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A technique to estimate parameters $\beta$ of the linear model $Y=X\beta$ when both $Y$ and $X$ are subject to measurement error. Includes Orthogonal and Deming regression as special cases.

21 votes
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Does a correlation matrix of two variables always have the same eigenvectors?

Algebraically, correlation matrix for two variables looks like that: $$\begin{pmatrix} 1 & \rho \\ \rho & 1 \end{pmatrix}.$$ Following the definition of an eigenvector, it is easy to verify that $(1, …
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10 votes
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Fitting a plane to a set of points in 3D using PCA

When you perform principal component analysis (PCA) on your 27 points in 3D, you first subtract the mean vector $\mathbf m$ and then obtain three eigenvectors $\mathbf e_1, \mathbf e_2, \mathbf e_3$ o …
amoeba's user avatar
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67 votes
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How to perform orthogonal regression (total least squares) via PCA?

Ordinary least squares vs. total least squares Let's first consider the simplest case of only one predictor (independent) variable $x$. For simplicity, let both $x$ and $y$ be centered, i.e. intercep …
amoeba's user avatar
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