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Questions tagged [multivariate-analysis]

Analyses where there is more than one variable analyzed together at once, and these variables are either dependent (response) ones or the only ones in the analysis. This can be contrasted with "multiple" or "multivariable" analysis, which implies more than one predictor (independent) variable.

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9 views

Decomposition of Mahalanobis distance: Where's my mistake?

Kim (2000) gives the formula for the decomposition of the Mahalanobis distance:  (\mathbf{x}-\mathbf{\mu})^{T}\mathbf{\Sigma}^{-1}(\mathbf{x}-\mathbf{\mu}) = \sum_{i = 1}^{d}(1 - \rho_{i}^{2})^{-1/...
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is “multivariable linear regression” the same as logistic regression?

I am new to machine learning and I am simultaneously studying linear and logistic regression. Logistic regression is when there is one dependent variable and there may be more than one independent ...
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how to understand an unit of analysis

I came a cross a definition which I cannot understand the logic. is there a way to explain what is the problem ? a longitudinal design for an intervention study in 10 participants where the ...
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How to generate a Tornado Diagram showing impact of different variables on output mean using 1000 sensitivity runs of a complex model?

How can I generate a Tornado diagram showing % impact of different variables on mean output? I have 1000 simulation runs of a complex model with multiple variables. In addition a normal QCing of ...
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Multivariate Bayesian Car Model Result

I have developed a multivariate Bayesian Car model for three crash severity level analysis. I found that the covariance for both heterogenous effects and the spatial effect is not significant for any ...
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Help with model selection [on hold]

In my experimental setup, I have a square symmetric matrix of variables (65) which I'll call A. Aij represents some relationship between the variables. I then performed an experiment in which I have ...
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Can I use LASSO as a variable selector with only three variable?

Can we still use LASSO even when we have a small number of variables?
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Simulate multivariate outliers that are hidden in 2D scatterplots

How could I simulate multivariate outliers that are "hidden" in all pairwise 2D scatterplots between the variables? By "hidden" I mean that they can't be seen (as obvious outliers) or detected ...
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Measuring variation explained in community matrix using both geographic distance and and environmental variables

So I have this dataset where I have species community data from a variety of sites. I’m trying to explain what are the factors that drive the variation in these data. For each site, I have a ...
21 views

Can knn be used for multivariate multiple regression?

I am working with MLB data with around 15000 observations for seasonal player stat. The data frame's structure looks like this (I'm making up the stats): ...
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Bayesian update vs optimization in multivariate case

Say I have a multivariate normal vector $r$~$N(\mu , \Sigma )$ and I observe that $y \equiv Pr + \epsilon = Q$ where $P$ is a matrix and $Q$ a vector and $\epsilon$~$N(0 , \Omega )$. Now I ...
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Distribution of a quadratic form [closed]

Problem: Let $x \sim N_{k}(\mu, \sigma^2 I)$. Show that $x^\prime x/\sigma ^2 \sim \chi^2(k, \mu ^\prime \mu/2\sigma ^2)$ I am trying to use the following theorem result but I am kinda confused how ...
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linear combination and univariate normal

Show that $(X_1,X_2)$ has a bivariate normal distribution with means $\mu_1, \mu_2$, variances $\sigma _1^2$ and $\sigma _2^2$, and correlation coefficient $\rho$ if and only if every linear ...
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Distribution to Examine Perceived Impact of Aircraft Flyovers [closed]

I am attempting to model the impact of aircraft flyover noise and had a two question survey filled by students 1) The frequency of disruption of their study sessions per day 2) The perceived noise ...
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What analysis should I use?

I have a dataset with 5 years of data, and am looking at trends in outcomes over time - so I have a categorical independent variable of Year, and 7 dependent variables, 5 of which are boolean, 2 of ...
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How to choose the suitable Neural Network Architecture for Regression Tasks

so I'm working on a Project where I want to predict the Vehicle Position from the Vehicle Data like speed, acceleration etc.. now the data that I have comes also with a timestamp for each sample ( I ...
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Best Statistical Test for my situation?

For an upcoming project we are trying to create a recommendation algorithm for customers and providers. I will have some number of service providers, probably hundreds to thousands, and some number ...
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conversion of multivariate time series into functional data analysis

i am trying to generalize the functional autoregressive model of order one to some order p, for this i calculate the functional principle components, and choose a particular pcs which explain a given ...
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In what circumstances would be consider using PLS-DA rather than Linear Discriminant Analysis?

I'm wondering in what kind of case would be consider using PLS-DA rather than Linear Discriminant Analysis? And what's the difference?
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Is the joint distribution $P_{XY}(x,y)$ determined from the marginal $P_X(x)$ and the conditional $P_{X|Y}(x|y)$?

For simplicity assume that $X,Y$ are discrete, finite, random variables, with joint distribution $P_{XY}(x,y) = \mathbb{P}(X=x\wedge Y=y)$. Now suppose that we do not know $P_{XY}(x,y)$, but are ...
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Convergence in distribution of $\sqrt{n-3}(z -\zeta)$

Let $(x_i,y_i)$ be iid $(x,y), i = 1,..,n$ be any bivariate sample. The correlation coefficient \begin{equation} \begin{split} \rho & = \text{cor}(x,y) \\ & = \frac{\text{cov}(x,y)}{\sqrt{\...
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Using NbClust on datasets that produce some negative eigenvalues. When to exclude data, when to force to positive, when to exclude test index?

Background on why I am using clustering: I am analyzing data from a multistep biological experiment, where each step is done in batches of varying sizes. I want to account for any biases that might ...
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compare 2 different sized multivariate samples

I have a two data tables like below. The dataset1 represents failed candidates. The dataset2 represents the successful candidates. I want to know, by applying some inference statistics which var (...
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Ranking groups based on multiple criteria

My objective: To give a more sound foundation to the data I have access to. This is an exercise that is aimed to look for some structure and soundness in the interpretation of the data BUT it can be ...
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How to show that $X = LY$ where $Y\sim N(0,I)$?

Let $X\sim MVN(0,\Sigma)$ denote a random vector having the multivariate normal distribution with mean $0$ and covariance matrix $\Sigma$. Suppose we want to sample from $X\sim MVN(0,\Sigma)$. ...
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How to Create a Toy Example of the Curds and Whey Algorithm?

Why is my simulated example failing? I am trying to create a toy example of the Curds and Whey method for multivariate linear regression in python (An example in R would be very helpful as well). I ...
60 views

Assessing Continuous Predictor with HR in Cox Model

I would like to recreate the following figure The authors state this about the figure: "Multivariate Cox regression models witha cubic natural spline analysis were used to determine the potential ...
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Secret Life of Covariance Matrix

I am currently reading Secret Life of Covariance Matrix: http://www.inf.fu-berlin.de/inst/ag-ki/rojas_home/documents/tutorials/secretcovariance.pdf and am confused by the following: Now, in the ...
I have a doubt about the proof of the fact that a positive semi-definite matrix is a covariance matrix. The professor do the following proof: Let $\Sigma$ be a positive semi-definite $p \times p$ ...