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Search Results: 1 - 10 of 4385 matches for " PLS regression "
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Application of PLS-Regression as Downscaling Tool for Pichola Lake Basin in India  [PDF]
Manish Kumar Goyal, Chandra Shekhar Prasad Ojha
International Journal of Geosciences (IJG) , 2010, DOI: 10.4236/ijg.2010.12007
Abstract: In this paper, downscaling models are developed using Partial Least Squares (PLS) Regression for obtaining projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of this approach is demonstrated 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. In this paper, we use PLS regression for quality prediction and its use for the variable selection based on the variable importance. The results of downscaling models using PLS regression show that precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.
In vivo prediction of intramuscular fat in pigs using computed tomography  [PDF]
J?rgen Kongsro, Eli Gjerlaug-Enger
Open Journal of Animal Sciences (OJAS) , 2013, DOI: 10.4236/ojas.2013.34048

One hundred and four pure-bred Norwegian Duroc boars were CT (computed tomography) scanned to predict the in vivo intramuscular fat percentage in the loin. The animals were slaughtered and the loin was cut commercially. A muscle sample of the m. Longissimus dorsi was sampled and analyzed by the use of near-infrared spectroscopy. Data from CT images were collected using an in-house MATLAB script. Calibration models were made using PLS (partial least square) regression, containing independent data from CT images and dependent data from near-infrared spectroscopy. The data set used for calibration was a subset of 72 animals. The calibration models were validated using a subset of 32 animals. Scaling of independent data and filtering using median filtering were tested to improve predictions. The results showed that CT is not a feasible method for in vivo prediction of intramuscular content in swine.

Land Suitability Evaluation for Agricultural Cropland in Mongolia Using the Spatial MCDM Method and AHP Based GIS  [PDF]
Munkhdulam Otgonbayar, Clement Atzberger, Jonathan Chambers, D. Amarsaikhan, Sebastian B?ck, Jargaltulga Tsogtbayar
Journal of Geoscience and Environment Protection (GEP) , 2017, DOI: 10.4236/gep.2017.59017
Abstract: The purpose of this study was to prepare a cropland suitability map of Mongolia based on comprehensive landscape principles, including topography, soil properties, vegetation, climate and socio-economic factors. The primary goal was to create a more accurate map to estimate vegetation criteria (above ground biomass AGB), soil organic matter, soil texture, and the hydrothermal coefficient using Landsat 8 satellite imagery. The analysis used Landsat 8 imagery from the 2016 summer season with a resolution of 30 meters, time series MODIS vegetation products (MOD13, MOD15, MOD17) averaged over 16 days from June to August 2000-2016, an SRTM DEM with a resolution of 30 meters, and a field survey of measured biomass and soil data. In total, 6 main factors were classified and quality evaluation criteria were developed for 17 criteria, each with 5 levels. In this research the spatial MCDM (multi-criteria decision-making) method and AHP based GIS were applied. This was developed for each criteria layer’s value by multiplying parameters for each factor obtained from the pair comparison matrix by the weight addition, and by the suitable evaluation of several criteria factors affecting cropland. General accuracy was 88%, while PLS and RF regressions were 82.3% and 92.8%, respectively.
Latent Structure Linear Regression  [PDF]
Agnar H?skuldsson
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.55077

A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes most linear regression methods based on linear algebra. The H-principle of mathematical modelling is presented. It uses the analogy between the modelling task and measurement situation in quantum mechanics. The principle states that the modelling task should be carried out in steps where at each step an optimal balance should be determined between the value of the objective function, the fit, and the associated precision. H-methods are different methods to carry out the modelling task based on recommendations of the H-principle. They have been applied to different types of data. In general, they provide better predictions than linear regression methods in the literature.

The Analysis of Influence Factors of Single Box Consumption Based on the PLS Regression—From the Data of Tobacco Consumption Control in Honghe Cigarette Factory

