An uncertain regression model

Abstract

PurposeThe purpose of this paper is to propose an uncertain regression model with an intrinsic error structure facilitated by an uncertain canonical process.Design/methodology/approachThis model is suitable for dealing with expert's knowledge ranging from small to medium size data of impreciseness. In order to have a rigorous mathematical treatment on the new regression model, this paper establishes a series of new uncertainty concepts sequentially, such as uncertainty joint multivariate distribution, the uncertainty distribution of uncertainty product variables and uncertain covariance and correlation based on the axiomatic uncertainty theoretical foundation. Two examples are given for illustrating a small data regression analysis.FindingsThe uncertain regression model is formulated and the estimation of the model coefficients is developed.Practical implicationsThe paper is devoted to a regression model to handle a small amount of data with mathematical rigor.Originality/valueThe theory and the methodology of the uncertain canonical process regression is proposed for the first time. It addresses the practical challenges of small data size modelling.