Volume 8, Issue 1, January 2015, Pages 114 - 127
Interval-Valued Linear Model
Xun Wang, Shoumei Li, Thierry Denœux
Received 30 May 2014, Accepted 15 July 2014, Available Online 1 January 2015.
- https://doi.org/10.2991/ijcis.2015.8.1.10How to use a DOI?
- interval-valued linear model, least square estimation, best binary linear unbiased estimation, D metric
- This paper introduces a new type of statistical model: the interval-valued linear model, which describes the linear relationship between an interval-valued output random variable and real-valued input variables. Firstly, notions of variance and covariance of set-valued and interval-valued random variables are introduced. Then, we give the definition of the interval-valued linear model and its least square estimator (LSE), as well as some properties of the LSE. Thirdly, we show that, whereas the best linear unbiased estimation does not exist, the best binary linear unbiased estimator exists and it is the LSE. Finally, we present simulation experiments and an application example regarding temperatures of cities affected by their latitude, which illustrates the application of the proposed model.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Xun Wang AU - Shoumei Li AU - Thierry Denœux PY - 2015 DA - 2015/01/01 TI - Interval-Valued Linear Model JO - International Journal of Computational Intelligence Systems SP - 114 EP - 127 VL - 8 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2015.8.1.10 DO - https://doi.org/10.2991/ijcis.2015.8.1.10 ID - Wang2015 ER -