Use this tag to refer to cases where regression is used to model >1 response variable. Use multiple-regression when your question centers on cases with 1 response and >1 covariate.

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Which regression analysis should I use for ranked dependent variables and proportional independent variables?

I am analysing the effect of deprivation on breastfeeding and am wondering which type of regression analysis I should use. It is area level data. Deprivation data is available as a score from 0 - ...
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0answers
8 views

References suggested for multivariate analysis of several similar time series

I have a time series dataset that reports the hourly page views and social media shares of online news stories. What I hope to obtain is the relationship between the two variables. I would imagine ...
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27 views

Using a PCA to reduce response variables or multivariate multiple regression?

Does it make sense to use a PCA (principal component analysis) on a set of response Y variables and then conduct a multiple regression, or carry out a multivariate multiple regression all response ...
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1answer
45 views

How to do multivariate regression in R?

I need to preform a multivariate normal regression in R. The question is: Let $Y_1$, $Y_2$, and $Y_3$ follows multivariate normal distribution. What is the conditional of $Y_3$ given ...
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1answer
70 views

Lag order for Granger Causality Test

Suppose I'm considering several independent variables for possible inclusion in an ARIMAX model I'm developing. Before fitting different variables, I'd like to screen out variables that exhibit ...
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0answers
18 views

What are some multivariate models with feature interactions

I have dependent variable matrix $Y_{i,j}$ and feature matrix $X_{i,k}$. My objective is to predict each element of the vector $[y_{i,0},...,y_{i,J}]$ by using new observations of the features, ...
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8 views

Covariance matrix specification in multivariate probit

Im having trouble with a multivariate probit model with partial observability/sample selection (written in GAUSS). In this model there is a probit at each of multiple stages, and only one of the two ...
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0answers
11 views

Marginally Uncorrelated Responses in Multivariate Regression

Consider the multivariate regression with random predictors as described in Izenman's Modern Multivariate Statistical Techniques, that is, $X : r \times 1$ and $Y: s \times 1$ are jointly normally ...
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23 views

Regression by multiple dependent variables with constraints & feature selection

I have a data set of 1000 records. Each record has three dependent variables $y_1, y_2, y_3$ and 100 independent variables $x_1,...,x_{100}$, where the dependent variable $y_i$ satisfies: $0\le y_i ...
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47 views

Alternative to MANOVA when group covariance matrices are heterogeneous?

I'm running into problems meeting some of the assumptions of MANOVA, namely homogeneous group covariance matrices and normality. I'm looking for an alternative approach where assumptions are not ...
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0answers
18 views

Parameter covariance matrix for a multivariate (matrix-Y) logit model

I've got a partially-observed unidirectional network. Nodes can be linked (0/1) in one of many ways. For now, lets call them $y_1$ and $y_2$. The unit of analysis is the potential network link ...
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2answers
284 views

Multivariate regression with weighted least squares in python?

I have a multivariate regression problem that I need to solve using the weighted least squares method. In particular, I have a dataset X which is a 2D array. It ...
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1answer
375 views

What is the difference between multiple regression & mutivariate regression?

I have data on GDP growth as a dependent variable and growth in main production sectors of Pakistan such as mining, electricity, communication, manufacturing and electricity. I am supposed to run a ...
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0answers
17 views

Multi variate regression in SPSS… How?

I want to conduct multivariate regression (Regression on more than 1 dependent variables) in SPSS. But I couldn't find any option. I am using SPSS 22. All I could find is linear regression option but ...
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32 views

$R^2$ in multivariate regression

I'm trying to determine how the population $R^2$ value is defined in the multivariate regression model where we have $Y_i = \mu_y + B^\prime(X_i - \mu_x) + err$ Where $Y_i \in \mathbb{R}^q$ and ...
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0answers
41 views

extract residuals from adonis function in vegan

I am using the adonis function in the vegan package to determine effects of different environmental factors in forest plant community composition in different regions. I would like to first use adonis ...
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1answer
111 views

What type of multivariate linear regression is this?

