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Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

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FIBERS AND POLYMERS 2009年第3期10卷 384-393页

作者： Balci, Onur Ogulata, R. Tugrul Kahramanmaras Sutcu Imam Univ Dept Text Engn Fac Engn & Architecture TR-46100 K Maras Turkey Cukurova Univ Dept Text Engn Fac Engn & Architecture TR-01330 Adana Turkey

Changes on the CIELab values of the dyed materials after the different chemical finishing treatments using artificial neural network (ANN) and linear regression (LR) models have been predicted. The whole structural properties of fabrics and some process data which were from fiber to the finishing parameters were accepted as inputs in these models. The networks having different structures were established, and it was also focus on the parameters which could affect the performance of the established networks. It was determined that we could successfully predict the color differences values occurring on the material after the finishing applications. In addition, we realized that some ANN parameters affected the prediction performance while establishing the models. After training ANN models, the prediction of the color difference values was also tried by linear regression models. Then, extra ANN models were established for all outputs using the parameters as inputs in the LR equations, and the prediction performances of both established models were compared. According to the results, the neural network model gives a more accurate prediction performance than the LR models.

关键词： Artificial neural network Chemical finishing CIELab linear regression model Prediction

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The behaviour of random forest permutation-based variable importance measures under predictor correlation

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BMC BIOINFORMATICS 2010年第1期11卷 110-110页

作者： Nicodemus, Kristin K. Malley, James D. Strobl, Carolin Ziegler, Andreas Univ Oxford Wellcome Trust Ctr Human Genet Oxford OX3 7BN England Univ Oxford Dept Clin Pharmacol Oxford OX3 7DQ England NIMH Genes Cognit & Psychosis Program Intramural Res Program NIH Bethesda MD USA NIH Math & Stat Comp Lab Div Computat Biosci Ctr Informat Technol Bethesda MD 20892 USA Univ Munich Dept Stat D-80539 Munich Germany Med Univ Lubeck Inst Med Biometrie & Stat Univ Klinikum Schleswig Holstein D-23562 Lubeck Germany

Background: Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results. Results: In the case when both predictor correlation was present and predictors were associated with the outcome (H-A), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H-0) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under H-A and was unbiased under H-0. Scaled VIMs were clearly biased under H-A and H-0. Conclusions: Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.

关键词： linear regression model Random Forest Correlate Variable Bivariate model Correlate Predictor

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linear regression models for Predicting Liquefaction during Cyclic Triaxial Testing

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GEOTECHNICAL TESTING JOURNAL 2009年第4期32卷 335-346页

作者： Polito, Carmine Valparaiso Univ Valparaiso IN 46383 USA

Cyclic triaxial tests are commonly used in research and engineering practice to evaluate soil liquefaction potential and the factors that influence it. During testing it is necessary to estimate the level of loading to be applied to the specimen. This can be a difficult task unless the engineer has previously acquired a reasonable amount of experience with this type of testing. In order to provide guidance in selecting an appropriate level of loading, a series of four linear regression models have been developed to predict liquefaction in sands and soils with nonplastic silts. The models were separated by the soil type and the method of specimen preparation. These models were developed using the data from over 750 tests collected from the author's files and the literature. The validity of each model was assessed by examining the statistical parameters of the model, an analysis of residuals, and predictions made using additional data obtained from the literature.

关键词： cyclic triaxial test linear regression model moist tamping pluviation slurry-deposition

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A standard paleoclimatological approach to temperature reconstruction in historical climatology: an example from the Czech Republic, AD 1718-2007

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INTERNATIONAL JOURNAL OF CLIMATOLOGY 2009年第10期29卷 1478-1492页

作者： Dobrovolny, Petr Brazdil, Rudolf Valasek, Hubert Kotyza, Oldrich Mackova, Jarmila Halickova, Monika Masaryk Univ Inst Geog CS-61137 Brno Czech Republic Moravian Land Arch Brno 65601 Czech Republic Reg Museum Litomerice 41201 Czech Republic

This paper addresses a standard paleoclimatological approach to reconstructing temperatures in historical climatology. Weighted monthly temperature indices on a scale from -3 to +3, derived from documentary evidence for the period 1718-1850 in the Czech Lands, were used to create a series of seasonal (DJF, MAM, JJA, SON) and annual temperature indices as simple sums of indices from corresponding months. The period 1771-1850, when such indices overlap with instrumental measurements taken at Prague-Klementinum, was used to calibrate and verify relations between indices and the temperatures measured, using linear regression models (LRM) evaluated by various statistical characteristics. The most accurate LRM performance was obtained for DJF, MAM and annual temperatures, while JJA and SON temperature results proved slightly less reliable. Reconstructed data for 1718-1770 were combined with measured data to create series of seasonal and annual temperatures for the Prague-Klementinum station covering the period 1718-2007. Various problems in temperature reconstruction are further discussed: the completeness and quality of the index. series, the expression of extreme temperatures, and the selection of a reference period for calibration. The suggested standard paleoclimatological approach lends more objectivity to both verification of reconstructed temperatures and the expression of uncertainties in reconstruction. Copyright (C) 2008 Royal Meteorological Society

关键词： documentary data temperature indices linear regression model temperature reconstruction Prague-Klementinum Czech Republic

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Influence of workpiece hardness on EDM performance

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INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE 2009年第9期49卷 744-748页

作者： Marafona, Jose Duarte Araujo, Arlindo Univ Porto Fac Engn Dept Engn Mecan & Gestao Ind P-4200465 Oporto Portugal

