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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7
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1answer
333 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 ...
0
votes
0answers
14 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 ...
1
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0answers
25 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 ...
0
votes
0answers
23 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 ...
2
votes
1answer
93 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 ...
0
votes
0answers
38 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 ...
1
vote
0answers
14 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 ...
1
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0answers
39 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, ...
0
votes
0answers
32 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 ...
0
votes
0answers
80 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 ...
1
vote
1answer
63 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 ...
1
vote
0answers
35 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 ...
0
votes
0answers
25 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 ...
13
votes
2answers
315 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 ...
8
votes
2answers
128 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 ...
1
vote
0answers
32 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. ...
3
votes
1answer
77 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 ...
2
votes
0answers
72 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 ...
1
vote
0answers
99 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 ...
0
votes
0answers
31 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 ...
3
votes
0answers
57 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 ...
0
votes
1answer
83 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 $Y$, ...
3
votes
1answer
99 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 ...
0
votes
0answers
72 views

Epanechnikov multivariate kernel

I have set of data which consists of vectors of size 1x5, each representing a pixel: [x,y,r,g,b], x and y are the position:0 <= x <= 720, 0 <= y <= 480. r,g,b are the colors of the pixel: ...
2
votes
1answer
138 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
votes
1answer
128 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 ...
1
vote
1answer
106 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 ...
-1
votes
1answer
113 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 ...
0
votes
1answer
62 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) ...
1
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0answers
50 views

Regression with some associated ordinal dependent variables

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 ...
1
vote
1answer
280 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 ...
1
vote
2answers
149 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 ...
1
vote
0answers
115 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
votes
1answer
218 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
votes
1answer
91 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
votes
1answer
402 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
votes
1answer
293 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 ...
2
votes
0answers
118 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?
1
vote
3answers
185 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?
1
vote
0answers
200 views

Bootstrap or jack-knife for crossvalidation of predictive model?

Is a bootstrap or jack-knife method better for crossvalidation of a multivariate logistic regression based predictive model?
0
votes
1answer
407 views

Multivariate ARIMA with regression

I have a dataset covering daily data for 3 years (3x365 rows) for multiple attributes TotalPhoneCall (main attribute that I want to predict), Christmas day, weekend, weekday, Easter, 4th_july, ...
3
votes
0answers
163 views

Goodness-of-fit of model derived from multivariate logistic regression

I have a question regarding the relationship between the value of Chi square and df in determining the goodness-of-fit in a model derived from multivariate logistic regression. If the N= 290, Chi ...
2
votes
2answers
132 views

Multivariate regression estimation when the variables' variances are known a priori / sourced seperately

I'm looking to use a multivariate regression for prediction, but making use of (possibly) superior estimates of variance for both the independent and extraneous variables. My approach is to ...
1
vote
1answer
1k views

How to create a composite variable to use as a response variable?

I am a student doing my master's thesis and I have a question regarding my study. I am working with country data for 25 countries and I am looking into cultural values, attitudes and ...
5
votes
0answers
416 views

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 ...
3
votes
0answers
112 views

Best practice when analysing placebo-controlled within-subject designs (cross-over trial)

I would like to analyse data from a placebo-controlled within-subject design. For every subject a numeric outcome variable (Y) was measured twice - once following administration of a drug and once ...
6
votes
1answer
159 views

Multivariate analysis techniques for fMRI data

I am doing a project in which I need to predict fMRI activation values for each voxel of the brain. The voxels are approximately 20,000 and I have 300 examples with 25 features in each. Thus there are ...
3
votes
2answers
208 views

PCA on independent but non-identically distributed multinormal data

In the empirical phase diagram approach the data are given by a set ${\boldsymbol X}$ of input (controlled) variables (such as pH and temperature) and a set ${\boldsymbol Y}$ of output variables. The ...
3
votes
1answer
323 views

Regression analysis with factor scores as explanatory variable

I intend to use factor scores as derived from exploratory factor analysis in subsequent multivariate regression analysis, as an explanatory variable. I've read in multiple books/papers that ...
0
votes
1answer
210 views

Testing threshold cointegration in vector error-correction models

In Hansen and Seo's paper on Testing two regime threshold cointegration in VECM (J. Econometrics, 2002; 110:293), the authors proposed a test based on Lagrange Multiplier for testing treshold in ...