The inverse of a given square matrix, $A$, is the matrix $A^{-1}$ such that $AA^{-1}$ is the identity matrix.

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27 views

Inverse of block covariance matrix

I have a positive definite symmetric covariance matrix which looks like this: A, B, C, D and E, F, G are MATRICES, also positive definite symmetric covariance What is the inverse of such a matrix? ...
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1answer
18 views

Evaluate high-dimensional Gaussian with variance matrix $\sigma^{2}I_{n_{t}\times n_{t}}+\boldsymbol{\Sigma}_{t}\boldsymbol{\Sigma}_{t}^{'}$

I need to compute the log-likelihood function in a high-dimensional Gaussian time-series. I have the following model: $\mathbf{y}_{t}\left|\mathcal{F}_{t-1}\sim\mathcal{N}\left(\mathbf{\boldsymbol{\...
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0answers
20 views

The Existance of Schur Complement Inverse

A block matrix $\mathbf{M}=\left[ \begin{array}{ccc} \mathbf{A} & \mathbf{B} \\ \mathbf{B}^T & \mathbf{C} \end{array} \right]$ is invertible if $\mathbf{A}$ and $(\mathbf{C}-\mathbf{B}^T\...
1
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1answer
44 views

Efficient routines for a regression with orthogonal regressors?

I have a standard OLS regression setup, where (sets of) the regresors are orthogonal to each other. I am looking for a fast low-level way (using qr() instead of <...
1
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38 views

Application of Givens rotation to two matrices

I am reading this paper on Multiresolution Matrix Fatorization, http://arxiv.org/pdf/1507.04396v1.pdf, and have come across something that seems like an error to me. In Algorithm 2, the authors take $...
5
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3answers
250 views

Invert a sparse covariance matrix

I have a postive definite symmetric covariance matrix which looks like this: Note that all A,B,C,D,E,F,G are also poitive definite symmetric covariance matrices I want to find an easy way were I can ...
5
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1answer
58 views

Interpretation of the cluster criterion $\operatorname{tr}(S_W^{-1}S_B)$

There is a cluster criterion defined as: $$\mathcal{C} = \operatorname{tr}(S_W^{-1}S_B) = \sum_{i=1}^d \lambda_i,$$ where $\operatorname{tr}$ is the trace, $S_W$ is the pooled within-group scatter ...
5
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1answer
93 views

Lucid explanation for “numerical stability of matrix inversion” in ridge regression and its role in reducing overfit

I understand that we can employ regularization in a least squares regression problem as $$\boldsymbol{w}^* = \operatorname*{argmin}_w \left[ (\mathbf y-\mathbf{Xw})^T(\boldsymbol{y}-\mathbf{Xw}) + \...
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0answers
25 views

Inverse of ordering function

Lets assume we have p by n matrix.We can generate an output matrix, w (p x p) such as w_ij represent how many times i_th rows number is bigger than j_th (can be at most n obviously). My question is ...
6
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2answers
208 views

Prove that $(A^{-1} + B^{-1})^{-1}=A(A+B)^{-1}B$

I have this equality $$(A^{-1} + B^{-1})^{-1}=A(A+B)^{-1}B$$ where $A$ and $B$ are square symmetric matrices. I have done many test of R and Matlab that show that this holds, however I do not know ...
4
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3answers
471 views

Matrix inverse not able to be calculated while determinant is non-zero

I was attempting to calculate an OLS regression in R when I saw some strange things. The inverse of a square matrix does not exist if and only if the determinants is 0. Also, the matrix must be of ...
2
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1answer
234 views

What is the purpose of inverse distance?

In many econometric models, they use the inverse distance, instead of just distance. For example, if they are looking at the impact of distance to CBD on land values, they might use the inverse ...
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2answers
75 views

Efficient, stable inverse of a patterned covariance matrix for gridded data

I have computed a stationary covariance matrix defined for data on a grid. The data y are regularly spaced in 3D, lexicographically ordered in the covariance matrix, and I'm using a using a square ...
6
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1answer
124 views

Explain how `eigen` helps inverting a matrix

My question relates to a computation technique exploited in geoR:::.negloglik.GRF or geoR:::solve.geoR. In a linear mixed model ...
3
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3answers
218 views

What does that statistically mean , if $(X'X)^{-1}$ does not exist?

