Monte Carlo and SBPNN-based critical values for Data Snooping

Published: 10 September 2021| Version 5 | DOI: 10.17632/77sfpx9b74.5
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Description

corrMatrix.m: This Matlab function computes the correlation matrix of w-test statistics. KMC.m: This Matlab function computes the critical values for max-w test statistic based on Monte Carlo method. It is needed to run corrMatrix.m before use it. kNN.m: This Matlab function based on neural networks allows anyone to obtain the desired critical value with good control of type I error. In that case, you need to download file SBPNN.mat and save it in your folder. It is needed to run corrMatrix.m before use it. SBPNN.mat: MATLAB's flexible network object type (called SBPNN.mat) that allows anyone to obtain the desired critical value with good control of type I error. Examples.txt: File containing examples of both design and covariance matrices in adjustment problems of geodetic networks. rawMC.txt: Monte-Carlo-based critical values for the following signifiance levels: α′= 0.001, α′= 0.01, α′= 0.05, α′= 0.1 and α′= 0.5. The number of the observations (n) were fixed for each α ′with n = 5 to n= 100 by a increment of 5. For each "n" the correlation between the w-tests (ρwi,wj) were also fixed from ρwi,wj = 0.00 to ρwi,wj = 1.00, by increment of 0.1, considering also taking into account the correlation ρwi,wj = 0.999. For each combination of α′,"n" and ρwi,wj, m= 5,000,000 Monte Carlo experiments were run.

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Institutions

Universidade Federal de Uberlandia

Categories

Geodesy, Statistical Quality Control, Quality Control Testing, Applied Statistics

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