Iterative Process of Estimation
Publication: Journal of Surveying Engineering
Volume 146, Issue 3
Abstract
estimation allows us to estimate competitive parameters, namely different versions of the parameter vector within the split functional model. In the univariate model, such parameters can be regarded as location parameters for different observation aggregations. The whole observation set might be an unrecognized mixture of observations that belong to such aggregations. There are two main variants of estimation: the squared and absolute estimations, which differ from each other in objective functions. The estimation process is always an iterative one, irrespective of the estimation variant. This paper addresses the main practical problem in such a context, namely the choice of the starting point and its possible influence on the estimation results. The paper shows that this issue is important; it also proposes the best choice that guarantees the correct solutions of the optimization problem. The authors also consider two types of iterative processes and conclude that the traditional iterative process is recommended for squared estimation, whereas the parallel process is suitable for absolute estimation.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request: the simulation results, code of simulation, and estimation computations for Mathcad version 15.0.
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©2020 American Society of Civil Engineers.
History
Received: Sep 11, 2019
Accepted: Jan 3, 2020
Published online: Apr 3, 2020
Published in print: Aug 1, 2020
Discussion open until: Sep 3, 2020
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