References
- Guo, P., Tanaka, H. (2006). Dual models for possibilistic regression analysis. Computational Statistics & Data Analysis 51 (1), 253-266.10.1016/j.csda.2006.04.005
- Jun-peng, G.,Wen-hua, L. (2008). Regression analysis of interval data based on error theory. In: Proceedings of 2008 IEEEInternational Conference on Networking, Sensing and Control, ICNSC, Sanya, China, 2008, 552-555.10.1109/ICNSC.2008.4525279
- Lee, H., Tanaka, H. (1998). Fuzzy regression analysis by quadratic programming reflecting central tendency. Behaviormetrika 25 (1), 65-80.10.2333/bhmk.25.65
- Lima Neto, E. de A., de Carvalho, F. de A. T. (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics & Data Analysis 54 (2), 333-347.10.1016/j.csda.2009.08.010
- Moral-Arce, I., Rodríguez-Póo, J. M., Sperlich, S. (2011). Low dimensional semiparametric estimation in a censored regression model. Journal of Multivariate Analysis 102 (1), 118-129.10.1016/j.jmva.2010.08.006
- Pan, W., Chappell, R. (1998). Computation of the NPMLE of distribution functions for interval censored and truncated data with applications to the Cox model. Computational Statistics & Data Analysis 28 (1), 33-50.10.1016/S0167-9473(98)00026-7
- Zhang, X., Sun, J. (2010). Regression analysis of clustered interval-censored failure time data with informative cluster size. Computational Statistics & Data Analysis 54 (7), 1817-1823.10.1016/j.csda.2010.01.035
- Inuiguchi, M., Fujita, H., Tanino, T. (2002). Robust interval regression analysis based on Minkowski difference. In: SICE 2002, proceedings of the 41st SICE Annual Conference, vol. 4, Osaka, Japan, 2002, 2346-2351.
- Nasrabadi, E., Hashemi, S. (2008). Robust fuzzy regression analysis using neural networks. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16 (4), 579-598.10.1142/S021848850800542X
- Hesmaty, B., Kandel, A. (1985). Fuzzy linear regression and its applications to forecasting in uncertain environment. Fuzzy Sets and Systems 15, 159-191.10.1016/0165-0114(85)90044-2
- Hladík, M., černý, M. (2010). Interval regression by tolerance analysis approach. Fuzzy Sets and Systems. Submitted, Preprint: KAM-DIMATIA Series 963.
- Hladík, M., černý, M. (2010). New approach to interval linear regression. In: Kasimbeyli, R., et al. (eds.), 24th Mini-EURO conference on continuous optimization and information-based technologies in the financial sector MEC EurOPT 2010, Selected papers, Vilnius, Lithuania, 2010, 167-171.
- Tanaka, H., Lee, H. (1997). Fuzzy linear regression combining central tendency and possibilistic properties. In: Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, vol. 1, Barcelona, Spain, 1997, 63-68.
- Tanaka, H., Lee, H., (1998). Interval regression analysis by quadratic programming approach. IEEE Transactions on Fuzzy Systems 6 (4), 473-481.10.1109/91.728436
- Tanaka, H., Watada, J. (1988). Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets and Systems 27 (3), 275-289.10.1016/0165-0114(88)90054-1
- Černý, M., Rada, M. (2010). A note on linear regression with interval data and linear programming. In: Quantitative methods in economics: Multiple Criteria Decision Making XV, Slovakia: Kluwer, Iura Edition, 276-282.
- Dunyak, J. P., Wunsch, D. (2000). Fuzzy regression by fuzzy number neural networks. Fuzzy Sets and Systems 112 (3), 371-380.10.1016/S0165-0114(97)00393-X
- Huang, C.-H., Kao, H.-Y. (2009). Interval regression analysis with soft-margin reduced support vector machine. Lecture Notes in Computer Science 5579, Germany: Springer, 826-835.10.1007/978-3-642-02568-6_84
- Ishibuchi, H., Tanaka, H., Okada, H. (1993). An architecture of neural networks with interval weights and its application to fuzzy regression analysis. Fuzzy Sets and Systems 57 (1), 27-39.10.1016/0165-0114(93)90118-2
- Bentbib, A. H. (2002). Solving the full rank interval least squares problem. Applied Numerical Mathematics 41 (2), 283-294.10.1016/S0168-9274(01)00104-0
- Gay, D. M. (1988). Interval least squares—a diagnostic tool. In: Moore, R. E., (ed.), Reliability in computing, the role of interval methods in scientific computing, Perspectives in Computing, vol. 19, Boston, USA: Academic Press, 183-205.10.1016/B978-0-12-505630-4.50016-X
- Sheppard, W. (1898). On the calculation of the most probable values of frequency constants for data arranged according to equidistant divisions of a scale. Proceedings of the London Mathematical Society 29, 353-380.
- Kendall, M. G. (1938). The conditions under which Sheppard's corrections are valid. Journal of the Royal Statistical Society 101, 592-605.10.2307/2980630
- Eisenhart, C. (1947). The assumptions underlying the analysis of variance. Biometrics 3, 1-21.10.2307/3001534
- Schneeweiss, H., Komlos, J. (2008). Probabilistic rounding and Sheppard's correction. Technical report 45, Department of Statistics, University of Munich. Available at: http://epub.ub.uni-muenchen.de/8661/1/tr045.pdf
- Di Nardo, E. (2010). A new approach to Sheppard's corrections. Mathematical Methods of Statistics, 19 (2), 151-162.10.3103/S1066530710020043
- Wimmer, G., Witkovský, V. (2002). Proper rounding of the measurement results under the assumption of uniform distribution. Measurement Science Review 2 (1), 1-7.
- Wimmer, G., Witkovský, V., Duby, T. (2000). Proper rounding of the measurement results under normality assumptions. Measurement Science and Technology 11, 1659-1665.10.1088/0957-0233/11/12/302
- Ziegler, G. (2004). Lectures on polytopes, Germany: Springer.
- Avis, D., Fukuda, K. (1996). Reverse search for enumeration. Discrete Applied Mathematics 65, 21-46.10.1016/0166-218X(95)00026-N
- Ferrez, J.-A., Fukuda, K., Liebling, T. (2005). Solving the fixed rank convex quadratic maximization in binary variables by a parallel zonotope construction algorithm. European Journal of Operational Research 166, 35-50.10.1016/j.ejor.2003.04.011
- Grötschel, M., Lovász, L., Schrijver, A. (1993). Geometric algorithms and combinatorial optimization, Germany: Springer.10.1007/978-3-642-78240-4