(a) Perform a principal components analysis of this dataset on both the covariance and correlation matrices, reporting both estimated variances and loadings. Why are the results different? Why does PCA on the correlation matrix make more sense for this data? The remaining parts assume that you use PCA on the correlation matrix. Solution: The results are different because PCA is not scale invari...

General ideas of robust statistics, and specifically robust statistical methods for calibration and dimension reduction are discussed. The emphasis is on analyzing high-dimensional data. The discussed methods are applied using the packages chemometrics and rrcov of the statistical software environment R. It is demonstrated how the functions can be applied to real high-dimensional data from chem...

There has been increasing interest in estimating a multivariate regression function subject to shape restrictions, such as nonnegativity, isotonicity, convexity and concavity. The estimation of such shape-restricted regression curves is more challenging for multivariate predictors, especially for functions with compact support. Most of the currently available statistical estimation methods for ...

this experiment was conducted to investigate the genetic diversity, relationship between morphological, agronomic and qualitative traits and to identify components of forage yield using some multivariate statistical methods in local sainfoin populations. the experiment was conducted as randomized complete block design with three replications in agricultural and natural resources research center...

Grouping Bread w heat Cultivars based on Agronomic Characteristics using Multivariate Statistical Methods

We propose a nonconvex estimator for joint multivariate regression and precision matrix estimation in the high dimensional regime, under sparsity constraints. A gradient descent algorithm with hard thresholding is developed to solve the nonconvex estimator, and it attains a linear rate of convergence to the true regression coefficients and precision matrix simultaneously, up to the statistical ...

Multivariate statistical techniques are used extensively in metabolomics studies, ranging from biomarker selection to model building and validation. Two model independent variable selection techniques, principal component analysis and two sample t-tests are discussed in this chapter, as well as classification and regression models and model related variable selection techniques, including parti...

The purpose of the study is to analyze affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), one data mining methods. Modeling, regression-based methods, quite popular for social sciences due various advantages it offers; however, requires very intensive assumptions. ...