Questions tagged [multivariate-regression]

Regression with more than one response (dependent) variable.

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Fitting a multivariate linear regression with different residual variance for each outcome (using a mixed effects model in R)

In a small simulation, I am fitting a multivariate normal model to predict two outcomes Y1 and Y2, while also modelling the covariance between them. This can be done through a mixed effects model (...
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34 views

Multivariate quantile regression group lasso in R

I'm trying to fit a multivariate sparse quantile regression model with group lasso. The regression is multivariate as there are several dependent variables, $Y=(y_1,\dots,y_k)$. The selected $X$s must ...
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Multivariate Regression with Unknown Number of Responses

I would like to perform a regression task in which the number of response variables differ for each observation. As a simple example, this could be taking a greyscale picture and aiming to determine a ...
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33 views

Multivariate Multiple Regressions

I have 24 multiple regression equations (24 dependent series and 24 independent series) and want the coefficients to be equal. How do I combine them? I allow for correlation of errors and tried SUR, ...
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Does multivariate multiple regression take into account correlated outcomes? [duplicate]

Thank you so much for your time. I am running an analysis where I explore the association of the same predictors across multiple outcomes (these outcomes are correlated). My understanding is that when ...
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Investigate correlations in dependent variables: interpreting EFA

Background I have a dataset that includes 5 dependent variables (different genes expression levels, all continuous) and independent variables (a categorical variable -treatment-, a few covariates and ...
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Is multivariate linear regression appropriate with a fully repeated measures design

I am trying to set up a multivariate linear regression model but am having difficulty figuring out as to whether it is appropriate for my dataset. For a group of participants, I have 3 response ...
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58 views

Autoencoders For Multivariate Time-series Anomaly Detection

I have a multivariate time series of size (1e6, 15) and would like to fit a LSTM autoencoder. I prepare data with multivariate rolling windows (one step rolling) where each sample has (1, 5, 15) ...
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Multivariate Regression Tree

I need to build a Multivariate Regression Tree. Looking at Scikit-learn's Multi-output Decision Tree Regressor it seems that what they do is define as many regressor as there are dependant variables. ...
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R-squared fot multivariate regression

I have an output of recalibrated VARX model, which means for each 500 points of my data set I have trained a model and predicted the next month values using that ...
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44 views

D-optimal design with nuisance parameters

I am a mechanical engineer trying to develope an optimal design of experiments in a problem with nuisance parameters. I would like to calculate the parameters $\mathbf{d}$ to optimally estimate ...
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Hyperparameter Tuning with Keras / Tensorflow for multivariate time series regression

The model I am training a dense feed-forward NN using the Keras API on Tensorflow. Each sample of the training set defines $\mathbf{X_t}$ and $\mathbf{Y_t}$ of an observed time period $t\in T$. Input ...
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Sas linear regression deciding on subgroups

I want to model the Total Volume as a function of the two variables Units and Price. I want to find out whether to model the total volume on the category as a whole or whether to model total volume by ...
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Formula for estimates of multivariate linear regression

Does anyone have links to a source which gives the mathematics behind how coefficients, standard error's and covariance structures are estimated in multivariate linear regression? There are plenty of ...
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What type of statistical test should I perform?

What statistical technique would I need to carry out if I want to see if there is a significant differences in the number of units purchased when classifying observations based the variables Age, ...
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28 views

Confidence Intervals for the coefficients of a Multiple Multivariate Regression

I have a functional response [ y(t) ] that I have discretized on a grid of q points, and 3 scalar regressors (x1,x2,x3). I have replaced the response function with an nxq matrix Y and this lead me to ...
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Determine the proper level of heteroskedasticity

My dependent variable is house prices and I need to understand whether the level of heteroskedasticity in this plot of residuals vs fitted values is low enough to accept the existing multi-variate ...
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Regress 4 values based on an input matrix with shape of (16,5)

Problem: I need to regress 4 values (which quantify how much a user likes a specific topic). I am performing simulations, so I know the ground truth (the real 4 values of the preferences) Input Data ...
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How to model a system of equations as a standard linear regression problem

Having a sample of two independent and two dependent variables (), and considering that and , and knowing that , how can I model this problem as a standard linear regression problem?
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How to approach modeling multiple outcomes with differing distributions in Bayesian setting?

I'm planning a replication study, and am exploring various ways of analyzing the data. In a single study, I will have two outcome measures, which are expressed on completely different scales - one is ...
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21 views

Appropriate model that considers both treatment and risk factors for outcome

Consider a retrospective study to be planned with 200 patients with 5 risk factors (such as age, disease score = disease severity, hypertension etc.) which may affect (a) outcome as such (older ...
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Multivariate Regression: Both Continuous and Categorical Predictor Variables

I am involved in a meta-analysis assessing the role of multiple baseline characteristics (e.g. age, BMI, symptoms and signs) in a given disease. One element of our analysis includes a multivariate ...
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In multivariate regression, under what condtions is $var(X_i\epsilon_i')$ positive definite?

Suppose we have $(Y_i, X_i)$, with $Y_i$ an r.v. in $\mathbb{R}^k$ and $X_i$ an r.v. in $\mathbb{R}^p$ and suppose the covariance matrix of $X_i$, $E(XX')$ is positive definite. Now we can estimate ...
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Exclusion of Spuriousness With More Dimensions?

The usual way of excluding spuriousness in multivariate correlation is to increase the number of samples. However, if increasing the dimensions of measurements produces an unusual magnitude of ...
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What is MCRMSE (mean columnwise root mean squared error)?

The MCRMSE evaluation metric was used in the Kaggle Competitions Africa Soil Property Prediction Challenge(6 years ago) and OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction(On-going) ...
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Are the outcome and predictor variables in a logistic/linear regression interchangeable?

