Principles of Multivariate Analysis: A User's Perspective

By James R. Schott. 2002

796 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/900d84bebdb5d76be7737ae765a07f162128598d

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Citations in the corpus

Information Transmission in Irrigation Technology Adoption and Diffusion: Social Learning, Extension Services, and Spatial Effects (2014, neutral): “The heaviest loadings for factor 2 come from the variables related to the 9 For more details about factor analysis, the reader is referred to Krzanowski (2000).”
Visualization of learning in multilayer perceptron networks using principal component analysis (2003, methodology): “Furthermore, since many existing techniques for multivariate data visualization have proven effective for data of dimensionality up to about ten [15], if the data can be effectively reduced to this kind of dimensionality, these techniques could be used following PCA.”
Sample Size Guideline for Correlation Analysis (2016, background): “Unlike correlation test, both variables may correlate with each other but it may not a relationship of predictor and outcome (Krzanowsk, 1988; Rodriguez, 1982).”
Proposed minimum reporting standards for data analysis in metabolomics (2007, background): “There are several excellent reviews on current multivariate techniques (Beebe et al. 1998; Chatfield and Collins 1980; Duda et al. 2001; Everitt 1993; Krzanowski 1988; Lavine 1998; Manly 1994; Martens and Næs 1989; Massart et al. 1997).”
Measuring convergence of National Accounting Standards with International Financial Reporting Standards (2005, methodology): “…harmonisation using Jaccard’s coefficients Of the various association coefficients available11, we proposeJaccard’s coefficients because they avoid contributing characteristics that are simultaneously absent in the computation of similarity between two sets of binary observations (Kranowski, 2000).”
Testing of null hypotheses in exploratory community analyses: similarity profiles and biota-environment linkage (2008, methodology): “linear for canonical correlation (Krzanowski, 2000) and unimodal Gaussian in canonical correspondence (ter Braak, 1986), and both of these latter techniques employ linear, additive combinations of the submitted environmental variables.”
Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease (2011, methodology): “Linear discriminant analysis (LDA) is a widely used technique to find a linear combination of features to best separate several classes [52].”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, methodology): “…as Principal Components Analyisis (PCA, where confidence is estimated using knowledge of class membership ), Canonical Variates Analysis (CVA) (see (Krzanowski, 1988)), sometimes referred to as Discriminant Function Analysis (DFA) (Manly, 1994) and Discriminant Partial Least Squares (D-PLS, or…”
Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, methodology): “As defined in [41], the significant eigenvectors of”
Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation (2003, methodology): “Although this weakness of imputation methods is certainly a critical problem, the strategy suggested by Krzanowski (1988) has an even more important weakness: it is not suitable at all for compositional data.”
Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach? (2012, background): “CCA enables one to identify the dominant linear modes of covariability between the setsN and M (e.g., Krzanowski, 1988; Ouarda et al., 2001).”
Comparison of multivariate classifiers and response normalizations for pattern-information fMRI (2010, neutral): “The orientation and shape of the multinormal distribution of the pattern estimates is accurately characterized by the covariance of the errors ( Krzanowski , 1988 ) .”
UniFrac – An online tool for comparing microbial community diversity in a phylogenetic context (2006, methodology): “UniFrac can compare many communities simultaneously using hierarchical clustering with the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) algorithm [25] and PCA (Principal Coordinates Analysis)[22].”
Dynamic factor analysis to estimate common trends in fisheries time series (2003, background): “3 is the same as in factor analysis (Krzanowski 1988).”
Choice of Distance Matrices in Cluster Analysis: Defining Regions (2001, background): “…a zero distance without the points being coincident, and also that a projection of the n points into lower-dimensional space may be problematic (Krzanowski 1988). a. Euclidean distance Geometrically the Euclidean distance between two points is the shortest possible distance between the two…”