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In Conventional Deformation Analysis (CDA), at least two different epochs are adjusted by using the Least Squares Estimation (LSE) method and compared statistically. The effect of the geometry of the network is an essential part of the adjustment model and the LSE method smears the effects of the displaced point over the other nondisplaced points. In this study, to remove these spoiling effects and to increase the reliability of the deformation analysis, a new approach is introduced. This approach depends on the analysis of the differences between observations of the two epochs, and also considers the principles of the model error approach. All possible combinations of the differences of the observations are considered as model errors in Gauss-Markov model and the estimated model error for the combination, that has the smallest variance, is compared with a critical value to answer the question whether it is significant or not. To compare the results of the new approach with the CDA, the Monte Carlo simulation technique and mean success rate are used in a leveling network. As a consequence, according to the simulation results, the new approach is better than the CDA by 7.6% and 9.7% for one and two displaced points, respectively, when the deformation network is designed as a subnetwork. [ABSTRACT FROM AUTHOR]

Classical outlier detection test methods such as Baarda test and Pope test are generally preferred in geodetic problems. They depend on the Least Square Estimation (LSE) and LSE is very sensitive to the variations of the model. The capacity of the LSE changes depending on the different significance level, different type of outlier, the number of outlier, magnitude of outlier, number of observations and the number of unknowns. In statistics, the power of test is the probability of rejecting the null hypothesis when the null hypothesis is false. It is a theoretical assumption and depends on the significance level α (Type I error) and β (Type II error). The different types of the outliers, such as random or non-random, affect the results of the test methods; but the power of test is the same for all different types of the outliers. In this study, empirical estimation of the power of test is presented as Mean Success Rate (MSR). The theoretical power of test and empirical MSR have been estimated for univariate model and linear model by using Baarda test; according to the obtained results, MSR can be used as empirical value of the power of test and capacity of the test models. Also, MSR reflects more realistic results than the theoretical power of test. [ABSTRACT FROM AUTHOR]