# Questions tagged [linear-regression]

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23
questions

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44 views

### Recovering sparsity from $\ell^1$ relaxation

I've read that $\ell^1$ norm on $\mathbb{R}^n$ is the convex relaxation of the $\ell^0$ "norm":
$$\|x\|_0 \triangleq \sum_{i=1}^n I_{x_i \neq 0}.$$
In the regression setting, under what ...

**0**

votes

**1**answer

102 views

### Hanson-Wright inequality with random matrix

I'm interested in bounding the tail probabilities of a quadratic form
$x^t A x$ where $x\in \mathbb{R}^n$ is a sub-Gaussian vector with independent entries. $A\in \mathbb{R}^{n\times n}$ is a matrix. ...

**1**

vote

**3**answers

69 views

### RKHS/non-parametric regression with missing response values

I am interested in doing RKHS regression with missing response variables.
Given input-output pairs $(x_i,y_i)$, I want to estimate a function $f(\cdot)$ as follows
\begin{equation}f(x)\approx u(x)=\...

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62 views

### How to compress variables in a linear regression

I have a large linear regression where all the independent variables are logical (ie TRUE/FALSE) and sparse. The data has roughly 10,000 variables and 10 million observations, on average around 20 ...

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72 views

### Does the intercept converge if we fit a best fit line to points with prime coordinates?

A few months ago I asked this question on Mathematics Stack Exchange but it has received little attention. Perhaps the question is more applicable here.
Let $p_k$ denote the $k$th prime such that $...

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**3**answers

226 views

### Regularized linear vs. RKHS-regression

I'm studying the difference between regularization in RKHS regression and linear regression, but I have a hard time grasping the crucial difference between the two.
Given input-output pairs $(x_i,y_i)...

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**2**answers

76 views

### Unique parameterization of size MxN matrices of rank k

Any rank k matrix $Y\in\mathbb{R}^{m\times n}$ can be written as:
$$ Y = UV'$$
Where $U\in \mathbb{R}^{m\times k}, V\in \mathbb{R}^{n\times k}$. This factorization is not unique since for any ...

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votes

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218 views

### Given the joint probability distributions of $X$ and $Y$ for $Y = R\,X+C$, find the probability distributions of $R$ and $C$

Let $R$, $C$, and $X$ be independent random variables defined on $(0,\infty)$ and
$$Y=\underbrace{R\, X}_{Z}+C.$$
We are given the joint probability distribution of $X$ and $Y$, $P_{XY}(x,y)$ and ...

**3**

votes

**1**answer

77 views

### Why to multiply the penalty by $n$ in the penalized least squares and likelihood?

In the SCAD paper by Fan and Li (2001), there exist two forms of penalized least squares as follows:
$$\frac{1}{2}\left \| y-X\beta \right \|^2+\lambda \sum_{j=1}^{d}p_j (\left | \beta _j \right |),$$
...

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40 views

### increasing inter-class distances results in decreasing linear regression error

Let $\{\mathbf{x}_i, y_i \}$ be a set of binary-labeled samples ($\mathbf{x}_i \in \mathbb{R}^d, y_i \in \{a,b\}, a,b\in\mathbb{R}$). Let $\{ \mathbf{x}'_i, y_i \}$ be also such a set.
Define $\mathbf{...

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**1**answer

47 views

### matrix regression under side conditions

I want to solve the folowing problem B*M=V, where B is the unknown of size 3x3, M of size 3xN and V of size 3xN. The difficulty is, that B has to be unitary.
N is in the range of 500. All matrices ...

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**0**answers

84 views

### A different objective function in liner regression analysis

I'm an undergraduate student who is green in statistics. I have a problem in the chose of objective function when estimating the parameters.
Let $Y = \beta^TX + \epsilon $ be the standard liner ...

**4**

votes

**1**answer

10k views

### Gauss-Newton vs Gradient Descent vs Levenberg-Marquadt for least squared method

I need to clarify some idea I have in my mind about linear and non-linear regressions. Whatever I now about this topic comes from the book of Taylor "Introduction to error analysis": a set of ...

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**0**answers

60 views

### Posterior consistency of non linear model

This is possibly a reference request. Let $G$ : $\mathbb{R}^p \to \mathbb{R}^q$ be a continuous injective/bijective function. Let $\mu$(we may also assume this to be a non degenerate Gaussian) be ...

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132 views

### Derivation of gradient of SSE in Geodesic Regression

On page 79 (or page 5) of this this paper the gradient of the SSE of the Geodesic model is described explicitly. My question is how are these equitations derived in detail; where can I find the ...

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**3**answers

1k views

### When does a Vandermonde-like matrix have full rank

I have a matrix which is similar to Vandermonde matrix except that the entries are monomials of degree $d$ polynomial in 2 variables. Each row has the following form:
$X_{i}= [1, x_{i}, y_{i}, x_{i}^...

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250 views

### Is there an efficient way to compute the “complete subset regression”?

Background: Let $X \in \mathbb{R}^{N\times K}$ and $y \in \mathbb{R}^{N\times 1}$ be data for a regression problem. The aim is to find $\beta \in \mathbb{R}^{K\times 1}$ such that $X\beta \approx y$ ...

**1**

vote

**1**answer

134 views

### Checking the intersection of two sets

Let $E\subset{\mathbb R}^n$ be a set of the type $I_1\times \dots \times I_n$, where $I_k$ are real intervals, and $X$ be and $n\times p$ real matrix. Suppose also that $rank(X)=p$ and $n>p$. Is ...

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votes

**1**answer

349 views

### Fitting a quadratic using regression when the y-intercept needs to be 0 [closed]

I'm trying to fit a quadratic $a_0 + a_1x + a_2x^2$ by Polynomial Regression:
$$
\begin{pmatrix}
n & \Sigma x_i & \Sigma x_i\\
\Sigma x_i & \Sigma x_i^2 & \Sigma x_i^3\\
\Sigma ...

**1**

vote

**0**answers

200 views

### How to find all least-square solutions [closed]

I was looking at numpy's lstsq to find a least squares solution of an equation system when the following occurred to me:
Given the points (0,0), (3,4), (4,3), if I ...

**4**

votes

**1**answer

116 views

### Regression with correlation structure

I have a theoretical question about regression models.
Let's say I measured multiple responses from $n$ subjects and these responses are correlated with each other. For example, let's say I measured ...

**1**

vote

**1**answer

2k views

### Minimizing sum of absolute deviations

Suppose we want to find coefficients $b$ in $\underset{b}{\operatorname{argmin}} \displaystyle\sum\limits_{i=1}^n | y_{i}-b_{1}x_{i}-b_{0}\mid$.
If we rewrite this problem in terms of linear ...

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278 views

### Testing contrasts in statistics: Is this provably a hard problem, or not?

Scheffé's method for identifying statistically significant contrasts is widely known. A contrast among the means $\mu_i$, $i=1,\ldots,r$ of $r$ populations is a linear combination $\sum_{i=1}^r c_i \...