It is not a real world case , but suppose that we have $n$ observations and $k$ variables , since $k= n - 1 $ , if $X$ is the design matrix,$(X'X)$ will be a square matrix , so What does that ...
2
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1answer
88 views

Notation for ordinary and ridge regression

I'm trying to obtain an estimator $f(x)=y$ where $x \in \mathbb{R}^{D_1}$ and $y \in \mathbb{R}^{D_2}$, both are column vectors. So my training set $X$ and $Y$ are data matrices of size $D_1 \times ...
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29 views

Deriving expectation involving Wishart distributions $E[\bf{A(A'WA)^-A'W}]=\bf{A(A'\Sigma A)^-A'\Sigma}$

I have a problem deriving two expectation involving Wishart distributions with mean zero. Let $\bf{W} \sim {W_p}({\bf{\Sigma }},$$n\bf{)}$ and $\bf{A}$$: p\times q$. Prove that $E[\bf{A(A'WA)^-A'W}]...
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80 views

System exactly singular with pgmm (package plm)

this is my first post, I'll do my best to be clear and complete. I am trying to run a pgmm regression (Arellano Bond estimator) following the example online with the EmplUK dataset. My dataset is ...
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1answer
29 views

Computing negative half power of a bandwidth matrix for multivariate KDE

I am currently working on a project on density-based clustering algorithms and I am trying to make sense of the formula for a multivariate kernel density estimator [see wiki]. $$ \hat{f}_\mathbf{H}(\...
4
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1answer
166 views

Generalized inverse solution to system of linear equations proof

I'm going through a set of course notes for an introduction to the theory of linear models class at my university. Unfortunately, the professor who wrote this note set is no longer at this school, and ...
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2answers
110 views

Sample covariance matrix and its inverse

Assume we have the sample covariance matrix $S_1 = XX'/k$ which is not positive definite (in fact it is positive semi-definite) and not well conditioned in very large dimension (large $p$, small $k$). ...
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1answer
102 views

Interpretation of (diagonalized) inverse covariance matrix

There are several threads here about covariance matrix and inverse covariance matrix interpretation (here, here or here). However, I was wondering how to interpret the inverse covariance matrix (or ...
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0answers
84 views

Regularization parameter to generate inverse covariance matrix

My data consists of approx. 5 Million binary strings (n) and every string is 2788 characters long. My goal is to find out if position i is correlated with position j. I approximated a covariance ...
4
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1answer
300 views

How to find factor that is making matrix singular

I have a 300+ column data.frame, and no matter how I break it up I get this error every time: ...
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2answers
641 views

reasons for a non-invertible matrix

I am trying to factor analyze a matrix in R and I keep getting errors that lead me to believe my matrix is non-invertible. What are the reasons a matrix could be non-invertible? The only one I found ...
4
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1answer
210 views

Cholesky factorization and forward substitution less accurate than inversion?

I recently asked this question asking for an efficient way to compute the Mahalanobis distance (without calculating the inverse). The accepted solution was to use the Cholesky factorization and ...
0
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1answer
44 views

Marginal Likelihood of a Gaussian Process Model, Duplicate entries in kernel matrix

I am trying to fit a Gaussian process model using the toolbox and I got stuck in the following problem. Assuming that I have some duplicated data points in my training data, then those will map to ...
0
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1answer
33 views

I need a matrix in order to calculate g-inverse of it [closed]

I want to calculate g-inverse of a matrix, which has 4 rows and not a square matrix and has no inverse. Please help me find such a sound (good) matrix. I only need a matrix. You can suggest a book or ...
2
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2answers
219 views

Understanding the marginal distribution of multivariate normal distribution

I am trying to better understand the multivariate normal distribution. Here I try to refer to the conditional distribution part of wiki also the fifth page of this tutorial. I do not quite ...
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36 views

Evaluate the multivariate normal using variance matrices $\boldsymbol{\Lambda}+\alpha_{i}\mathbf{a}\mathbf{a}^{T}+\beta_{j}\mathbf{b}\mathbf{b}^{T}$

I need to calculate a huge amount of inverses and determinants to evaluate the pdf of the multivariate Gaussian. Specifically I need to compute the inverses and determinants of the following ...
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175 views

Propogation of error in a matrix inversion

I'm trying to find the deterministic error bounds for some parameters calculated through distance geometry. The equation can be simplified to the following form: $ \left[\begin{matrix} x_1 \\ x_2 \\ ...
5
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1answer
135 views

How to calculate the inverse of sum of a Kronecker product and a diagonal matrix

I want to calculate the inverse of a matrix of the form $S = (A\otimes B+C)$, where $A$ and $B$ are symetric and invertible, $C$ is a diagonal matrix with positive elements. Basically if the ...
13
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1answer
1k views

Why does inversion of a covariance matrix yield partial correlations between random variables?

