RT Journal Article ID 5859e5395b9ef185 A1 Kostina, Ekaterina A1 Nattermann, Max T1 SECOND-ORDER SENSITIVITY ANALYSIS OF PARAMETER ESTIMATION PROBLEMS JF International Journal for Uncertainty Quantification JO IJUQ YR 2015 FD 2015-08-14 VO 5 IS 3 SP 209 OP 231 K1 uncertainty quantification K1 representing of uncertainty K1 inverse problems K1 parameter estimation K1 maximum likelihood K1 stochastic sensitivity analysis AB The use of model-based simulation to gain knowledge of unknown phenomena and processes behavior is a challenging task in many natural sciences. In order to get a full description of an underlying process, an important issue is to estimate unknown parameters from real but erroneous observations. Thus the whole system is affected by uncertainties and a sensitivity analysis is necessary. Usually one applies first-order sensitivity analysis and resulting linearized confidence regions to determine the statistical accuracy of the solution to parameter estimation problems. But especially in significantly nonlinear cases linearized regions may not be an adequate representation. In this paper, we suggest quadratic regions based on the second-order sensitivity analysis. The new region definition is based on a map that transforms the input uncertainties onto the parameter space. Furthermore, the approximation accuracy of the quadratic confidence regions is exemplary illustrated at two examples. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,7348f81710c4fb51,5859e5395b9ef185.html