Set Of Uncorrelated Variables Research Articles

Estimating lichen α- and β-diversity using satellite data at different spatial resolutions

Understanding biodiversity patterns and its environmental drivers is crucial to meet conservation targets and develop effective monitoring tools. Inconspicuous species such as lichens require special attention since they are ecologically important but sensitive species that are often overlooked in conservation planning. Remote sensing (RS) can be particularly beneficial for these species as in combination with modelling techniques it allows planners to assess and better understand biodiversity patterns. This study aims to model the lichen α-diversity (species richness) and β-diversity (species turnover) biodiversity components using high resolution RS variables across a subarctic region in Northern Quebec (∼190.25 km2). Two sensors, one commercial (WorldView-3, WV3) and another freely accessible (Sentinel-2, S2), at different resolutions (1.2 m and 10 m, respectively) were tested separately to develop our variables and feed the models. Lichens were sampled in 45 plots across different habitat types, ranging from forested habitats (coniferous, deciduous) to wetlands (bogs, fens) and rocky outcrops. Two sets of uncorrelated variables (Red and NIR; EVI2) from each sensor were parallelly used to build the α- and β-diversity models (8 models in total) through Poisson regressions and generalized dissimilarity modelling (GDM), respectively. Red and NIR variables were useful for modeling the two biodiversity components at both resolutions, providing information on stand canopy closure and structure, respectively. EVI2, especially from WV3, was only informative for assessing β-diversity, providing similar information than Red. Poisson models explained up to 32 % of the variation in lichen α-diversity, with Red, NIR and EVI2, either from WV3 or S2, showing negative relationships with lichen richness. GDMs described well the relationship between β-diversity and spectral dissimilarity (R2 from 0.25 to 0.30), except for the S2 EVI2 model (R2 = 0.07), confirming that more spectrally and thus environmentally different areas tend to harbor different lichen communities. While WV3 often outperformed the S2 sensor, the latter still provides a powerful tool for the study of lichens and their conservation. This study contributes to improve our knowledge and to inform on the use of RS to understand biodiversity patterns of inconspicuous species, which we consider to be an essential step to enhance their representation in conservation planning.

M20 - COMBINING POLYGENIC RISK SCORES ACROSS SEVERAL TRAITS CAN IMPROVE SCHIZOPHRENIA RISK PREDICTION

Background Schizophrenia is a highly heritable disorder and is known to have a substantial common polygenetic component [1] . The polygenic risk score approach [1] allows common genetic liability to the disorder to be directly estimated in individuals regardless of their affected status. In addition to weakly predicting schizophrenia affected status in independent case-control datasets, polygenic risk score approaches have revealed significant shared SNP heritability between schizophrenia and multiple psychiatric and behavioural traits [2 , 3] . We therefore postulated that combining risk score profiles derived from several traits might improve the prediction of schizophrenia risk. Methods Polygenic risk score profiles were calculated for the CLOZUK schizophrenia GWAS study [4] (11260 cases, 24542 controls) by training on six recent GWAS datasets: schizophrenia (independent of the CLOZUK set), bipolar disorder, bipolar vs schizophrenia [5] , major depression disorder, educational attainment [6] and neuroticism [7] . Principal component analysis (PCA) was employed to generate factor loadings for the six polygenic risk score profiles. The advantage of PCA is that it converts a set of possibly correlated variables (e.g. due to shared genetic liability) into a set of uncorrelated variables, with the first principal component accounting for the greatest degree of variance in the dataset outcome. We investigated the prediction accuracy of the first principal component compared to the schizophrenia polygenic risk score by means of area under the curve (AUC) analysis. Results Our analysis indicates that schizophrenia case prediction accuracy of the combined schizophrenia and neuropsychiatric-related first principal component is increased as compared to the AUC of schizophrenia polygenic risk scores alone. The first principal component also visually separates cases and controls in the CLOZUK individuals based upon their combined (schizophrenia and neuropsychiatric-related traits) profiles. Discussion We have proposed an approach to combine genetic evidence for schizophrenia and neuropsychiatric-related traits into one score and tested its prediction accuracy. This approach takes advantage of the shared genetic risk between schizophrenia, psychiatric disorders and behavioural traits, and at present, can improve the prediction of schizophrenia risk.

