The hypothesis testing statistics in linear ill-posed models

Abstract

In the geodetic community, an adjustment framework is established by the four components of model choice, parameter estimation, variance component estimation (VCE), and quality control. For linear ill-posed models, the parameter estimation and VCE have been extensively investigated. However, at least to the best of our knowledge, nothing is known about the quality control of hypothesis testing in ill-posed models although it is indispensable and rather important. In this paper, we extend the theory of hypothesis testing to ill-posed models. As the Tikhonov regularization is typically applied to stabilize the solution of ill-posed models, the solution and its associated residuals are biased. We first derive the statistics of overall-test, w-test and minimal detectable bias for an ill-posed model. Due to the bias influence, both overall-test and w-test statistics do not follow the distributions as those used in well-posed models. Then, we develop the bias-corrected statistics. As a result, the bias-corrected w-test statistic can sufficiently be approximated by a standard normal distribution; while the bias-corrected overall-test statistic can be approximated by two non-central chi-square distributions, respectively. Finally, some numerical experiments with a Fredholm first kind integral equation are carried out to demonstrate the performance of our designed hypothesis testing statistics in an ill-posed model.