许磊, 李兴绪, 张雁, 李文能, 张波
Statistics and Applications (SA) , 2015, DOI: 10.12677/SA.2015.43016
Abstract: 本文在分析了偏最小二乘回归分析和多元线性回归分析的适用条件基础上,认为偏最小二乘回归(PLS)可以有效地解决变量间多重共线性的问题,甚至适合在样本量少于变量个数的情况下进行回归建模。然后,依据红河卷烟厂烟丝消耗控制的12组样本数据,本文比较分析了偏最小二乘回归建模和多元线性回归建模的结果,发现影响因变量单箱耗丝的显著性因素为单箱废烟、单箱跑条、单箱小包机剔除量和单箱空头剔除量。因此,卷烟厂在卷包过程中的降耗工作应当首先从控制这四个单箱损耗指标开始实施,才能取得立竿见影的效果。
On the basis of some conditions for the application of partial least squares regression analysis and multivariate linear regression analysis in this paper, we can conclude that partial least squares regression (PLS) can effectively improve multicollinearity of variables. When the sample size is less than the number of variables, it also can be used to do regression modeling. Then, from 12 groups of sample data of Tobacco consumption control in Honghe Cigarette Factory, we have analyzed and compared the results of partial least squares regression modeling and multivariate linear regression modeling in the paper. It has showed that the significant factors affecting the single box consumption are single case of Wasting, single case of Running, single case of Packet rejection and single case of Short excluded volume. Therefore, the work of the cigarette factory in the process of reducing the cost should be firstly controlling these four single box loss indicators, so that we will achieve the immediate results.
Partial least squares (PLS) regression and its application to coal analysis
Alciaturi,Carlos E; Escobar,Marcos E; De La Cruz,Carlos; Rincón,Carlos;
Revista Técnica de la Facultad de Ingeniería Universidad del Zulia , 2003,
Abstract: instrumental chemical analysis methods use the relationships between a signal obtained and a property (generally a concentration) of the system under study. the study and applications of these relations is known as chemometrics, a discipline of intense development, with ample applications in chemical and process industry and in environmental studies. the method of partial least squares (pls) is one of the most used in chemometrics. this method is closely related to principal components regression (pcr). pls have theoretical and computational advantages that have led to a great number of applications. the numbers of internet sites referring to pls are hundreds of thousands. here, we give the fundamentals and show an application to prediction of coal properties from mid-infrared data, with the purpose of developing fast, non-destructive methods of analysis for these materials.
Assessing wheat performance using environmental information
Dodig Dejan,Zori? Miroslav,Kne?evi? Desimir,Dimitrijevi? Bojana
Genetika , 2007, DOI: 10.2298/gensr0703413d
Abstract: The partial least squares (PLS) regression model was applied to wheat data set with objective to determining the most relevant environmental variables that explained biomass per plant and grain yield genotype x environment interaction (GEI) effects. The data set had 25 wheat genotypes (20 landraces + 5 cultivars) tested for 4 years in two different water regimes: rainfed and drought. Environmental variables such as maximum soil temperature at 5 cm in April and May, soil moisture in the top 75 cm in March, and sun hours per day in May accounted for a sizeable proportion of GEI for biomass per plant. Similar results were obtained for grain yield: maximum soil temperature at 5 cm in April, May and June, and sun hours per day in May were related to the factor that explained the largest portion (>38%) of the GEI. Generally, wheat landraces are able to better exploit environments with higher temperatures and lower water availability during vegetative growth (March-June) than cultivars.
Determination of the Biodiesel Content in Petrodiesel/Biodiesel Blends: A Method Based on Uv-Visible Spectroscopy and Chemometrics Tools  [PDF]
Armando Guerrero, Francisco Anguebes, Mepivoseth Castelán, Victorino Morales, Ismael León, José C. Zavala, Atl V. Córdova
American Journal of Analytical Chemistry (AJAC) , 2013, DOI: 10.4236/ajac.2013.46034
Abstract: In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentrations between 0-100 wt% were prepared. The spectral fingerprints that were obtained from the analysis of the samples by UV-visible spectroscopy were used to build predictive model using PLS regression. The predictive ability of the models was evaluated through statistical parameters: the standard error of calibration (SEC), the standard error of validation (SEV), the correlation coefficient of calibration (r Cal) and validation (r Val), the ratio (SEC/SEV), the coefficient of determination R2, the paired data Students t-test, cross-validation and external validation. The results indicate that the PLS model predicts the concentration of biodiesel from African palm with high precision in mixtures with petrodiesel. The method developed in this study can be applied to determine the concentration of biodiesel African palm in mixtures of petrodiesel in a more rapid and economical way. Moreover, this method has less analytical errors and is more environmentally friendly than the conventional methods.
Quantitative Analysis of Lavender (Lavandula angustifolia) Essential Oil Using Multiblock Data from Infrared Spectroscopy  [PDF]
Nathalie Dupuy, Vincent Gaydou, Jacky Kister
American Journal of Analytical Chemistry (AJAC) , 2014, DOI: 10.4236/ajac.2014.510071
Abstract: Near-infrared and mid-infrared spectroscopies were currently used to analyze natural compounds. During the last ten years various multiblocks methods were developed such as Concatenated PLS, Hierarchical-PLS (H-PLS), and MultiBlock-PLS (MB-PLS). These three algorithms were used to analyze 55 lavender (Lavandula angustifolia) essential oil samples. The results obtained were compared to the ones obtained respectively in NIR and MIR ranges. The accuracies of the models depend on the spectroscopic technique, pretreatment and the PLS methods. The results showed that the choice of the factor numbers used to build the multiblock models was the most important parameter for the H-PLS and MB-PLS methods.
The Application of Variable Selection to Multi-Collinearity Problems—Based on the Research and Development Input and Output Data

安蕾, 贾慧芝
Statistics and Applications (SA) , 2015, DOI: 10.12677/SA.2015.43015
Abstract: 科研投入是提升一国创新能力的前提,但指标之间往往存在较强的多重共线性问题。本文使用岭回归、PLS回归的方法,把我国31个主要的省市自治区分为两类,依次构建R&D投入–产出模型,以期了解我国R&D投入模式。研究结果表明,不同地区受科技投入指标的影响不同,中西部发展地区受政府及企业投入的影响都很显著,而经济较为发达的省市企业的科技创新意识更强。
A prerequisite for the promotion of a nation’s innovation ability is the input of scientific research, but there are always many multi-collinearity problems among the indexes. In order to know the R&D input-output mode, 31 provinces are divided into two parts to set up ridge regression and PLS regression models separately. The research results show that different areas are influenced by different factors. The Midwest is susceptible to the input of the government and companies, while the technological innovation consciousness of the enterprises in the developed area is stronger.
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