I heard that partial correlations between random variables can be found by inverting the covariance matrix and taking appropriate cells from such resulting precision matrix (this fact is mentioned in ...
3
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1answer
141 views

Invertibility of $X^TX$ with severe multicollinearity in regression

I am studying about multicollinearity in regression and in the book it says, "if there is severe (but not perfect) multicollinearity, two or more predictor variables are highly correlated, so $X^TX$ ...
0
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1answer
114 views

Matrix Inversion Error

I a Multiple linear regression model, from published literature, I am implementing a spreadsheet to generate new predictions based on the published model. the literature stated Coefficients and the ...
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0answers
379 views

R - system is computationally singular - dealing with small numbers

I'm working with a ~200x200 Markovian transition matrix of non-zero probabilities. Forcibly, these probabilities are, for the large part, going to be very small. I am trying to find the inverse of my ...
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0answers
220 views

Condition Number in Single Variable Regression

I've calculated a single variable linear regression using OLS (in the python statsmodels library). The model has a large condition number, and suggests there may ...
0
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1answer
36 views

Is it posible to find a derivative of the mean function of Gaussian process regression?

The mean function $\hat{\mu}(x_*)$ of GPR is $k(x_*, X)(k(X, X) + \sigma^2_w I)^{-1}Y$ where $k(\cdot, \cdot)$ is a kernel matrix or vector of appropriate size and is parametrized by some ...
6
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1answer
310 views

Diagonal elements of the inverted correlation matrix

Is it true that the diagonal elements of the inverted correlation matrix will always be larger than 1? Why?
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277 views

The inverse of AR correlation matrix

I want to find the inverse of the following matrix: $$ R_{k-1}=\begin{pmatrix} 1 &\rho &\rho^2 &\cdots &\rho^{k-2} \\ \rho &1 &\rho &\cdots &\...
2
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1answer
474 views

How to proof relationship between inverse covariance matrix and linear regression coefficients?

Edited: I would like to work out the above relationship, more precisely: Let $(Y_{1}, ..., Y_{m})$ be a zero-mean vector with covariance matrix $\Sigma$, and let $S \subset \{1, ..., m\}.$ The ...
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0answers
83 views

A non-negative definate matrix has a non-negative generalized inverse

I'm having trouble proving a N.N.D matrix has a N.N.D G-Inverse. So far I have: If we assume x = Az where x >= 0 and A is a nnd matrix. So if Y is a G-inverse than: x = Az = YAz = Yx >= 0 . Thus ...
0
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1answer
143 views

Orthogonalizing predictors for least squares estimation

I know that orthogonalization in LS is to avoid inverting X'X. The idea behind it is to find variables Z that are orthogonal to each other. Although the process to find those is clear to me, I don't ...
1
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1answer
276 views

Tolerance for pseudoinverse

In calculating the pseudoinverse of a matrix $A$, of size (m,n), I need to choose a tolerance threshold for the eigenvalues. I'm trying to understand how I should pick this. Matlab default is to use ...
3
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2answers
473 views

Inverting non positive definite covariance matrix

I have an expression for a covariance matrix $C$ in terms of the indices $i$ and $j$. In this way I can analytically calculate the elements of my covariance matrix, however when I try to invert $C$ ...
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2answers
199 views

Returning the inverse of a matrix in a quadratic program (SVM) in cvx optimization package

I am solving the dual QP of an SVM, and using the RBF kernel. As you know, the objective function is of the form $$f(\alpha) = \alpha^T Q \alpha $$ where $\alpha$ is the optimization variable and $Q$ ...
3
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1answer
106 views

Uniqueness of $x'\beta$ even when $\mathbb{E}(x^Tx)$ is not invertible

As discussed in user25658's answer to this question, when one wants to compute $$ \beta = \mathbb{E}(x^Tx)^{-1} \mathbb{E}(x^TY) $$ but $\mathbb{E}(x^Tx)$ is not invertible, $\beta$ is not uniquely ...
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126 views

Interpretation of regression coefficients obtained from applying left inverse of regressor matrix in an underdetermined system?

If $X^\dagger$ is the pseudo-inverse of $X$, $\beta = X^\dagger y$ is the least squares solution for $\beta$ when $y=X\beta$. In the overdetermined case, applying $X^{\dagger,L} = (X^TX)^{-1}X^T$ ...
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2answers
129 views

IV estimator: efficient implementation?

I would like to implement (in R) an instrumental variable (IV) estimator, that takes the most general form (here not 2SLS or GMM!): $$ \beta_{IV} = (Z'X)^{-1}Z'Y $$ I could code this in the naive way,...
26
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2answers
11k views

What does the inverse of covariance matrix say about data? (Intuitively)

I'm curious about the nature of $\Sigma^{-1}$. Can anybody tell something intuitive about "What does $\Sigma^{-1}$ say about data?" Edit: Thanks for replies After taking some fantastic courses, I'd ...