Questions tagged [reduced-rank-regression]

Question 1

So with reduced rank regression we identify response variables associated with our outcome of interest (Y) and model proc pls relationships between the independent variables say dietary data vs. the ...

Question 2

A main benefit of Gaussian Process Regression is, that we not only get a prediction, but also a variance that we might use as indication of the prediction confidence. While bayesian linear regression ...

Question 3

I got two multidimensional datasets, X and Y. I thought I build the model, which explains the relationship between two datasets, using canonical correlation analysis (CCA). The first correlation ...

Question 4

In sparse optimization, I am trying to solve the problem $$ \min_{x\in \mathbb R^{n}} \quad f(x) + \|x\|_1 $$ and at optimality, $x^*$ may be sparse. If I define the sparse manifold as $\mathcal M = ...

Question 5

I am trying to optimize a panel regression $G=\beta G+e$. $G \in R^{N\times T}$. $\beta\in R^{N\times N}$ is unknown coefficient, constrained to $diag(\beta)=0$, and reduced rank $rank(\beta)\leq r$. ...

Question 6

I am interested in running a partial least squares analysis using PROC PLS in SAS 9.4. I understand that, by default, the predictors and response variables in PLS are centered to a mean 0 and scaled ...

Question 7

Consider a rank k matrix, call it M, of size nxm. All the elements are non-negative. Now do a noisy observation of it and assume independent Poissonian errors (the error on element $M_{ij}$ is ...

Question 8

In grad school, I was always taught the general linear model $$\mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\epsilon\tag{1}$$ where $\mathbf{y}$ is a vector, $\mathbf{X}$ is some matrix, $\...

Question 9

I am trying to find dietary patterns related to a disease outcome. Unfortunately, I only have the binary outcome "disease yes/no" as outcome. I tried to perform PCA on the data, but the dietary ...

Question 10

What is meant by the term "meta-parameter"? Can a definition, informal and/or formal, be provided? For example, in reduced-rank regression, the rank ($r$) can be referred to as a meta-parameter of ...

Question 11

This question results from the discussion following a previous question: What is the connection between partial least squares, reduced rank regression, and principal component regression? For ...

Question 12

Are reduced rank regression and principal component regression just special cases of partial least squares? This tutorial (Page 6, "Comparison of Objectives") states that when we do partial least ...

Question 13

Given two vectors of random variables $X$ and $Y$, Canonical Correlation Analysis (CCA) finds the transformation matrices $A$ and $B$ so that $\operatorname{corr}(A_{1*} X, B_{1*} Y)$ is first maximal,...

Question 14

I have been reading The Elements of Statistical Learning and I could not understand what Section 3.7 "Multiple outcome shrinkage and selection" is all about. It talks about RRR (reduced-rank ...

Question 15

I am trying to learn Reduced-Rank Regression (RRR) from The Elements of Statistical Learning. I find the writing and them mathematics a little too prohibitive. Does any of you have a resource/text/...

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Questions tagged [reduced-rank-regression]

Mediating effects in reduced rank regression

Low Rank Gaussian Process vs Bayesian Linear Regression

I'd like to do regression using canonical correlation analysis

Is low rank finite-iteration manifold identification possible?

how to optimize reduced rank regression with constant diagnoal constraint?

categorical predictors in partial least squares

Noisy Observation of Matrix of Certain Rank

Is there a way to specify reduced-rank regression using $\mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\epsilon$?

Reduced rank regression with binary outcome variable

Definition of "meta-parameter" [duplicate]

Probabilistic models for partial least squares, reduced rank regression, and canonical correlation analysis?

What is the connection between partial least squares, reduced rank regression, and principal component regression?

Objective function of canonical correlation analysis (CCA)

What is "reduced-rank regression" all about?

Friendly tutorial or introduction to reduced-rank regression

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