I'm trying to reproduce a result from a book (see bottom) and it doesn't work. I would like to do some further readings about this method but he doesn't specifically give the method other than a ...
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0answers
46 views

Self study problem: Predicting performance using multivariate linear regression

I'm trying to improve my statistics knowledge using football(soccer) results. So, this is a self study problem. You don't have to provide a complete solution. Pointing me in the right direction is ...
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0answers
21 views

Controlling for respondents' propensity to rate high/low or moderate/extreme

I am doing a simple linear regression to test the relationship between voters' self-reported policy stands and their approval ratings for two hypothetical presidential candidates in a simulated ...
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0answers
48 views

causal inference with correlated multivariate outcomes

I've been struggling with how to think about the causal estimate of a program on two outcomes, when one of the two outcomes affects the other outcome. It seems sort of like simultaneous equations, ...
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0answers
88 views

Comparing 2D heat maps of observed data to 2D model predictions

From "How to ask a statistics question": PROBLEM you are trying to solve: Given two-dimensional heat maps of responses (DV), choose the 2D model (also a heat map, but can have different ranges of ...
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0answers
121 views

Fitting a multilevel multivariate model in R with `glmer`

Background I have a large dataset that contains three binary outcomes for individuals belonging to groups. I am interested in jointly modeling these binary outcomes because I have reason to believe ...
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1answer
133 views

Finding the best linear model for each response variable in multivariate multiple regression using R

I don't know if a similar problem has been asked before so if it has been, please provide me a link to the related/duplicate questions. I am sorry if I seem to be asking too much. But I really like to ...
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0answers
61 views

Prediction Intervals for General Linear Model

How do I derive prediction intervals for a general linear model? My general linear model written in matrix form is, $$ \mathbf{Y} = \mathbf{X} \mathbf{B} + \mathbf{R}$$ with each of the rows of ...
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0answers
34 views

Modeling consumer demand for a multi-tiered product that is subject to cannibalization?

I'm looking for research/white papers on demand estimation for goods/services that are offered on a tiered-basis with varying prices (i.e. more complex the good/service, higher the price.) I'm not ...
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2answers
368 views

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 ...
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2answers
143 views

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 ...
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36 views

how to explain the anova object for a multivariate linear regression?

I performed a multivariate linear regression such that: fit<-lm(as.matrix(y)~mwtkg+mbmi+mage,data=x) where $y$ is a $500 \times 26$ multivariate outcomes. ...
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1answer
100 views

Why signs of coefficients change when doing multivariate vs. univariate logit regression?

Excuse my dumb question, but I did an univariate logistic regression where the sign of the coefficient of my variable was negative (and it was significant). Once I have input it into a multivariate ...
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0answers
80 views

Is it ever appropriate to put dependent variables in a model when they calculate the independent variable directly?

Is it ever appropriate to fit a multivariable regression model with dependent variables that directly calculate the independent variable? For example, I know that fitting a model to predict BMI by ...
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0answers
118 views

General linear hypothesis testing in repeated-measures multivariate linear model

In a multivariate linear model $Y = XB + U$, where within-subject (or repeated-measures) factors are coded as simultaneous response variables in $Y$, a general linear hypothesis can be formulated as ...
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0answers
44 views

Alternative denominator for F test in nested model comparison?

