Common Subset Selection of Inputs in Multiresponse Regression

@article{Simil2006CommonSS,
  title={Common Subset Selection of Inputs in Multiresponse Regression},
  author={Timo Simil{\"a} and Jarkko Tikka},
  journal={The 2006 IEEE International Joint Conference on Neural Network Proceedings},
  year={2006},
  pages={1908-1915}
}
We propose the multiresponse sparse regression algorithm, an input selection method for the purpose of estimating several response variables. It is a forward selection procedure for linearly parameterized models, which updates with carefully chosen step lengths. The step length rule extends the correlation criterion of the least angle regression algorithm for many responses. We present a general concept and explicit formulas for three different variants of the algorithm. Based on experiments… CONTINUE READING
13 Citations
19 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 19 references

Developments in linear regression methodology: 1959– 1982

  • R. R. Hocking
  • Technometrics, vol. 25, pp. 219–249, Aug. 1983.
  • 1983
Highly Influential
3 Excerpts

Sparse regression for analyzing the development of foliar nutrient concentrations in coniferous trees

  • M. Sulkava, J. Tikka, J. Hollmén
  • Ecological Modelling, vol. 191, pp. 118–130, Jan…
  • 2006
1 Excerpt

On homotopy algorithms in statistics

  • B. A. Turlach
  • keynote talk during the Symposium on Optimisation…
  • 2005
1 Excerpt

Predicting multivariate response in linear regression model

  • M. S. Srivastava, T.K.S. Solanky
  • Communications in Statistics – Simulation and…
  • 2003
1 Excerpt

Similar Papers

Loading similar papers…