TY - JFULL
AU - Michal Cerny
PY - 2011/10/
TI - Computational Aspects of Regression Analysis of Interval Data
T2 - International Journal of Mathematical and Computational Sciences
SP - 1468
EP - 1476
VL - 5
SN - 1307-6892
UR - https://publications.waset.org/pdf/6479
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 57, 2011
N2 - We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.
ER -