Sustainability evaluation for biomass supply chain synthesis: Novel principal component analysis (PCA) aided optimisation approach

Abstract The evaluation of sustainability performance of a biomass supply chain is often compounded of a complex series of variables. The redundancies in variables often make the results become hard to be analysed and diagnosed. Therefore, principal component analysis (PCA) is introduced to reduce the redundancy of data series by converting a series of correlated variables into a set of uncorrelated variables known as principal components (PCs), without losing too much information. However, the optimisation of PCs is relatively difficult as PCs encompass of convex combinations of original variables. In this paper, a novel PCA aided optimisation approach is introduced to solve the multi-echelon biomass supply chain problem (i.e., technology selection and transportation design), with the consideration of economic, environmental (including various impact categories and environmental footprints) and social (including health aspect, safety aspect and job creation) dimensions. On top of that, analytical hierarchy process (AHP) is applied to determine the priority scale assigned to each objective. The model is illustrated by using a case study in Johor, Malaysia. In this case study, the original 13 variables (indicators) had been successfully reduced to less than 3 PCs. Besides, the obtained results are benchmarked with two other conventional optimisation approaches, namely weighted-sum approach and max-min aggregation approach. The results show that PCA optimisation approach can provide reliable and comparable results (degree of satisfaction of the obtained results, λ S C M are greater than 70%). In addition, sensitivity analysis is conducted to analyse the effect of relative priority of each objective on the technology selection and transportation design. This research is also expected to be useful for the big data (large volumes of extensively varied data that are generated and processed at high velocity) analysis in future supply chain management.

Nonlinear Kalman filters for aircraft engine gas path health estimation with measurement uncertainty

This paper is concerned with nonlinear Kalman filtering approach to aircraft engine gas path analysis with measurement uncertainty. The uncertain measurements are characterized by time delay and packet dropout. The delay step of physical parameters occurs randomly, and its probability is regulated by a set of uncorrelated variables following Poisson distribution and uniform distribution. Packet dropout is caused as the data are not collected in time or data buffer overflows. The novel nonlinear Kalman filters (KFs) are developed using a multistep recursive estimation strategy with self-tuning buffer in the presence of gas path measurement uncertainty. The data buffers are introduced in the state estimator, the length of which is adaptive to the information loss level. The algorithms run recursively using the new arrival data and buffer position information. With a more effective arrangement of the collected measurements in real time, the better estimation accuracy of gas path health status is expected. Simulations involving abrupt fault and degradation datasets of aircraft engines were carried out to numerically evaluate and compare the performance of the improved nonlinear KFs with their existing KFs in the context of health estimation with time delay and packet dropout. The test results demonstrate that the proposed methodology not only reduces the computational time but also obtains a satisfactory accuracy for state estimation in the cases of engine gas path measurement uncertainty.

宁夏生态足迹影响因子的偏最小二乘回归分析

PDF HTML阅读 XML下载 导出引用 引用提醒 宁夏生态足迹影响因子的偏最小二乘回归分析 DOI: 10.5846/stxb201211181613 作者: 作者单位: 宁夏大学农学院草业科学研究所,北方民族大学计算机科学与工程学院,宁夏大学农学院,宁夏大学农学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目（31160484）；北方民族大学自主科研资助项目（2011ZQY013） Analysis the relationship between ecological footprint (EF) of ningxia and influencing factors: Partial Least-Squares Regression (PLS) Author: Affiliation: School of Agriculture of Ningxia University,,School of Agriculture of Ningxia University, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态足迹分析方法是一种度量区域生态可持续程度的有效方法，偏最小二乘回归法（PLS）能有效解决多元回归分析中变量的多重相关性问题，具有容易操作，相关分析精度高等特点。以宁夏为研究区域，在计算了宁夏2001-2010年人均生态足迹的基础上，应用偏最小二乘回归分析法，对影响宁夏生态足迹的各因子的重要程度进行了分析。通过变量投影重要性分析、特异点分析和预测分析，证明所得偏最小二乘回归模型具有较好的精度。研究结果为：2001-2010年，宁夏人均生态足迹由1.818103793 hm2上升至 2.894958909 hm2，生态赤字由1.28352051 hm2上升至2.42316627 hm2，生态承载力由0.53458328 hm2下降至0.47179264 hm2；全区GDP、城镇居民人均生活消费支出、第二产业产值和第一产业产值是影响宁夏生态足迹的显著因子。 Abstract:The model of ecological footprint (EF) was originally brought forward by ecological economist William E. Rees and his fellows in 1992, and Wackernage modified and perfected the model in 1996. Ecological footprint is an effective method to evaluate the ecological security and sustainability of a region from the aspects of demand and supply. Ecological footprint is defined as biologically productive areas required to sustainably support certain population in a region, and ecological footprint theory can be simply generalized as follows. First, people's daily consumption should be classified and the biologically productive area needed to support the consumption should be caculated. The ecological footprint of people is the summation of biologically productive area. We can then reckon the ecological capacity and convert to the biologically productive land area for comparison. At last, we can compare the ecologcial footprint with the ecological capacity Partial. Least-Squares Regression (PLS) method was proposed by S.Wold and C.Albano in 1983. As a statistical tool, PLS regression has been specifically designed to deal with multiple regression problems where the number of observations are limited and correlations between variables are high. PLS regression has gained great success in scientific fields, such as chemometrics, medicine, market analysis and finance. There are multi-correlation problems between ecological footprint's influencing factors of multiple regression analysis. PLS regression aims at producing a model that can transform a set of correlated explanatory variables into a new set of uncorrelated variables, called PLS factors in this paper. PLS factors capture most information of the independent variables that is useful for explaining and predicting the dependent variables. In the meantime, PLS regression reduces the dimensionality of the regression by using fewer PLS factors than the number of independent variables. The per capita EF and ecological carrying capacity (EC) of Ningxia are calculated from 2001 to 2010 by employing the quantitative method for EF and major important factors influencing the ecological footprint selected by PLS regression. It is found that ecological footprint of Ningxia increased from 1.818103793 hm2 to 2.894958909 hm2, ecological deficit (ED) increased from 1.28352051 hm2 to 2.42316627 hm2 and ecological capacity (EC) decreased from 0.53458328 hm2 to 0.47179264 hm2. GDP of Ningxia, urban per capita life expenditure, the secondary industry, and the primary industry are the main factors influencing ecological footprint of Ningxia. 参考文献 相似文献 引证文献

