In Multiple Regression without Intercept, we address the situation where the intercept coefficient is set to zero (i.e. regression through the origin). In this part of the website, we consider the cases where any of the regression coefficients can be set to a specific value or constrained by a lower and upper bound.
Topics
Examples Workbook
Click here to download the Excel workbook with the examples described on this webpage.
References
Lawson, C. L. and Hanson, R. J, (2014) BVLS code
https://netlib.org/lawson-hanson/all
Stark, P. B. and Parker, R. L. (1995) Bounded variable least squares: an algorithm and applications. Journal of Computational Statistics
https://digitalassets.lib.berkeley.edu/sdtr/ucb/text/394.pdf
Saraf, N. (2019) Bounded-variable least-squares methods for linear and nonlinear model predictive control
http://e-theses.imtlucca.it/296/1/Saraf_phdthesis.pdf
Fruehwirth-Schnatter, S. (2012) Linear combination of parameters
https://statmath.wu.ac.at/~fruehwirth/Oekonometrie_I/Folien_Econometrics_I_teil5.pdf
Jun, J. (2021) Regression and linear combinations
https://jeremykun.com/2021/03/29/regression-and-linear-combinations/