Monte Carlo and SBPNN-based critical values for Data Snooping

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.

corrMatrix.m：此 Matlab 函数计算 w-test 统计量的相关矩阵。
KMC.m：此 Matlab 函数基于蒙特卡洛方法计算最大-w 统计量的临界值。在使用前必须运行 corrMatrix.m。
kNN.m：此基于神经网络的 Matlab 函数允许用户在良好控制 I 类错误的前提下获取所需的临界值。在此情况下，需要下载文件 SBPNN.mat 并将其保存在您的文件夹中。在使用 corrMatrix.m 之前必须运行它。
SBPNN.mat：MATLAB 的灵活网络对象类型（称为 SBPNN.mat），允许用户在良好控制 I 类错误的前提下获取所需的临界值。
Examples.txt：包含地籍网络调整问题中设计和协方差矩阵示例的文件。
rawMC.txt：基于蒙特卡洛方法的临界值，针对以下显著性水平：α′= 0.001、α′= 0.01、α′= 0.05、α′= 0.1 和 α′= 0.5。对于每个 α′，观测数（n）均固定在 n = 5 至 n = 100 之间，增量为 5。对于每个 n，w-检验之间的相关性（ρwi,wj）也固定在 ρwi,wj = 0.00 至 ρwi,wj = 1.00 之间，增量为 0.1，同时考虑 ρwi,wj = 0.999 的相关性。对于 α′、n 和 ρwi,wj 的每种组合，均进行了 5,000,000 次蒙特卡洛实验。