Partial Least Square Discriminant Analysis for bankruptcy prediction

This paper uses Partial Least Square Discriminant Analysis (PLS-DA) for the prediction of the 2008 USA banking crisis. PLS regression transforms a set of correlated explanatory variables into a new set of uncorrelated variables, which is appropriate in the presence of multicollinearity. PLS-DA performs a PLS regression with a dichotomous dependent variable. The performance of this technique is compared to the performance of 8 algorithms widely used in bankruptcy prediction. In terms of accuracy, precision, F-score, Type I error and Type II error, results are similar; no algorithm outperforms the others. Behind performance, each algorithm assigns a score to each bank and classifies it as solvent or failed. These results have been analyzed by means of contingency tables, correlations, cluster analysis and reduction dimensionality techniques. PLS-DA results are very close to those obtained by Linear Discriminant Analysis and Support Vector Machine.

A pattern recognition and data analysis method for maintenance management

This article presents a pattern recognition method based on grouping by linear relationship a set of faults. The majority of faults can be detected, but only a few experiments can be identified. The algorithm called Principal Component Analysis (PCA) is employed together with the statistical parameters of the signals for detecting and identifying the faults. PCA technique is utilised for modifying dataset reducing the coordinate system, which must be correlated, by linear transformation, into a smaller set of uncorrelated variables called ‘principal components’. The signals analysed were the current and force signals in normal-to-reverse and reverse-to-normal directions of the system.

Principal component analysis: Development of a multivariate index for assessing eutrophication according to the European water framework directive

Abstract The discriminating power of eutrophication assessment schemes is often affected by the intercorrelation between cause (nutrient concentrations) and response (phytoplankton biomass and diversity) variables. Principal component analysis, a multivariate data reduction technique producing new sets of uncorrelated variables, was applied on nitrate, nitrite, ammonia, phosphate and chlorophyll α concentrations from coastal waters in the Aegean Sea, Eastern Mediterranean, and the first principal component was derived and evaluated as a eutrophication index on an independent dataset. The index, a linear combination of the five variables with almost equal weights, was found efficient in discriminating levels of eutrophication and critical thresholds characterizing oligotrophy, mesotrophy and eutrophication were set. The applicability of these thresholds for the implementation of the European Water Framework Directive was also examined thereof a five-level water quality classification scheme was developed.

