 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Cervical spondylotic myelopathy multivariate linear regression Support vector regression Surgical outcomes

来源：评论

学校读者我要写书评

暂无评论

Intensity Normalization of ¹²³ I-ioflupane-SPECT Brain Images Using a Model-Based multivariate linear regression Approach

Intensity Normalization of <SUP>123</SUP> I-ioflupane-SPECT ...

引用

5th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Brahim, A. Manuel Gorriz, J. Ramirez, Javier Khedher, L. Univ Granada Dept Signal Theory Networking & Commun Granada Spain

ISBN: (纸本)9783319189147;9783319189130

The intensity normalization step is essential, as it corresponds to the initial step in any subsequent computer-based analysis. In this work, a proposed intensity normalization approach based on a predictive modeling using multivariate linear regression (MLR) is presented. Different intensity normalization parameters derived from this model will be used in a linear procedure to perform the intensity normalization of (123) I-ioflupane-SPECT brain images. This proposed approach is compared to conventional intensity normalization methods, such as specific-to-non-specific binding ratio, integral-based intensity normalization and intensity normalization by minimizing the Kullback-Leibler divergence. For the performance evaluation, a statistical analysis is used by applying the Euclidean distance and the Jeffreys divergence. In addition, a classification task using support vector machine to evaluate the impact of the proposed methodology for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome detection.

关键词： Intensity normalization DaTSCAN SPECT images multivariate linear regression Parkinsonian syndrome Computer-aided diagnosis system

来源：评论

学校读者我要写书评

暂无评论

Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis

引用

JOURNAL OF multivariate ANALYSIS 2014年第0期132卷 138-150页

作者： Katayama, Shota Imori, Shinpei Tokyo Inst Technol Grad Sch Decis Sci & Technol Meguro Ku Tokyo 1528550 Japan Osaka Univ Grad Sch Engn Sci Toyonaka Osaka 5608531 Japan

This paper proposes two model selection criteria for identifying relevant predictors in the high-dimensional multivariate linear regression analysis. The proposed criteria are based on a Lasso type penalized likelihood function to allow the high-dimensionality. Under the asymptotic framework that the dimension of multiple responses goes to infinity while the maximum size of candidate models has smaller order of the sample size, it is shown that the proposed criteria have the model selection consistency, that is, they can asymptotically pick out the true model. Simulation studies show that the proposed criteria outperform existing criteria when the dimension of multiple responses is large. (C) 2014 Elsevier Inc. All rights reserved.

关键词： multivariate linear regression Model selection High-dimensional data Consistency

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：