Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data...

In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents....

Component analysis may refer to one of several topics in statistics: Principal component analysis, a technique that converts a set of observations of...

Component analysis is the analysis of two or more independent variables which comprise a treatment modality. It is also known as a dismantling study. The...

field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques...

Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA) that is used to analyze M-way arrays,...

Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e.,...

In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical...

the components of a particular chemical compound (qualitative analysis), to identify the proportions of components in a mixture (quantitative analysis),...

In statistics, path analysis is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple...

Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which works...

In statistics, kernel-independent component analysis (kernel ICA) is an efficient algorithm for independent component analysis which estimates source...

LDA method. LDA is also closely related to principal component analysis (PCA) and factor analysis in that they both look for linear combinations of variables...

data analysis, cluster analysis, inductive data analysis, correspondence analysis, multiple correspondence analysis, principal components analysis and...

Following Kolmogorov's work in the 1950s, advanced statistics uses approximation theory and functional analysis to quantify the error of approximation. In this...

Directional component analysis (DCA) is a statistical method used in climate science for identifying representative patterns of variability in space-time...

In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element...

In statistics, multiple correspondence analysis (MCA) is a data analysis technique for nominal categorical data, used to detect and represent underlying...

nonparametric statistics. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For...

analysis of variance to data analysis was published in 1921, Studies in Crop Variation I. This divided the variation of a time series into components...

techniques that are commonly used in statistics include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure...

Statistics (from German: Statistik, orig. "description of a state, a country") is the discipline that concerns the collection, organization, analysis...

Fisher discriminant analysis Kernel methods Kernel principal component analysis Kernel regression Kernel smoother Kernel (statistics) Khmaladze transformation...

Spectral estimation, in statistics and signal processing, an algorithm that estimates the strength of different frequency components (the power spectrum)...

Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using this...

also interchangeable with the geographically weighted Principal components analysis in geophysics. The i th basis function is chosen to be orthogonal...

analyze order statistics of random samples from a continuous distribution, the cumulative distribution function is used to reduce the analysis to the case...

Bayesian analysis for outlier problems, variance components, linear models and multivariate statistics. Theory of Probability Author: Bruno de Finetti...

component analysis (L1-PCA) is a general method for multivariate data analysis. L1-PCA is often preferred over standard L2-norm principal component analysis...

overview of and topical guide to statistics: Statistics is a field of inquiry that studies the collection, analysis, interpretation, and presentation...