A constrained center and range joint model to fit linear regression to interval-valued symbolic data is introduced. This new method applies both the center and range of the interval to fit a linear regression m
Regression analysis for interval-valued data. In Data Analysis, Classification and Related Methods : Proceedings of the 7th Conference of the IFCS, IFCS 2002, Berlin, pp. 369--374. Springer.BILLARD L,DIDAY E. Regression analysis for interval-valued data[M]. Berlin: Springer-Verlag,2000: 369...
Interval-valued linear regression has been investigated for some time. One of the critical issues is optimizing the balance between model flexibility and interpretability. This paper proposes a linear model for interval-valued data based on the affine operators in the cone $\\\mathcal{C} = \\\...
Billard & Diday (2000) proposed a regression method for interval-valued data based on the minimisation of mid-point errors. This method yields predictions for the lower and upper bounds of the dependent variable by substituting the lower and upper bounds of the covariates in the estimated ...
Interval-valued data is a complex data type which can be got by summarizing large datasets, linear regression models for interval-valued data have been widely studied. Panel data models combining cross-section and time series real-valued data have become
Linear regressionInterval dataCAPM modelAutoregressive modelThis paper introduces a new approach to fitting a linear regression model to interval-valued data by relaxing an assumption about using the center of interval data. We use convex combination between l...
4. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the centre and range of the interval-valued data are considered. 在线性区间迴归中,低和高的边界的区间值资料和中心点与范围的区间资料都被列入考虑。 5. Positive time-order-error of...
给出了均匀分布区间长度的估计量以及概率密度,并给出了区间长度的区间估计。 4. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the centre and range of the interval-valued data are considered. ...
This paper develops an estimation method for interval-valued data and applies it to forecasting the daily returns (up to five days into the future) of the SP500 over 13 years of data. We concentrate here on the case where the information is observed in the form of minimum and maximum valu...
Interval-valued time series are interval-valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval-valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt’s exponential smo...