[PDF] Top 20 High-Dimensional Linear and Functional Analysis of Multivariate Grapevine Data
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High-Dimensional Linear and Functional Analysis of Multivariate Grapevine Data
... the data, it holds out the possibility of more efficient & rapid operation of the data ...a linear model with a response variable Y, depended on p explanatory variable X ∊ ℝ 𝑝𝑝 ...a data ... See full document
134
Dimensionality Reduction of High Dimensional Highly Correlated Multivariate Grapevine Dataset
... reflectance data, for the reflective 330 - 2510 nm wavelength re- gion (986 total spectral bands), to assess vineyard nutrient status; this constitutes a high dimensional dataset with a covariance ... See full document
16
Koch I. Analysis of Multivariate and High-Dimensional Data 2013
... about data in many – and sometimes very many – variables and about analysing such ...classical multivariate methods with contemporary methods suitable for high-dimensional data and to ... See full document
532
Partially Linear Functional Additive Models for Multivariate Functional Data
... partially linear functional additive models (PLFAM) that predicts a scalar response by both parametric effects of a multivariate predictor and nonparametric effects of a multivariate ... See full document
41
Multivariate Correlation Analysis for Supervised Feature Selection in High-Dimensional Data
... the data from several sensors (continuous values), sta- tus bits, gear-position (categorical values) and calculations forms a mixed dataset with a large number of ... See full document
191
Model-based methods for high-dimensional multivariate analysis
... the multivariate response linear regression model in high-dimensional settings without relying on the popular assumption that the regression coefficient matrix is sparse or has small Frobenius ... See full document
102
Adopting Star Plot for Visualization of High Dimensional Multivariate Data
... every data item are represented on uniformly spaced axes radiating from a single fixed origin point on a 2D plane and connected together to get a Star-shaped ...a data set as a multi-plot consisting of as ... See full document
44
Effective Linear Discriminant Analysis for High Dimensional, Low Sample Size Data
... Three classifiers, the nearest centroid, 1-nearest neighbor and support vector machine, have been applied to the re- duced data for classification. Figure 4 plots the average test error rate as a function of ... See full document
6
Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data
... for linear regression were developed over the last ten years to reduce the weakness of ordinary least squares (OLS) regression with respect to prediction ...And, high dimensional data are ... See full document
8
Linear Methods for Joint Analysis of Multivariate Genomics Data
... Big Data has enabled sophisticated analysis of the human genome in unprece- dented ...multiple data types and multiple functional units at the molecular level of the ...these data offer ... See full document
160
Covariance-based Clustering in Multivariate and Functional Data Analysis
... of data whose difference lies in their variance-covariance structures rather than in their ...of functional data, yet it can be of interest also in other contexts, such as for multivariate ... See full document
21
Random Projection for Fast and Efficient Multivariate Correlation Analysis of High-Dimensional Data: A New Approach
... for high-dimensional genotype-phenotype association ...combined analysis of SNP and neuroimaging data in a systematic comparison of multivariate techniques ( Grellmann et ... See full document
19
On some classification methods for high dimensional and functional data
... In Chapter two, we propose nonparametric methods for classifying d-dimensional ob- servations based on multivariate rank. They are minimal rank classifier(RC) and affine invariant version of minimal rank ... See full document
168
Sparse Linear Discriminant Analysis with Applications to High Dimensional Low Sample Size Data
... the data? Fisher (1936) proposed a classic approach: Find the projection direction such that for the projected data, the between-class variance is maximized relative to the within-class ...the data ... See full document
13
Sparse multivariate models for pattern detection in high-dimensional biological data
... significant linear relationship be- tween the gene’s methylation profile and its expression value from the same ...methylation data is available, we found that ...a linear fit (Bonferroni correction, ... See full document
241
Analysis Challenges for High Dimensional Data
... traditional linear regression as an example, we first fit a linear model with all variables included, then preserve the significant ones after drawing the hypothesis test- ings to all the predictors, and ... See full document
153
Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models
... power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified ...univariate analysis ... See full document
17
Methods and Applications of Multivariate Pattern Analysis in Functional MRI Data Analysis.
... analyze data and recognize patterns [31, 32, ...test data. Also, when a linear kernel is used, they allow the possibility of generating discrimination maps so that they can be visualized in the ... See full document
124
Sparse Principal Component Analysis for High Dimensional Multivariate Time Series
... twitter data, the exis- tence of non-dependence is easily ...dent data (Skinner et al., 1986), no such result exists for high dimensional ...dependent data analysis in ... See full document
9
Learning from High-Dimensional Multivariate Signals.
... of high-throughput data collection techniques, low-dimensional matrix factorizations have become an essential tool for pre-processing, interpreting or compressing high-dimensional ... See full document
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