Abstract
Abstract. This paper mainly expounds the parameter estimation method, the outlier diagnosis and the establishment of the optimal regression equation in the linear regression model theory, the analysis of the principle of the polynomial fitting model, the derivation of the algorithm process, and the research on the accuracy evaluation method.The GPS survey area is fitted and calculated. The fitting model is analyzed and compared in detail. The better parameter values and regression equation models of the planar region are estimated. The fitting accuracy meets the requirements of the fourth level measurement, which can be used in actual engineering. Replace the fourth level measurement in the application.
Highlights
Using the general linear hypothesis theory in the linear regression model, the significance of regression equations and regression coefficients in linear models, especially the gross error detection theory, is established, and a robust linear regression model is established to improve the prediction accuracy
The study uses the theory of parameter estimation in linear regression model theory, such as regression diagnosis (Cook distance) and Box-Cox transformation to improve the accuracy and reliability of parameter estimation in linear transformation model.The estimation of the regression parameters, the parameter estimation mainly solves the regression diagnosis statistic by knowing the point data, and analyzes the abnormal points by comparing the sizes of the respective quantities, 1thereby ensuring the accuracy requirement of the elevation fitting
Different GPS elevation fitting models based on linear regression theory are used to optimize the geoid-like surface and achieve high-precision conversion from high ground to normal high
Summary
Using the general linear hypothesis theory in the linear regression model, the significance of regression equations and regression coefficients in linear models, especially the gross error detection theory, is established, and a robust linear regression model is established to improve the prediction accuracy. Linear statistical model is a kind of highly practical model. It is widely used in the processing of surveying data. The theory mainly includes important parts such as parameter estimation, hypothesis analysis, linear regression, etc. The study uses the theory of parameter estimation in linear regression model theory, such as regression diagnosis (Cook distance) and Box-Cox transformation to improve the accuracy and reliability of parameter estimation in linear transformation model.The estimation of the regression parameters, the parameter estimation mainly solves the regression diagnosis statistic by knowing the point data, and analyzes the abnormal points by comparing the sizes of the respective quantities, 1thereby ensuring the accuracy requirement of the elevation fitting
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