The aim of this research is to show the influence of the hardness of the alloy steel on the material removal rate and on the workpiece surface roughness. The Taguchi methodology was used to study that influence. The result of the verification test for workpiece surface roughness was a strong confirmation. This type of outcome allows the use of the additive model to predict the workpiece surface roughness with an average error of 0.4%. The result of the verification test for material removal rate was a poor confirmation due to an interaction of parameters. This type of outcome does not allow the additive model to predict the material removal rate with accuracy. Therefore, a linear regression model was developed for material removal rate using workpiece hardness and its interactions, among other variables. This model predicts the material removal rate with an average error of 1.06%. These results show that workpiece hardness and its interactions have influence on the material removal rate and on the workpiece surface roughness. (C) 2009 Elsevier Ltd. All rights reserved

关键词： Electrical discharge machining (EDM) Alloy steel hardness Material removal rate (MRR) Workpiece surface roughness Taguchi methodology linear regression model

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Functional linear regression with derivatives

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JOURNAL OF NONPARAMETRIC STATISTICS 2009年第1期21卷 19-40页

作者： Mas, Andre Pumo, Besnik Inst Modelisat Math Montpellier Montpellier 5 France Agrocampus Ouest Inst Natl Hort & Paysage Angers 1 France

We introduce a new model of linear regression for random functional inputs taking into account the first-order derivative of the data. We propose an estimation method that comes down to solving a special linear inverse problem. Our procedure tackles the problem through a double and synchronised penalisation. An asymptotic expansion of the mean square prevision error is given. The model and the method are applied to a benchmark dataset of spectrometric curves and compared with other functional models.

关键词： functional data linear regression model differential operator penalisation spectrometric curves

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Stein-rule estimation under an extended balanced loss function

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2009年第10期79卷 1259-1273页

作者： Shalabh Toutenburg, H. Heumann, C. Indian Inst Technol Dept Math & Stat Kanpur 208016 Uttar Pradesh India Univ Munich Dept Stat Munich Germany

This paper extends the balanced loss function to a more general setup. The ordinary least squares estimator (OLSE) and Stein-rule estimator (SRE) are exposed to this general loss function with quadratic loss structure in a linear regression model. Their risks are derived when the disturbances in the linear regression model are not necessarily normally distributed. The dominance of OLSE and SRE over each other and the effect of departure from normality assumption of disturbances on the risk property are studied.

关键词： linear regression model Stein-rule estimator ordinary least squares estimator balanced loss function non-normal disturbances

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An alternative form of the Watson efficiency

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2009年第8期139卷 2767-2774页

作者： Yang, Hu Wang, Litong Chongqing Univ Coll Math & Phys Chongqing 400030 Peoples R China

Watson [1951. Serial correlation in regression analysis. Ph.D. Thesis, Department of Experimental Statistics, North Carolina State College, Raleigh] introduced a relative efficiency, which is often called the Watson efficiency in literatures, to measure the inefficiency of the least squares in linear regression models, The Watson efficiency is defined by determinant, but we shall show by two examples that such a criterion does not always work well in some cases. In this paper, an alternative form based on Euclidean norm of the Watson efficiency is proposed and some examples are given to illustrate Superiority of the new relative efficiency. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Euclidean norm Least squares linear regression model Watson efficiency

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Nonparametric Bayesian modelling using skewed Dirichlet processes

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2009年第3期139卷 1203-1214页

作者： Iglesias, Pilar L. Orellana, Yasna Quintana, Fernando A. Pontificia Univ Catolica Chile Fac Matemat Dept Estadist Santiago Chile

We introduce a new class of discrete random probability measures that extend the definition of Dirichlet process (DP) by explicitly incorporating skewness. The asymmetry is controlled by a single parameter in such a way that symmetric DPs are obtained as a special case of the general construction. We review the main properties of skewed DPs and develop appropriate Polya urn schemes. We illustrate the modelling in the context of linear regression models of the capital asset pricing model (CAPM) type, where assessing symmetry for the error distribution is important to check validity of the model. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Bayes factor Density estimation Dirichlet process linear regression model Polya sequence Skewed distribution

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Prognosis of the copper bromide laser generation through statistical methods

Prognosis of the copper bromide laser generation through sta...

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17th International Symposium on Gas Flow, Chemical Lasers, and High-Power Lasers

作者： Iliev, Iliycho P. Gocheva-Ilieva, Snezhana G. Sabotinov, Nikola V. Tech Univ Plovdiv Dept Phys 25 Tz Djustabanov St Plovdiv 4000 Bulgaria Paisii Hilendarski Univ Plovdiv Dept Appl Math & Modeling Plovdiv 4000 Bulgaria Bulgarian Acad Sci Georgi Nadjakov Inst Solid State Phys Dept Metal Vapor Lasers Sofia 1784 Bulgaria

ISBN: (纸本)9780819473653

A new statistical approach to predict the possible increase of the laser generation in copper bromide lasers is presented. The results are based on a big amount of experimental data with wavelengths 510.6 nm and 578.2 nm, obtained in Georgi Nadjakov Institute of Solid State Physics, Bulgarian Academy of Sciences. The data are treated by statistical methods, such as multivariable factor analysis and linear regression modeling. The approximate increase of the output laser power up to 18% is obtained by using six input working parameters. The proposed methodology is applied for prognosticate the experiment and further design of the laser tube.

关键词： copper bromide laser laser generation prognosis factor analysis principal components analysis multiple regression linear regression model

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