# Questions tagged [constrained-regression]

constrained regression is (linear or nonlinear) regression models with further constraints on the coefficients. That could be non-negativity constraints, constraints on the norm of the coefficient vector or otherwise.

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### Minimizing $L_2$ norm with constrained residual sum of squares (RSS)

I have some complex-valued time-series data, $y \in \mathbb{C}^n$ - a signal with additive Gaussian white noise. The goal is to find the Fourier coefficients of this signal. Ideally, you would just do ...
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### How to get standard errors of linear regression coefficients subject to linear equality constraints

I have a regression problem with multiple sets of sum-to-one categorical features (e.g. X1=Male, X2=Female, X3=Weekday, X4=Weekend), intercept needs to be included as well as some restrictions on the ...
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### Neural network regression with a constrained dependent variable

I'm using a multi-layer perceptron model to predict the cumulative number of boardings and alightings from a bus travelling on a standard route based on previous cumulative boarding and alighting ...
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### Difference between glmnet and nnls for non-negative least squares in R

I'm trying to do some non-negative linear regressions in R, and I found in the blog here https://www.r-bloggers.com/2019/11/non-negative-least-squares/ that either the package ...
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### How to fit a constrained regression in R?

My regression: $Y = α + β_1X_1 + β_2X_2 + β_3X_3 + β_4X_4 + β_5X_5 + β_6X_6 + β_7X_7 + β_8X_8 + β_9X_9 + β_{10}X_{10} + β_{11}X_{11} + β_{12}X_{12} + β_{13}X_{13} + β_{14}X_{14} + ε$ Constraints that ...
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### Constrained least squares for standardization of dataset

I have a set of correlation matrices $\mathbf{\Sigma}_{i,j}$ where $i$ is the $i^{th}$ dataset and $j$ is the $j^{th}$ sample of the $i^{th}$ dataset. I am trying to standardize correlation matrix ...
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### Can it be proven that non-negative least squares is a perfectly convex problem?

Can it be proven that NNLS is a perfectly convex problem? Myre writes in section 3 of his TNT-NN manuscript: The NNLS objective function is a convex quadratic function with linear inequalities as ...
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### Numerical solution to the constrained ridge regression

The constrained ridge regression problem is of the form: $\arg\min_{\|\beta\|_2\le t}\|X\beta-y\|_2$. Given a matrix $X$, a vector $y$ and the constrain parameter $t$, how do you solve it numerically? ...
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### How to fit a constrained harmonic fit in R to mean daily temperature data?

I am trying to reproduce the statistics procedures for creating daily normal temperatures according to this paper. I was provided code in IDL (a Fortran based scientific language), but it is so ...
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### Constraining regression coefficient to non-negative [duplicate]

I have a regression problem where I don't want the coefficients to be negative. Is setting negative coefficients of OLS to zero the same as constraining the coefficient to be non-zero and solving it ...
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### How to get accurate estimates on Neural Networks Hessian?

I need to get not only accurate estimates on the neural network output itself but also on its second order derivatives in order to use the NN for optimization problems. With Adam optimizer I can't get ...
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### Linear Regression with constraint(s): need s.e., t-stat, and p-values of the regressors

I am performing a linear regression and what I need is (1) to constrain the sum of the regression coefficients to 1, and (2) to constrain the sum of regression coefficients to 1 AND each regression ...
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### Query on constrained regressions using -mgcv- and -pcls- in R

Bert Gunter from the R-help list directed me to ask this query here. Basically, I'm puzzled! How is it that this constrained regression routine using -pcls- runs satisfactorily (courtesy of Tian Zheng)...
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### How to test if sum of two coefficients of ols model is greater than zero using R?

The regression model is: y = b0 +b1x1 + b2x2 + b3x3 + e I want to test if b1 + b2 > 0. the R package ...
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### How to create a constrained spline meta model in R?

Background I have an outcome measure I'm trying to predict that is on a scale 0-1000. I've trained (say) M different models (model definition doesn't matter) on overlapping sub-ranges of the data (e....