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regression-Based Automated Facial Image Quality model

INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2017年第4期11卷 22-40页

作者： Zohra, Fatema Tuz Gavrilov, Andrei D. Zatarain, Omar A. Gavrilova, Marina L. Univ Calgary Comp Sci Calgary AB Canada Univ British Columbia Integrated Engn Calgary AB Canada Univ Calgary Schulich Sch Engn Calgary AB Canada Univ Calgary Dept Comp Sci Calgary AB Canada

Nowadays, biometric technologies became reliable and widespread means of unobtrusive user authentication in a variety of real-world applications. The performance of an automated face recognition system has a strong relationship with the quality of the biometric samples. The facial samples can be affected by various quality factors, such as uneven illumination, low or high contrast, excessive brightness, blurriness, etc. In this article, the authors propose a quality estimation method based on linear regression analysis to characterize the relationship between different quality factors and the performance of a face recognition system. The regression model can predict the overall quality of a facial sample which reflects the effects of various quality factors on that sample. The weights assigned to the different quality factors by the linear regression model reflect the impact of those quality factors on the performance of the recognition system. Therefore, the prediction scores generated from the model is a strong indicator of the overall quality of the facial images. The authors evaluated the quality estimation model on the Extended Yale Database B. They also performed a study to understand which quality factors affect the face recognition the most.

关键词： Facial Image Quality Intelligent Adaptive System linear regression model Quality Index Recognition Accuracy

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The importance of statistical modelling in clinical research

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NEUROPSYCHIATRIE 2016年第2期30卷 92-102页

作者： Alexandrowicz, Rainer W. Jahn, Rebecca Friedrich, Fabian Unger, Anne Alps Adria Univ Klagenfurt Dept Appl Psychol & Methods Res Inst Psychol Univ Str 65-67 A-9020 Klagenfurt Austria Med Univ Vienna Dept Psychiat & Psychotherapy Clin Div Social Psychiat Vienna Austria

Various studies have shown that caregiving relatives of schizophrenic patients are at risk of suffering from depression. These studies differ with respect to the applied statistical methods, which could influence the findings. Therefore, the present study analyzes to which extent different methods may cause differing results. The present study contrasts by means of one data set the results of three different modelling approaches, Rasch modelling (RM), Structural Equation modelling (SEM), and linear regression modelling (LRM). The results of the three models varied considerably, reflecting the different assumptions of the respective models. Latent trait models (i. e., RM and SEM) generally provide more convincing results by correcting for measurement error and the RM specifically proves superior for it treats ordered categorical data most adequately.

关键词： Rasch model Structural Equation model linear regression model Latent regression model comparison

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DETERMINANTS OF FX-RISK MANAGEMENT EVIDENCE OF HUNGARY 31

DETERMINANTS OF FX-RISK MANAGEMENT EVIDENCE OF HUNGARY

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31st European Conference on modelling and Simulation, ECMS 2017

作者： Domotor, Barbara Kovacs, Erzsebet Corvinus Univ Budapest Dept Operat Res & Actuarial Sci Dept Finance H-1093 Budapest Hungary

ISBN: (纸本)9780993244049

This paper investigates the motives of FX-risk management based on the changes of forward open positions of Hungarian corporations. We have found that Hungarian companies are significantly more exposed in short EUR forward position, than in EUR long one. Our linear regression model also showed that changing market conditions have an essentially higher impact on the EUR short positions. Our results confirmed that expectations are determining in the risk hedging decisions proving that financial risk management also has a speculative motive.

关键词： Corporate hedging Forward hedge linear regression model

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Construction of a traffic estimation model using dynamic response features extracted from SHM data in a cable-stayed bridge 10

Construction of a traffic estimation model using dynamic res...

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10th International Conference on Structural Dynamics (EURODYN)

作者： Wattana, Kaiwan Nishio, Mayuko Dept Rural Rd 9 Paholyotin Rd Bangkok 10220 Thailand Yokohama Natl Univ Hodogaya Ku 79-5 Tokiwadai Yokohama Kanagawa 2408501 Japan

This paper presents the traffic volume estimation by constructing a statistical model using dynamic response features acquired in the structural health monitoring of an in-service cable-stayed bridge. The study started from the correlation coefficient analysis between some dynamic response features and the environmental and operational effects. It was found that the response amplitudes at several resonant modes were dominantly influenced by the traffic volume. The numerical analysis was thus conducted to understand the relationship between the traffic flow parameters;speed and density, and the bridge dynamic responses. The results showed that the sensitivity of acceleration amplitude to the traffic flow speed increased with increasing the traffic density. A linear regression model to estimate the traffic volume was then constructed using the training data. The explanatory variables were determined by a procedure that considered the correlation coefficients of possible features to the traffic volume and their multicollinearity. The constructed regression model showed the accurate fitting performance to the traffic data, and it was also capable of predicting both the traffic volume per five minute and the average daily traffic (ADT). (c) 2017 The Authors. Published by Elsevier Ltd.

关键词： traffic volume estimation linear regression model dynamic response features response amplitude cable-stayed bridge

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Registration with linear regression model in Augmented Reality

Registration with Linear Regression Model in Augmented Reali...

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2011 IEEE International Conference on Computer Science and Automation Engineering(CSAE 2011)

作者： MA De-yi,CHEN Yi-min,LI Qi-ming,HUANG Chen,XU Sheng,ZOU Yi-bo Department of Computer Science and Technology School of Computer Engineering and Science,Shanghai University Shanghai,China

Precise registration is very important in augmented *** magnetic tracking is a normal instrument to measure position and *** paper presents a novel registration method with linear regression model in augmented *** on the data,which are measured by the magnetic tracking,a linear regression model is set *** the linear regression model,the world coordinate and the magnetic tracking coordinate are *** theory analysis and simulation prove the feasibility of this brand new *** system is more precise and robust with linear regression model.

关键词： Augmented reality Registration linear regression model

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A Cross-Core Performance model for Heterogeneous Many-Core Architectures 12th

A Cross-Core Performance Model for Heterogeneous Many-Core A...

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12th International Conference on High-Performance Computing for Computational Science (VECPAR)

作者： Pinheiro, Rui Roma, Nuno Tomas, Pedro Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal

ISBN: (纸本)9783319619828;9783319619811

An accurate performance predictor to identify the most suitable core-architecture to execute each thread/workload in a heterogeneous many-core structure is proposed. The devised predictor is based on a linear regression model that considers several different parameters of the many-core processor architectures, including the cache size, issue-width, re-order buffer size, load/store queues size, etc. The devised predictor is easily integrated in most system schedulers, providing the ability to periodically determine whether a certain thread is running in the most efficient core-architecture. The obtained experimental results show that the devised model is able to identify the correct core-architecture in a large majority of the cases, leading to average performance differences as low as 7% when compared with an oracle scheduling solution.

关键词： Performance estimation linear regression model Heterogeneous systems Single-ISA architecture Many-core processor Application scheduling

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Partial Least-Squares Method for Three-Mode Three-Way Datasets Based on Tucker model

Partial Least-Squares Method for Three-Mode Three-Way Datase...

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Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS)

作者： Tsuchida, Jun Yadohisa, Hiroshi Doshisha Univ Grad Sch Culture & Informat Sci Kyoto 6100394 Japan Doshisha Univ Dept Culture & Informat Sci Kyoto 6100394 Japan

When analyzing two three-mode three-way datasets (object x variable x condition), the objective is to obtain common factors that show the relationships between the two datasets. The partial least-squares (PLS) method has been applied to such datasets to investigate the common factors. However, the PLS method was proposed for two-mode two-way datasets, such as multivariate datasets. Therefore, this method does not consider the condition when searching for relationships between datasets;that is, it tends to regard the same variable under different conditions as different variables. To address this problem, we extended the PLS method to three-mode three-way datasets by using the Tucker model so that the same variable under different conditions is regarded as the same. Moreover, we can apply the proposed method to three-mode three-way datasets with different dimensions for the conditions and variables, and the output is obtained in the form of three-mode three-way datasets. We show the advantage of the proposed method by applying it to a multicollinearity case as a numerical example. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Alternative least squares Dimensional reduction linear regression model

