Questions tagged [multivariate-regression]

Regression with more than one response (dependent) variable.

Use this tag to refer to cases where regression is used to model more than one response variable. Use when your question centers on cases with one response.

Special techniques may be used to regress more than one response variable onto a set of predictors. Examples:

More info and references:

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Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math

Are multiple and multivariate regression really different? What is a variate anyways?
Neil McGuigan
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Multivariate multiple regression in R

I have 2 dependent variables (DVs) each of whose score may be influenced by the set of 7 independent variables (IVs). DVs are continuous, while the set of IVs consists of a mix of continuous and binary coded variables. (In code below continuous…
Andrej
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Why do we need multivariate regression (as opposed to a bunch of univariate regressions)?

I just browsed through this wonderful book: Applied multivariate statistical analysis by Johnson and Wichern . The irony is, I am still not able to understand the motivation for using multivariate (regression) models instead of separate univariate…
KarthikS
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Casting a multivariate linear model as a multiple regression

Is recasting a multivariate linear regression model as a multiple linear regression entirely equivalent? I'm not referring to simply running $t$ separate regressions. I have read this in a few places (Bayesian Data Analysis -- Gelman et al., and…
bill_e
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Methods to predict multiple dependent variables

I have a situation in which I have $n$ observations, each with $p$ independent variables and $q$ dependent variables. I would like to build a model or series of models to obtain predictions of the $q$ dependent variables for a new observation. One…
rguha
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Neural network for multiple output regression

I have a dataset containing 34 input columns and 8 output columns. One way to solve the problem is to take the 34 inputs and build individual regression model for each output column. I am wondering if this problem can be solved using just one model…
sjishan
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gaussian process regression for large datasets

I've been learning about Gaussian process regression from online videos and lecture notes, my understanding of it is that if we have a dataset with $n$ points then we assume the data is sampled from an $n$-dimensional multivariate Gaussian. So my…
Iltl
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Multivariate linear regression vs. several univariate regression models

In the univariate regression settings, we try to model $$y = X\beta +noise$$ where $y \in \mathbb{R}^n$ a vector of $n$ observations and $X \in \mathbb{R}^{n \times m}$ the design matrix with $m$ predictors. The solution is $\beta_0 =…
Roy
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Multivariate linear regression with lasso in r

I'm trying to create a reduced model to predict many dependent variables (DV) (~450) that are highly correlated. My independent variables (IV) are also numerous (~2000) and highly correlated. If I use the lasso to select a reduced model for each…
kmace
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How to interpret coefficients of a multivariate mixed model in lme4 without overall intercept?

I'm trying to fit a multivariate (i.e., multiple response) mixed model in R . Aside from the ASReml-r and SabreR packages (which require external software), it seems this is only possible in MCMCglmm . In the paper that accompanies the MCMCglmm…
Chris
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T-test for regression coefficients obtained from Ridge, LASSO etc

In ordinary least squares, for example in an experimental design case, I obtain the regression coefficents by: $ \hat B = {({X^t}{X})}^{-1}X^ty$ Then, my null hypothesis for each coefficent is: $H_0 : \hat B_i = 0$ $H_1 : \hat B_i \neq 0$ Next, by…
gunakkoc
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Coupling time series information from sources with multiple spatial resolutions/scales

I have many satellite raster images available from different sensors. From these, the coarser ones have a very abundant temporal resolution. The medium resolution rasters tend to have less acquisition dates but still some degree of information is…
JEquihua
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Multivariate response regressions vs many linear models

Would anyone be willing to venture an intuitive description of the situations under which a multivariate response model is more appropriate than many linear regressions? As an example, take a randomly allocated agricultural extension program, and…
generic_user
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How to model an "order-invariant" function by neural networks

I want to approximate a multi-variable function $f(x_1,x_2,x_3,x_4,x_5,y)$ from data by neural networks, and $f$ satisfies $f(x_1,\ldots,x_5,y)=f(x_{i_1},\ldots,x_{i_5},y)$, where $(i_1,\ldots,i_5)$ is an arbitrary permutation of $(1,\ldots,5)$. How…
wwtian
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Simple, multiple, univariate, bivariate, multivariate - terminology

I do realise (some of) this has already been addressed here (e.g., Why do we need multivariate regression (as opposed to a bunch of univariate regressions)? , Explain the difference between multiple regression and multivariate regression, with…
Tilen
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