Statistical Timing Analysis Considering Spatially and Temporally Correlated Dynamic Power Supply Noise

Power supply noise is having increasingly more influence on timing, even though noise-aware timing analysis has not yet been fully established, because of several difficulties such as its dependence on input vectors and dynamic behavior. This paper proposes static timing analysis that takes power supply noise into consideration where the dependence of noise on input vectors and spatial and temporal correlations are handled statistically. We construct a statistical model of power supply voltage that dynamically varies with spatial and temporal correlation, and represent it as a set of uncorrelated variables. We demonstrate that power-voltage variations are highly correlated and adopting principal component analysis as an orthogonalization technique can effectively reduce the number of variables. Experiments confirmed the validity of our model and the accuracy of timing analysis. We also discuss the accuracy and CPU time in association with the reduced number of variables.

TH‐C‐224C‐05: Analysis of Radiation‐Induced Pneumonitis in Lung Cancer Patients

Purpose: To study clinical, physiological, biological and dose‐volume factors in lung cancer patients who developed symptomatic radiation‐ induced pneumonitis (RP). Method and Materials: Data from two‐hundred patients with non‐small cell lung cancer who received 3D conformal radiotherapy (RT) treatment were analyzed using an in‐house developed MATLAB‐based analysis tool, EUCLID. The effect of 52 clinical, physiological and biological factors, as well as dosimetric variables extracted from the lung dose‐volume histogram (DVH) was analyzed using univariate and multivariate approaches. Results: Thirty nine patients (19%) developed RP. In the univariate analysis, the forced expiratory volume in 1 second (FEV1) post‐treatment, normal tissue complication probability (NTCP), mean lung dose, history of smoking, level of TGF‐β post‐treatment and nodal stage were associated with RP (p‐range 0.001–0.033). When considering the percentage of volume of lung receiving more than a specified dose, we found that 60 Gy (V60) yields the strongest correlation with RP (p=0.0001). In the multivariate analysis, a logistic regression analysis was performed using different sets of uncorrelated variables. The predictive power of the model was quantified using the area under the Receiver‐Operating Characteristic (ROC) curve. The best result was obtained with V30, FEV1, age, time of chemotherapy (before, after or concurrent with RT) and Lobe (location of tumor inside the lung). The area under the curve for these variables was 0.85. While only the regression coefficients of V30 and FEV 1 determined a correlation with RP with a p‐value less than 0.05, the exclusion of the other three variables led to a lower area under the curve. Conclusion: V30 and FEV 1 were the best predictors of symptomatic RP after external beam RT for lung cancer. Multivariate analysis showed that other clinical factors that are not individually strongly associated with RT can be included in a regression model to increase the predictive power of that model.

Sample size and error in the determination of mode shapes by principal components analysis

Principal components analysis (PCA) is a multivariate statistical technique that transforms a data set having a large number of inter-related variables to a new set of uncorrelated variables called the principal components, determined to allow the dimensionality of the data set to be reduced while retaining as much of the variation present as possible. PCA can be applied to dynamic structural response data to identify the predominant modes of vibration of the structure. Because PCA is a statistical technique, there are errors in the computed modes due to the use of a sample of finite size. The aim of this paper is to study the effect of sample size on the accuracy with which the modes of vibration can be computed. The paper focuses predominantly on elastic response data and examines the potential influence of various parameters such as the period of the structure, the input excitation, and the spatial distribution of mass over the structure. Issues relating to errors in the modes of nonlinear structures are also discussed.

Principal Component Analysis In Radar Polarimetry

Abstract. Second order moments of multivariate (often Gaussian) joint probability density functions can be described by the covariance or normalised correlation matrices or by the Kennaugh matrix (Kronecker matrix). In Radar Polarimetry the application of the covariance matrix is known as target decomposition theory, which is a special application of the extremely versatile Principle Component Analysis (PCA). The basic idea of PCA is to convert a data set, consisting of correlated random variables into a new set of uncorrelated variables and order the new variables according to the value of their variances. It is important to stress that uncorrelatedness does not necessarily mean independent which is used in the much stronger concept of Independent Component Analysis (ICA). Both concepts agree for multivariate Gaussian distribution functions, representing the most random and least structured distribution. In this contribution, we propose a new approach in applying the concept of PCA to Radar Polarimetry. Therefore, new uncorrelated random variables will be introduced by means of linear transformations with well determined loading coefficients. This in turn, will allow the decomposition of the original random backscattering target variables into three point targets with new random uncorrelated variables whose variances agree with the eigenvalues of the covariance matrix. This allows a new interpretation of existing decomposition theorems.

