Questions tagged [regression]
The regression tag has no usage guidance.
11 questions
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non-linear regression analysis using a floor function over the independent variable?
I have created a data set to understand better an equation and apply it to predict behavior.
The equation is y = 10/(1+k*⌊((x/t)^s)⌋).
To create the data set and see if it is working properly, I did ...
2
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1
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How to show that minimizing the epsilon-insensitive loss is equivalent to a quadratic program with inequality constraints?
This question is about an optimization problem that arises in support vector regression (SVR). Suppose you have $N$ pairs $(\vec{x}_n, y_n)$ as data and would like to find a vector of weights $\vec w \...
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2
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Loglog transformation of optimization problem, how can the solution be equal to the nontransformed counterpart?
Consider the following two functions:
$$y_t = e^{lt} \cdot e^{st} \cdot \prod_{p=0}^{n} x_{tp}^{b_{tp}}\tag1$$
Where $e^{lt}$ captures the trend, $e^{st}$ captures the seasonality and $x_{tp}$ is our ...
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Training ML models to be used as objectives in optimization problems
Suppose that we have data (in my case, from a chemical process) which includes input data $X$ (characteristic of the material to be processed) and decision data $Y$ (decisions taken by operators to ...
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2
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Lagrange for quadratic programming with linear constraint
First of all, thanks for the help in previous post. The problem I'm facing is some legacy codes used Lagrange multipliers to solve a weighted regression problem. New requirements changed and I'm ...
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3
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Translate standard weighted least square regression to quadratic programming
Sorry if this is really easy for you gurus. I'm trying to derive the reformulation of a weighted least square regression to a quadratic programming form. I understand there is a closed form solution ...
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Operational research and Linear regression
I have a pretty large data set with several independent variables and one variable that I would like to explain the behavior (dependent variable).
What I want to do is find the linear coefficient and ...
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Fast solvers for LASSO-type non-convex optimization problems
Given $y \in \mathbb{R}^{n \times 1}, X \in \mathbb{R}^{n \times p}$, $p > n$, assume a LASSO-type optimization problem in the form of
$$ \hat\beta=\underset{\beta}{\operatorname{argmin}}\frac{1}{2}...
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Tanaka's Fuzzy House Price Problem
I would like to understand and solve Tanaka's fuzzy house price problem (Table III). Unfortunately I couldn't able to build the problem structure and restrictions here (according to (19)). Anyone have ...
7
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Ridge Regression lagrange duality
In every machine learning book we see that it is roughly mentioned that the ridge regression:
$$p_1^* = \min\limits_{\beta} \ \left( \mathrm{RSS} + \lambda\sum_{j=1}^p \beta_j^2 \right)$$
is ...
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How to model nonlinear regression?
As part of my research in statistics, I recently stumbled upon this paper1 which provides an operational perspective into linear models.
In simple linear regression, quadratic programming can be used ...
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