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L1-norm principal component analysis (L1-PCA) is a general method for multivariate data analysis. [1] L1-PCA is often preferred over standard L2-norm principal component analysis (PCA) when the analyzed data may contain outliers (faulty values or corruptions), as it is believed to be robust .
Many other free to use programs were designed specifically for particular functions, like factor analysis, power analysis in sample size calculations, classification and regression trees, or analysis of missing data. Many of the free to use packages are fairly easy to learn, using menu systems. Many others are command-driven.
Here i represents the equation number, r = 1, …, R is the individual observation, and we are taking the transpose of the column vector. The number of observations R is assumed to be large, so that in the analysis we take R → , whereas the number of equations m remains fixed.
[Note 1] Descriptive statistics Nonparametric statistics Quality control Survival analysis Data processing Base stat. [Note 2] Normality tests [Note 3] CTA [Note 4] Nonparametric comparison, ANOVA: Cluster analysis Discriminant analysis BDP [Note 5] Ext. [Note 6]
Stata (/ ˈ s t eɪ t ə /, [2] STAY-ta, alternatively / ˈ s t æ t ə /, occasionally stylized as STATA [3] [4]) is a general-purpose statistical software package developed by StataCorp for data manipulation, visualization, statistics, and automated reporting.
DAP – free replacement for SAS; Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) a software framework for developing data mining algorithms in Java; Epi Info – statistical software for epidemiology developed by Centers for Disease Control and Prevention (CDC). Apache 2 licensed [1]
In the field of multivariate statistics, kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space .
This is the aim of multiple factor analysis which balances the different issues (i.e. the different groups of variables) within a global analysis and provides, beyond the classical results of factorial analysis (mainly graphics of individuals and of categories), several results (indicators and graphics) specific of the group structure.