Abstract: Variance component estimation (VCE), herein called joint inversion, is a widely used approach to weigh the contributions of different data sets. Traditionally, the random errors of observations in VCE are modeled as Gaussian. ...

Abstract: A multistart Nelder–Mead neural network algorithm (multi NM-NNA) is presented, the purpose of which is to solve the problem that the existing nonlinear search algorithms are unstable when inversing earthquake source ...

Abstract: To address the problem of inaccurate mixed additive and multiplicative random error stochastic model weighting, we apply the minimum norm quadratic unbiased estimator (MINQUE) to a mixed additive and multiplicative random ...

Abstract: In the field of geodetic data processing, the existing literature on the treatment of the multiplicative random error model assumes that the random multiplicative error elements are independent of one another. However, ...

Abstract: The scaled unscented transformation (SUT) with scaled symmetric sampling strategy is introduced into Geomatics and expanded. Unlike the Taylor series expansion, this method treats nonlinear error propagation without a ...

Abstract: Estimating the accuracy of source geometry parameters is helpful for understanding error propagation in a nonlinear inversion. To date, no studies have estimated the nonlinear inversion accuracy of source geometry parameters. ...

Abstract: The mixed additive and multiplicative random error model is a combination of the additive error random model and multiplicative random error model. Weakness is an attribute of the mixed additive and multiplicative random ...

Abstract: To further improve the quality of estimated values based on variance component estimation of the partial errors-in-variables (EIV) model, the jackknife resampling method is introduced in this paper. Focusing on the bias ...