Parameter estimation method of multidimensional measurement model with small sample based on MCM

In response to the problem of solving multiple linear regression measurement model in the case of small sample calibration test data, a method of parameter estimation for regression coefficient of measurement model based on Monte Carlo sampling idea and uncertainty evaluation theory is presented. In detail, the general form of multidimensional linear regression measurement model is introduced, and the principle and evaluation process based on Monte Carlo method (MCM) of solving the estimates and their standard deviations of the regression coefficient in measurement model are put forward. The optimization scheme for Monte Carlo sampling is further given, which can be adopted to ensure the reliability of parameter estimation under small sample condition. Finally, the feasibility and practicability of the proposed method are verified by a simulation example. In the case of small sample test data, this method can not only estimate the estimated values of each parameter in the multidimensional measurement model, but also evaluate the corresponding standard deviation. It can obtain a more scientific, reasonable and accurate measurement model, which provides a reliable model guarantee for practical measurement and evaluation applications.