In comparing two nested multivariate models, the usual F test statistic is in this form: $\frac{(SSR_2 - SSR_1)/(p_2 - p_1)}{MSE_2} \sim F_{p_2-p_1, N - p_2}$ assuming model 1 is nested within model ...
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60 views

Parameter space exploration

I do realise this question is quite specific and practical, but I seek for some general help which helps me progress further in my analysis. Let $y(\boldsymbol{x})\in\mathbb{R}$ be the function I'd ...
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1answer
90 views

Definition of multivariate regression coefficient

I know that the regression coefficient of $Y$ and $X$ is defined as $$\beta(Y,X) = \frac{\mathrm{Cov}(X,Y)}{\mathrm{Var}(X)}$$ Does this expression also hold in a multivariate regression with $Y$, ...
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1answer
124 views

Seemingly unrelated regression and multivariate Regression

I have a problem with an linear regression model in which I have 2 dependent variables. Both are highly correlated with each other and should both be explained by the linear model ...
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1answer
155 views

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 ...
2
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1answer
143 views

Breakpoint for bivariate data

The breakpoint(s) estimation approach implemented in the strucchange package (Zeilei & al) seems to work very well (based on my little experience with this package on real case studies). Is ...
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1answer
137 views

Is it normal to obtain better (smaller) P values in multivariate analysis compared to bivariate one?

If a multivariate design controls for other predictors when calculating the effect of a predictor, shouldn't it give paler P values (less significant ones, or less vivid odds ratios)? I am seeing ...
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1answer
132 views

Singular Value Decomposition and PCR

Can anyone guide me to understand the relation between Singular Value Decomposition (SVD) and Principle Component Regression (PCR)? I know that we can construct the principle components (PCs) using ...
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1answer
67 views

How to analyze data with more than one associated categorical dependent variables?

I have some dependent variables related to the growth of a company having categories like (e.g. for variables indicating net profit, financial turnover etc.) (1) decreasing, (2) stable, (3) ...
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52 views

Regression with some associated ordinal dependent variables [duplicate]

I have some associated categorical dependent variables that are ordinal in nature (with 4 or 5 categories). If I want to see the effect of a set of independent variables (which can be both continuous ...
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1answer
329 views

Multivariate regression and use of proportion type variables as DV in it

I have two very important things to know. Can anyone help? Question-1 If I have three categorical dependent variables and a continuous dependent variable, which are correlated (or associated), then ...
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2answers
165 views

Multivariate Weighted Linear Regression

Very simple. I am looking for a package that does Multivariate Linear Regression with weights on the observations. Does anyone know of a package that does this? I am shocked that I have not been ...
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0answers
131 views

Hypothesis testing to determine cluster outliers

I have a cluster of $p$-dimensional data from $n$ samples which is assumed to be normally distributed as a multivariate Gaussian with sample mean ${\bar{\mu}}$ and sample covariance matrix ...
3
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1answer
235 views

A measure of overall variance from multivariate Gaussian

I am performing some regression task, where I try to discover the underlying multivariate Gaussians from a set of $n$, $p$-dimensional vectors. For example, given a split of the set into $S_i$ and ...
2
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1answer
93 views

Predict 2 responses from two co-variates

I'm not quite sure how I should fit a model that has two responses. The data consists of target (x,y) co-ordinates and actual (x,y) co-ordinates. I would like to fit a model to predict a new set of ...
3
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1answer
495 views

Bayesian approach and least-squares approach to multivariate regression with structural design

Assume for example a trivariate Gaussian model: $$ {\boldsymbol Y}_1, \ldots, {\boldsymbol Y}_n \sim_{\text{iid}} {\cal N}_3\left({\boldsymbol \mu}, \Sigma\right) \quad (*) $$ with ${\boldsymbol \mu} ...
2
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1answer
329 views

Standard deviation of a particular dimension in a multivariate Gaussian distribution

I have a set (cluster) of vectors in dimension d. From this I have calculated the sample mean and covariance matrix ( I make the assumption that they are from a multivariate Gaussian). My question ...
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126 views

Variable selection with restricted cubic splines

Is there any function in R for doing variable selection (backward elimination) in a multiple logistic regression using restricted cubic splines like mvrs procedure for STATA?
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3answers
217 views

Data set to experiment with multivariate count regression

I have a model for predicting multiple count variables (multivariate count regression) given some covariates. Are there any publicly available datasets I could experiment with?