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Everything about Multivariate random variable
Random variable with multiple component dimensions
In probability and statistics, a multivariate random variable or random vector is a list or vector of mathematical variables each of whose value is unknown, either because the value has not yet occurred or because there is imperfect knowledge of its value. The individual variables in a random vector are grouped together because they are all part of a single mathematical system — often they represent different properties of an individual statistical unit. For example, while a given person has a specific age, height and weight, the representation of these features of an unspecified person from within a group would be a random vector. Normally each element of a random vector is a real number. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Multivariate random variableA 5-minute orientation
- Skim a recent paper: Applications of random forest in multivariable response surface for short-term load forecastingInfluential work since 2022
- Pick up a classic book: Statistical ComputingThe most-reprinted book on the subject
- Read the landmark paper: Bounds on the Expectation of a Convex Function of a Multivariate Random VariableCited 108 times
- Go sideways: Cross-covariance matrixA closely related topic
- Go sideways: Covariance matrixA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Bounds on the Expectation of a Convex Function of a Multivariate Random Variable
Random correlation in variable selection for multivariate calibration with a genetic algorithm
An efficient variable selection method based on random frog for the multivariate calibration of NIR spectra
Identification of parameters by the distribution of the maximum random variable: The general multivariate normal case
Flexible Modeling of Variable Asymmetries in Cross-Covariance Functions for Multivariate Random Fields
Variable Importance Measures for Multivariate Random Forests
Variable selection in multivariate linear regression with random predictors
New Model for Multivariate Non-Gaussian Random Fields Involving between-Variable Nonlinear Correlation
🆕 Influential since 2022
Applications of random forest in multivariable response surface for short-term load forecasting
Finding PageRank (PR) Using Stochastic Matrix and Multivariate Random Variable
An importance sampling for a function of a multivariate random variable
Multivariate Asymmetric Distributions of Copula Related Random Variables
📚 Books worth reading
Statistical ComputingWilliam J. Kennedy, James E. Gentle · 2021- 📕Understanding RandomnessSalsburg · 2017
Stochastic processesM. M. Rao · 1995
M-StatisticsEugene Demidenko · 2023
Multivariate Statistical Modeling and Data AnalysisH. Bozdogan, Arjun K. Gupta · 2011
Categorical longitudinal dataJacques A. Hagenaars · 1990
Limit Distributions for Sums of Independent Random VectorsMark M. Meerschaert, Hans-Peter Scheffler · 2001
Statistical modeling, analysis and management of fuzzy dataCarlo Bertoluzza · 2002
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Cross-covariance matrix In probability theory and statistics, a cross-covariance matrix is a matrix whose element in the i, j position is the covariance between the i-th element…
- Covariance matrix In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance…
- Distance correlation In statistics and in probability theory, distance correlation is a measure of dependence between two paired random vectors of arbitrary, not necessarily…
- Cross-correlation matrix The cross-correlation matrix of two random vectors is a matrix containing as elements the cross-correlations of all pairs of elements of the random vectors…
- Uncorrelatedness In probability theory and statistics, two real-valued random variables, X, Y, are said to be uncorrelated if their covariance, cov[X,Y]=E[XY]−E[X]E[Y]…
- Covariance In probability theory and statistics, covariance is a measure of the joint variability of two random variables. The sign of the covariance shows the tendency…
- Comonotonicity In probability theory, comonotonicity mainly refers to the perfect positive dependence between the components of a random vector, essentially saying that…
- Multivariate normal distribution In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization…