Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Comparison of iterative algorithms for parameter estimation in nonlinear regression
Download
index.pdf
Date
2018
Author
Musluoğlu, Gamze
Metadata
Show full item record
Item Usage Stats
366
views
121
downloads
Cite This
Nonlinear regression models are more common as compared to linear ones for real life cases e.g. climatology, biology, earthquake engineering, economics etc. However, nonlinear regression models are much more complex to fit and to interpret. Classical parameter estimation methods such as least squares and maximum likelihood can also be adopted to fit the model in nonlinear regression as well, but explicit solutions can not be achieved unlike linear models. At this point, iterative algorithms are utilized to solve the problem numerically. Since there is no extensive study which compiles, classifies and compares the existing methods for nonlinear parameter estimation, the objective of this study is to fill this gap. In our study, we aim to compile the methods which are used for nonlinear parameter estimation purpose and compare them with respect to several criteria such as bias, execution time, number of iterations etc. The comparison will be conducted considering different scenarios which are small vs. large sample sizes, good vs. poor initial values, normal vs. non-normal error terms, simple vs complex models (with respect to number of parameters), and robustness. Both real and simulated data are used in the comparative study.
Subject Keywords
Regression analysis.
,
Nonlinear theories.
,
Computer algorithms.
URI
http://etd.lib.metu.edu.tr/upload/12622525/index.pdf
https://hdl.handle.net/11511/27619
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Structured neural networks for modeling and identification of nonlinear mechanical systems
Kılıç, Ergin; Dölen, Melik; Koku, Ahmet Buğra; Department of Mechanical Engineering (2012)
Most engineering systems are highly nonlinear in nature and thus one could not develop efficient mathematical models for these systems. Artificial neural networks, which are used in estimation, filtering, identification and control in technical literature, are considered as universal modeling and functional approximation tools. Unfortunately, developing a well trained monolithic type neural network (with many free parameters/weights) is known to be a daunting task since the process of loading a specific pat...
Development of a model updating technique for nonlinear systems
Canbaloğlu, Güvenç; Özgüven, Hasan Nevzat; Ünver, Hakkı Özgür; Department of Mechanical Engineering (2015)
In structural dynamics, obtaining an accurate numerical model is very crucial. However there are usually discrepancies between calculated dynamic behavior from numerical models and the ones obtained experimentally, and therefore it will be necessary to update the numerical models. In real life applications, structures usually have nonlinearity, and for nonlinear structures, in order to update the numerical model, firstly nonlinearity in the structure can be identified, and then updating procedure may be app...
Fuzzy versus statistical linear regression
Kim, KJ; Moskowitz, H; Köksalan, Mustafa Murat (1996-07-19)
Statistical linear regression and fuzzy linear regression have been developed from different perspectives, and thus there exist several conceptual and methodological differences between the two approaches. The characteristics of both methods, in terms of basic assumptions, parameter estimation, and application are described and contrasted. Their descriptive and predictive capabilities are also compared via a simulation experiment to identify the conditions under which one outperforms the other. It turns out...
A new contribution to nonlinear robust regression and classification with mars and its applications to data mining for quality control in manufacturing
Yerlikaya, Fatma; Weber, Gerhard Wilhelm; Department of Scientific Computing (2008)
Multivariate adaptive regression spline (MARS) denotes a modern methodology from statistical learning which is very important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. MARS is very useful for high dimensional problems and shows a great promise for fitting nonlinear multivariate functions. MARS technique does not impose any particular class of relationship between the predictor variables and outcome variable of interest....
Modeling, inference and optimization of regulatory networks based on time series data
Weber, Gerhard Wilhelm; DEFTERLİ, ÖZLEM; ALPARSLAN GÖK, Sırma Zeynep; Kropat, Erik (2011-05-16)
In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by variou...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
G. Musluoğlu, “Comparison of iterative algorithms for parameter estimation in nonlinear regression,” M.S. - Master of Science, Middle East Technical University, 2018.