A model to predict the residual life of aircraft engines based upon oil analysis data

AbstractThis paper reports on a study using the available oil monitoring information, such as the data obtained using the Spectrometric Oil Analysis Programme (SOAP), to predict the residual life of a set of aircraft engines. The relationship between oil monitoring information and the residual life is established using the concept of the proportional residual, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis programmes. Assuming such a relationship between wear and the residual life exists, we formulated a recursive prediction model for the item's residual life given measured oil monitoring information to date. A set of censored life data of 30 aircraft engines (right censored due to preventive overhaul) along with the history of their monitored metal concentration information are available to us. The metal concentration information includes many variables, such as Fe, Cu, Al, etc.; not all of them are useful, and some of them may be correlated. The principal component analysis (PCA) has been adopted to reduce the dimension of the original data set and to produce a new set of uncorrelated variables, which we shall use in the prediction model. The procedure associated with estimating model parameters is discussed. The model is fitted to the actual SOAP data from the aircraft engines, and the goodness‐of‐fit test has been carried out. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

Designs and solutions to multiple trait comparisons

Multiple trait detection and analysis of quantitative trait loci via linkage to genetic markers is problematic, first because of the increase in the number of comparisons tested, second because of possible multitrait loci effects, and third because of biases due to selection based on phenotypic trait values. Nearly all studies that considered multiple traits have analyzed each trait separately. Two methods have been proposed for multitrait experiments; maximum likelihood multivariate analysis, and canonical transformation to a set of uncorrelated variables. If individuals are selected for genotyping based on a single trait, parameter estimates for other correlated traits will be biased using single trait analyses, and significance levels will be incorrect. With multivariate analysis, unbiased estimates of QTL effects can be derived even with selective genotyping. Furthermore, power is increased per individual genotyped, even if selective genotyping is relative to a trait unaffected by the segregating locus. For a preliminary multiple trait analysis, controlling the false discovery rate rather than the experiment wise type‐I error allows for greater statistical power to detect true effects. In the absence of any true effects the two methods are equivalent. An example is given using granddaughter design data.

The Study of Biological Versus Statistical Variation in Multivariate Morphometrics: The Descriptive Use of Multiple Regression Analysis

Albrecht, G. H. (Department of Anatomy, School of Medicine, University of Southern California, Los Angeles, California 90033) 1979. The study of biological versus statistical variation in multivariate morphometrics: The descriptive use of multiple regression analysis. Syst. Zool. 28:338-344.-Multivariate statistical techniques (such as canonical variate and principal component analyses) are often used to ordinate or summarize morphometric data to facilitate biological interpretation of the morphological relationships under study. While the major axes of statistical variation which are derived in such analyses may have direct biological significance, there is no a priori reason that the biological and statistical determinants of morphological variation necessarily be concordant. Multiple regression provides a simple means of identifying and describing the maximum degree of relationship between (1) a variable, such as size or latitude, which is thought to have some biological relevance to the problem at hand, and (2) the set of uncorrelated variables, such as canonical or principal component variates, which represent the major axes of statistical variation and which may be thought of as a convenient, analytically efficient system of reference axes describing the multivariate data space. Of particular significance is the ability to examine the full multidimensional space and detect biological information having an angular relationship to the major axes of statistical variation. [Multiple regression analysis; multivariate analysis; morphometrics; statistical and biological variation.] Multivariate statistical techniques are often used to ordinate morphometric data so that biological parameters underlying morphological relationships among individuals or groups may be more readily discovered. Commonly used techniques include canonical variate, discriminant function, principal component, and principal coordinate analyses (see Blackith and Reyment, 1971, for definitions and examples). All have in common a primary purpose of summarizing multivariate data in a relatively few dimensions that retain the majority of information formerly dispersed among the larger array of original variables. An additional advantage shared by all is the lack of statistical correlation among the transformed variates as compared to the complex of statistical dependencies usually found among the original variables. Such a reduction in both dimensionality and correlation results in a greater probability that the investigator will be able to make biologicallv relevant statements about the morphometric relationships under study. Populations (or individuals) are often found to be ordered on the first few transformed variates of a multivariate analysis according to morphological gradients suggestive of differences in size, shape, time, function, behavior, or ecology. For example, Oxnard (1967; nine measurements of the scapula of 27 genera of primates) interpreted the first canonical variate as reflecting the extent to which the shoulder is subjected to compressive or tensile forces, the second canonical variate as reflecting relative degrees of arboreality or terrestriality, and the third canonical variate as reflecting the uniqueness of the human condition. Johnston and Selander (1971; 16 measurements of the skeleton of 33 populations of North American house sparrows) interpreted the first and second principal component variates, after regressiot analyses involving 15 environmental variables, as reflecting classic examples