Consider the following example. I am studying the mutation burden across three subtypes of cancer. In my dataset, I have individuals without cancer (controls) and individuals with cancer (cases); the ...
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Wald test / seemingly unrelated regression on models with clustered standard errors in R

I am trying to conduct a Wald test (aka seemingly unrelated regression) on multivariate models with clustered standard errors in R. This is easy to do in Stata, but I cannot figure out how to do it in ...
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Multivariate regression (multi-target regression) dataset where some regression coefficients are known to be zero

I'm looking for a sample dataset for multivariate linear regression - also known as multi-target or multi-output. Preferably with more than 10 inputs and more than 10 outputs. There don't seem to ...
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SEs in multivariate regression

To avoid confusion, my question refers to multivariate regression as multiple dependent variables for the same set of independent variables. As far as I understand (see e.g., this question), the ...
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Multivariate Linear Regressions with heterogeneous covariance matrices

Let $\mathbf Y$ be $N\times K$ response matrix and $\mathbf X$ be $N\times (p+1)$ design matrix (including the intercept), consider the multivariate linear regression, $$ \mathbf Y = \mathbf{XB+E}\,, $...
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How to compute sobol indices from a multi-variate regression model?

I have a dataset that consists of 300 input variables, 200 response variables, and 15000 samples. These response variables are basically a profile of 200 different values of the same response and they ...
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1answer
43 views

Corrections for logistic regression (multiple comparisons?)

I'll cut to the chase. Here's what I'm doing: looking at the influence of water temperature and salinity on the presence of water-borne parasites for a certain estuarine bird population birds are ...
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29 views

Test for comparing corresponding coefficients in multivariate regression

This question is related to this but I am hoping for more tangible techniques than a general discussion. For simplicity, suppose I am trying to linearly model two correlated responses $Y_1$ and $Y_2$ ...
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p-value of multivariate response in partykit::ctree

I wonder if anyone can help me to understand the two questions regarding partykit::ctree: what's the difference between "quadratic" and "maximum&...
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mcglm matrix linear predictor - Error in base::tcrossprod(x, y) : non-conformable arguments

I'm trying to fit a multivariate multiple regression model using the mcglm package (version 0.6.0) and have faced problems when specifying components of the matrix ...
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Model selection for multivariate mixed models

I would like to perform a multivariate mixed model but am a bit confused about model selection for such models. I wonder if I could get some help here. When fitting a univariate mixed model, to avoid ...
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112 views

Including both binary and Gaussian responses in one multivariate mixed model in MCMCglmm

I would like to fit a multivariate mixed model to a dataset that contains: a binary Direction variable showing the direction the individuals moved toward, a continuous PropRangeChange_s variable ...
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53 views

When will lasso and regression tree perform differently?

If I want to use lasso and regression tree respectively to generate the important variable lists. I'm wondering when will they generate lists with a huge difference? Under what signal structure?
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How do I make sense of 200 regression models?

I'm dealing with a dataset that has about 300 input features, and about 200 response variables and consists of 25000 samples. These response variables are basically a profile of 200 different values ...
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Difference between several univariate regressions and one multivariate regression in a machine learning context

I know there are several other questions asking for the advantages of multivariate regression over several univariate regressions (e.g. this). I understand that the dependent variables can be ...
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1answer
75 views

How does SPSS calculate composite variable for MANOVA/Multivariate multiple regression?

I have analysed my data using multivariate multiple regression (8 IVs, 3 DVs), and significant composite results have been found. Before reporting my findings, I want to discuss in my results chapter ...
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1answer
121 views

Setting priors for bivariate regression

I would like to perform a bivariate MCMC regression with boldness scores as the continuous response variable, aggression ranks as the ordinal response variable, trial numbers as fixed effect and ...
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Multivariate regression with a mix of ordinal and continuous dependent variables [closed]

I would like to run a multivariate mixed regression MCMC model with two response (independent) variables, namely x and y. x is continuous while y is ordinal. There is one predictor variable that is ...
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1answer
29 views

Is multivariate regression the right test?

Data Amount spent on product type A Amount spent on product type B 5 age groups External factor X External factor y I have data in the form of monthly sums over 60 months for all of those data-...
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How to calculate the ratio of observations to model parameters in a VAR(p) model

In machine learning, it's pretty common to use the general rule of thumb that one should have at least 10X as many observations as there are model parameters to avoid overfitting. As discussed here ...
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Liklihood ratio test and linear mixed effects regression

I have a data set which includes sex, age, and 5 polygenic scores as independent variables, with 16 dependent variables. I have constructed univariate linear mixed effects regression models and ...
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Comparing two approaches to modeling dependence in bi-variate Gaussian regression

Presume we would like to model the dynamics of two related variables as a bi-variate Normal, while also accounting for the effect of other covariates (via regression). E.g. I would like to model ...
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1answer
93 views

Forecasting a time series $(x_t,{\bf Y_t})$ where all we care about is forecasting $x_t$

Consider a multivariate time series $(x_t,{\bf Y}_t)$ $1\le t \le n$ taking values in $\mathbb{R}^{d+1}$, and suppose that we wish to forecast $x_t$ using its own path as well as the "exogenous&...
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Best way to find correlation between categorical response variables and continuous explanatory variables

Sorry if this is a really simple question, but I'm very new to multivariable statistics and I'm trying to find a best method to deal with my ecological dataset. I recorded the environmental ...
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How can the cost function dimensionality be understood?

Most tutorials on the internet demonstrate the cost function with the m * x + b = y function, which is fine and understandable for a start.. They presents the ...

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