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Arm Movements Effect on Grasping Force Prediction Using Surface Electromyography Signals

Arm Movements Effect on Grasping Force Prediction Using Surf...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Chen, Chen Xie, Tao Yao, Lin Sheng, Xinjun Zhu, Xiangyang Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat 800 Dongchuan Rd Shanghai 200240 Peoples R China Univ Waterloo Fac Engn Dept Syst Design Engn Waterloo ON Canada

ISBN: (纸本)9781538637425

This study investigated the arm movements effect on the relationship between surface electromyography (EMG) signals and grasping force. An experiment was conducted with four static arm conditions and two dynamic arm conditions. Six able-bodied subjects participated in the experiment. Surface EMG signals were acquired from five forearm muscles to build a multiple linear regression model. Subjects were instructed to complete three kinds of calibration tasks to train the model and one voluntarily varying grasping force task to test the model performance. The grasping force exerted by each subject was limited to be lower than 50% maximum voluntary contraction (MVC) grasping force. Mean absolute difference (MAD) between predicted and observed grasping force was used to estimate the prediction performance. The window size of moving average filter was firstly optimized. Results showed that arm movements had a significant impact on grasping force prediction performance. Inter-condition MADs (training data and testing data are from different arm conditions) were greater than intra-condition MADs (training data and testing data are from the same arm condition, average 7.41% +/- 1.46% MVC vs. 6.03 +/- % 0.40% MVC, p = 0.023). A multi-condition training scheme was applied to attenuate the arm movements effect. The multi-condition training scheme was proved to be useful to improve the model robustness to the arm movements effect.

关键词： grasping force prediction surface electromyography arm movements linear regression model

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On the Generalization Performance of a regression model with Imprecise Elements

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2017年第5期25卷 723-740页

作者： Ferraro, Maria Brigida Sapienza Univ Rome Dept Stat Sci Ple A Moro 5 I-00185 Rome Italy

A linear regression model for imprecise random variables is considered. The imprecision of a random element has been formalized by means of the LR fuzzy random variable, characterized by a center, a left and a right spread. In order to avoid the non-negativity conditions the spreads are transformed by means of two invertible functions. To analyze the generalization performance of that model an appropriate prediction error is intro duced, and it is estimated by means of a bootstrap procedure. Furthermore, since the choice of response transformations could affect the inferential procedures, a computa tional proposal is introduced for choosing from a family of parametric link functions, wthe Box-Cox family, the transformation parameters that minimize the prediction error of the model.

关键词： LR fuzzy random variable linear regression model prediction error boot-strap approach Box-Cox transforms

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Shrinkage Estimation of linear regression models with GARCH Errors

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JOURNAL OF STATISTICAL THEORY AND APPLICATIONS 2016年第4期15卷 405-423页

作者： Hossain, S. Ghahramani, M. Univ Winnipeg Dept Math & Stat Winnipeg MB R3B 2E9 Canada

This paper introduces shrinkage estimators for the parameter vector of a linear regression model with conditionally heteroscedastic errors such as the class of generalized autoregressive conditional heteroscedastic (GARCH) errors when some of the regression parameters are restricted to a subspace. We derive the asymptotic distributional biases and risks of the shrinkage estimators using a large sample theory. We show that if the shrinkage dimension exceeds two, the relative efficiency of the shrinkage estimator is strictly greater than that of the full model estimator. Furthermore, a Monte Carlo simulation study is conducted to examine the relative performance of the shrinkage estimators with the full model estimator. Our large sample theory and simulation study show that the shrinkage estimators dominate the full model estimator in the entire parameter space. We illustrate the proposed method using a real data set from econometrics.

关键词： Stein-type shrinkage likelihood ratio test linear regression model GARCH error asymptotic bias asymptotic risk

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