# Questions tagged [multivariate-regression]

Regression with more than one response (dependent) variable.

263 questions
Filter by
Sorted by
Tagged with
0answers
51 views

### Multivariate analysis. Hypothesis income variable increases 2 outcome variables simultaneously

I am working on an epidemic analysis on a known blood disease. My outcome variables are two, lets call them A and B. I have already two income variables lets call them c and d, that have been proved ...
1answer
699 views

### Time series Prediction as a multi-output multivariate regression for many input and output values for each lag [closed]

Recently I am working on time series prediction. My topic is related to wind turbine blades which has many sections in each blade. Now we want to predict some performance and metrics of 51 sections of ...
1answer
108 views

### Is it okay to use Multivariate Multiple Regression on correlated independent and dependent variables?

I'm confused on this one. So, I'd appreciate it if you could help me explain this a little bit. So, I have about 15 independent variables and 3 dependent variables. out of these 15 independent ...
1answer
230 views

0answers
58 views

### How to construct a regression model with two inter-dependent dependent variables?

Let's work through a concrete (if somewhat impractical) example: I'm a medical researcher who has reason to investigate a possible trend in a dataset of tissue samples from the human lung and human ...
0answers
221 views

### Comparability of multivariate vs multiple regression

I have 3 layers of data [in individual columns] indicating different characteristics of resistance of vegetation to stress across a city. I have another layer of median income for my study area. I ...
1answer
57 views

### problem with linear regression

I made a linear regression multiple model, I've got all the parameters insignificant with high p value, I get this result: The variables are time series. Rates except Polity which is natural number ...
0answers
51 views

### Detecting changes in multivariate time series

I have the following problem I am trying to solve. I am hoping to pick your brain on the possible solutions. I have (say) 10000 machines, each one spit out a number for me each day; I have collected ...
0answers
126 views

### Are multivariate probit models with the same set of explanatory variables for each outcome more efficient that piecewise probit regressions?

I understand that multivariate probit models are analogous to SUR models. In the SUR case, there's no efficiency gain by fitting a SUR model over several independent OLS regressions when the model ...
0answers
45 views

### Approaching a multivariate regression problem with multiple observations over days

Assume for every $Y_{n*1}$, there is a covariate matrix $X_{n*p}$. The goal is to predict $Y'_{n*1}$ when new $X'_{n*p}$ becomes available. What complicates the matter is that I have $k$, let's say ...
1answer
142 views

### Why do we need adjustment when applying Ljung-Box test over residuals?

Why do we need adjustment when applying Ljung-Box test over residuals? The following is from the Multivariate Time Series Analysis by R. Tsay. ...
0answers
125 views

### Can I try applying multiple linear regression when there is no correlation with independent variable

Can I still try multiple linear regression when there is no or very small(2 to 10%) correlation (Pearson) between the Independent variable(continuous) and Dependent variable(continuous). But very ...
1answer
263 views

### comparing coefficients from multivariate regression

I have a multivariate linear regression model where the predictors are concentrations of different drugs, of the same units, and the responses are the survival percentages of each different kind of ...
1answer
198 views

### Terminology for regression with more than 1 independent variable and more than 1 dependent variable?

I know that multiple regression corresponds to the case where we have 1 dependent variable and multiple independent variables. Multivariate regression, on the other hand, corresponds to the case where ...
0answers
458 views

### GLM in Matrix Notation

I would like to verify my thoughts here concerning matrix notation of generalized linear models (i.e. generalized general linear models). A classical generalized linear model is given by  Y_i = h(\...
1answer
5k views

### What's the point in neural networks for multivariate regression?

Do you have any case in which fitting a multivariate regression (so having multiple output nodes) outperforms the fitting of a single output one at a time in terms of accuracy? I ask this because ...
0answers
357 views

### What's the difference between multivariate regression and univariate regression? [duplicate]

If we have $Y$ as response variable which has more than 1 component, and we do want to do regression, then is there such a thing as multivariate regression, and if so, how those it differ from ...
1answer
724 views

### Summary of manyglm model objects running too slowly in R; can I speed them up?

I have implemented two independent multivariate abundance regressions, both of which use the manyglm function in the mvabund ...
1answer
268 views

### A reasonable multivariate regression error metric

How would you compare error metrics of a multiple output regression? Normalised mean square error for each variable? How about for the overall performance of the model, would you just take the mean ...
2answers
6k views

### 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 ...
4answers
18k views

### 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 ...
6answers
16k views

### 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 (...
0answers
37 views

### Can two multicollinear variables affect a third IV that is not correlated with the two?

I have three dependent variables, $X_1$ (my 'main' independent variable), $X_2$ $X_3$ where When I test $X_1$ against the response variable y in a bivariate regression, the results are not ...
1answer
6k views

### 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 ...
1answer
471 views

0answers
108 views

### Multivariate Regression equivalent to Multinomial Regression (aggregated variables)?

Context (optional) In genetics one often uses data coming from SNP (Single Nucleotide Polymorphismes) which are genetic markers of which several (usually 2) versions (a.ka. alleles) exists in a given ...
1answer
271 views

### Choice of algorithm for fitting Multivariate Covariance Generalized Linear Models

I am using the mcglm function in R to fit a Multivariate Covariance Generalized Linear Model. Although the help for the ...
1answer
65 views

### Clarification on multivariate regression

In (what I call) standard regression, we have a problem of the form $y=f(a,b,c,d)$. Is it correct to say that multivariate regression is the problem of the form $g(x,y,z)=f(a,b,c,d)$? In case this is ...
0answers
628 views

### Multiresponse Poisson regression in R

Can I apply generalised linear regression to a multiresponse setting? I mean the regression $Y = \beta X + \epsilon$ where $\beta$ is a parameter matrix and $Y$ is a response vector, in my case ...
0answers
33 views

### Multivariate regression with relationship between outputs

Can anyone suggest some machine learning algorithms for the problem of multi-output regression when the outputs depend both on input vector and other outputs. More specifically, given input vector x ...