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Uniform approximation of the CoxIngersollRoss process
, 2014
"... The DossSussmann (DS) approach is used for uniform simulation of the CoxIngersollRoss (CIR) process. The DS formalism allows to express trajectories of the CIR process through solutions of some ordinary differential equation (ODE) depending on realizations of a Wiener process involved. By simula ..."
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The DossSussmann (DS) approach is used for uniform simulation of the CoxIngersollRoss (CIR) process. The DS formalism allows to express trajectories of the CIR process through solutions of some ordinary differential equation (ODE) depending on realizations of a Wiener process involved
Uniform approximation of the CoxIngersollRoss process
, 2013
"... The DossSussmann (DS) approach is used for uniform simulation of the CoxIngersollRoss (CIR) process. The DS formalism allows to express trajectories of the CIR process through solutions of some ordinary differential equation (ODE) depending on realizations of a Wiener process involved. By simula ..."
Abstract
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The DossSussmann (DS) approach is used for uniform simulation of the CoxIngersollRoss (CIR) process. The DS formalism allows to express trajectories of the CIR process through solutions of some ordinary differential equation (ODE) depending on realizations of a Wiener process involved
MAXIMUM LIKELIHOOD ESTIMATION OF THE COXINGERSOLLROSS PROCESS: THE MATLAB IMPLEMENTATION
"... The square root diffusion process is widely used for modeling interest rates behaviour. It is an underlying process of the wellknown CoxIngersollRoss term structure model (1985). We investigate maximum likelihood estimation of the square root process (CIR process) for interest rate time series. T ..."
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The square root diffusion process is widely used for modeling interest rates behaviour. It is an underlying process of the wellknown CoxIngersollRoss term structure model (1985). We investigate maximum likelihood estimation of the square root process (CIR process) for interest rate time series
Gaussian processes for machine learning
 in: Adaptive Computation and Machine Learning
, 2006
"... Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperpar ..."
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Cited by 631 (2 self)
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Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
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Cited by 678 (4 self)
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) finding the parameters of a hidden Markov model (HMM) (i.e., the BaumWelch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical
Wiener chaos and the CoxIngersollRoss model
 Lond, A
, 2005
"... In this paper we recast the Cox–Ingersoll–Ross (CIR) model of interest rates into the chaotic representation recently introduced by Hughston and Rafailidis. Beginning with the ‘squared Gaussian representation ’ of the CIR model, we find a simple expression for the fundamental random variable X∞. By ..."
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Cited by 6 (0 self)
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In this paper we recast the Cox–Ingersoll–Ross (CIR) model of interest rates into the chaotic representation recently introduced by Hughston and Rafailidis. Beginning with the ‘squared Gaussian representation ’ of the CIR model, we find a simple expression for the fundamental random variable X
The Wiener chaos expansion for the Cox–Ingersoll–Ross model
"... In this paper we recast the Cox–Ingersoll–Ross model of interest rates into the chaotic representation recently introduced by Hughston and Rafailidis. Beginning with the “squared Gaussian representation” of the CIR model, we find a simple expression for the fundamental random variable X∞. By use of ..."
Abstract
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In this paper we recast the Cox–Ingersoll–Ross model of interest rates into the chaotic representation recently introduced by Hughston and Rafailidis. Beginning with the “squared Gaussian representation” of the CIR model, we find a simple expression for the fundamental random variable X∞. By use
MultiFactor CoxIngersollRoss Models of the Term Structure: Estimates and Tests from a Kalman Filter Model
, 1995
"... This paper presents a method for estimating multifactor versions of the Cox, Ingersoll, Ross (1985b) model of the term structure of interest rates. The fixed parameters in one, two, and three factor models are estimated by applying an approximate maximum likelihood estimator in a statespace model ..."
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Cited by 62 (0 self)
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This paper presents a method for estimating multifactor versions of the Cox, Ingersoll, Ross (1985b) model of the term structure of interest rates. The fixed parameters in one, two, and three factor models are estimated by applying an approximate maximum likelihood estimator in a statespace model
Blind Beamforming for Non Gaussian Signals
 IEE ProceedingsF
, 1993
"... This paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resorting to their hypothesized value. By using estimates of the directional vectors obtained via blind identification i.e. without knowing the arrray mani ..."
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Cited by 704 (31 self)
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This paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resorting to their hypothesized value. By using estimates of the directional vectors obtained via blind identification i.e. without knowing the arrray
Results 1  10
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