Various parameter estimation techniques for stochastic differential equations

Download
2019
Ergişi, Semi
Dynamic systems appear in many fields from economics to physics, from biology toengineering include randomness. Therefore, stochastic differential equations are oneof the necessary mathematical tools to model dynamic systems in these disciplines.In this study, we propose two parameter estimation methods when modelling withSDEs which are driven by Brownian motion. Maximum likelihood estimation andgeneralized method of moment techniques are used to estimate parameters and it isobtained that when the assumptions for Brownian motion satisfy, both techniques givethe same result.

Suggestions

A GENERALIZED CORRELATED RANDOM WALK APPROXIMATION TO FRACTIONAL BROWNIAN MOTION
Vardar Acar, Ceren (null; 2018-04-30)
In this study, we mainly propose an algorithm to generate correlated random walk converging to fractional Brownian motion, with Hurst parameter, H∈ [1/2,1]. The increments of this random walk are simulated from Bernoulli distribution with proportion p, whose density is constructed using the link between correlation of multivariate Gaussian random variables and correlation of their dichotomized binary variables. We prove that the normalized sum of trajectories of this proposed random walk yields a Gaussian p...
Parameter estimation in generalized partial linear models with Tikhanov regularization
Kayhan, Belgin; Karasözen, Bülent; Department of Scientific Computing (2010)
Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the e...
An exponential big bang-big crunch algorithm for discrete design optimization of steel frames
Hasançebi, Oğuzhan (2012-11-01)
In the present study an enhanced variant of the big bang-big crunch (BB-BC) technique, namely exponential BB-BC algorithm (EBB-BC) is developed for code based design optimization of steel frame structures. It is shown that the standard version of the BB-BC algorithm is sometimes unable to produce reasonable solutions to problems from discrete design optimization of steel frames. Hence, through investigating the shortcomings of BB-BC algorithm, it is aimed to reinforce the performance of the technique for th...
A deep learning approach for the transonic flow field predictions around airfoils
Duru, Cihat; Alemdar, Hande; Baran, Özgür Uğraş (2022-01-01)
Learning from data offers new opportunities for developing computational methods in research fields, such as fluid dynamics, which constantly accumulate a large amount of data. This study presents a deep learning approach for the transonic flow field predictions around airfoils. The physics of transonic flow is integrated into the neural network model by utilizing Reynolds-averaged Navier–Stokes (RANS) simulations. A detailed investigation on the performance of the model is made both qualitatively and quant...
A finite field framework for modeling, analysis and control of finite state automata
Reger, Johann; Schmidt, Klaus Verner (Informa UK Limited, 2004-09-01)
In this paper, we address the modeling, analysis and control of finite state automata, which represent a standard class of discrete event systems. As opposed to graph theoretical methods, we consider an algebraic framework that resides on the finite field F-2 which is defined on a set of two elements with the operations addition and multiplication, both carried out modulo 2. The key characteristic of the model is its functional completeness in the sense that it is capable of describing most of the finite st...
Citation Formats
S. Ergişi, “Various parameter estimation techniques for stochastic differential equations,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Statistics., Middle East Technical University, 2019.