1. Delete method: If the proportion of missing data is small, you can consider deleting samples or variables with missing values directly. This method is simple and feasible, but it may lead to a decrease in sample size, thus affecting the reliability of the research results.
2. The commonly used interpolation methods are random interpolation, multiple interpolation and Lagrange interpolation. These methods can effectively use the information of existing data, but may introduce some errors.
3. Regression method: the value of missing data is predicted by establishing a regression model. This method can make use of the correlation between existing data, but it needs to choose a suitable regression model, and may be affected by multiple * * * linearity and other problems.
4. Bayesian method: Based on Bayesian theory, the value of missing data is estimated by calculating prior probability and posterior probability. This method can make full use of the information of existing data and control the error by adjusting the prior probability.
In short, it is necessary to choose appropriate methods to deal with the lack of paper materials according to specific conditions. In practice, we can try many methods first, and determine the best scheme by comparing the results of different methods. In addition, we should pay attention to avoid introducing new errors when dealing with missing data to ensure the reliability and effectiveness of the final research results.