[PDF] Top 20 Maximum likelihood estimators in linear regression models with Ornstein Uhlenbeck process
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Maximum likelihood estimators in linear regression models with Ornstein Uhlenbeck process
... This process is now widely used in many areas of ...the Ornstein-Uhlenbeck process is the tendency to return towards the long-term equilib- rium ...the Ornstein-Uhlenbeck ... See full document
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Maximum likelihood estimators of a long memory process from discrete observations
... these models with long memory and self-similarity requires efficient and accurate synthesis of discrete ...space models and Kalman filter estimators cannot be applied to the parameters of these ... See full document
18
Asymptotic law of limit distribution for fractional Ornstein Uhlenbeck process
... In case of diffusion type processes driven by fractional Brownian motions, a popular method is the maximum likelihood estimators (MLE). The MLE of the drift parameter has also been extensively studied ... See full document
7
Maximum Likelihood Estimators for a Supercritical Branching Diffusion Process
... Diffusion Process under several conditions assuring existence and uniqueness of the diffusion part and nonexplosion of the branching ...the process and its local characteristics, a Girsanov-type result is ... See full document
21
Conditionally unbiased bounded influence estimation in general repression models, with applications to generalized linear models
... (1.3) In linear and generalized linear regression, maximum likelihood estimators are conditionally Fisher consistent whenever the distribution of 2: does not depend on 9.. Conditiunal Fi[r] ... See full document
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Review Regression Models with Stochastic Regressors: An Expository note
... the linear regression model the regressors are traditionally assumed to be non stochastic, likewise it is often assumed that the random error is normally ...Modified maximum likelihood ... See full document
11
Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation
... acoustic models with a small corpus of Spanish and Valencian, which has produced poor results due to the lack of ...acoustic models that are trained with a large corpus of a language inr order to obtain ... See full document
5
Distributions of Maximum Likelihood Estimators and Model Comparisons
... Consider the probability density function of the MLE from a specified statistical model. Inferences about the parameters from sample data can be based on descrip- tors of this density. For example, the mean of the ... See full document
7
An application of Ornstein Uhlenbeck process to commodity pricing in Thailand
... In this work, we used the sum squared error to test our model when we used the three techniques to estimate λ. In Table , we can see that the jackknife technique is appropriate to estimate λ for BHMR and RSS pricing, ... See full document
10
Minimum distance estimation for fractional Ornstein Uhlenbeck type process
... either maximum likelihood estimation (MLE) or least square estima- tion ...simple linear model driven by a fractional Brownian motion was studied in [] in the continuous ...the ... See full document
8
Maximum likelihood estimation for multiscale Ornstein Uhlenbeck processes
... observation process as component of a multiscale process, converging in some limit to an OU ...the estimators corresponding to multiscale and approximate Ornstein-Uhlenbeck (OU) ... See full document
28
Calibration of the exponential Ornstein–Uhlenbeck process when spot prices are visible through the maximum log likelihood method Example with gold prices
... arithmetic Ornstein–Uhlenbeck ...exponential Ornstein– Uhlenbeck process and obtained the calibration equations ...this process through the log-likelihood approach has not ... See full document
14
Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling
... (i.e., estimators converge to the true parameter values as sample size tends to infinity if the model is true), because it yields the observed case-control ratio in ...pseudo likelihood or conditional like- ... See full document
30
Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model
... Berner, J., Achatz, U., Batte, L., Bengtsson, L., Cámara, A. d. L., Christensen, H. M., Colangeli, M., Coleman, D. R., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., ... See full document
27
Comparison of Maximum Likelihood Estimators and Regression Models in Mediterranean Forests Fires for Severity Mapping Using Landsat TM and ETM+ Data
... time using remotely sensed spectral indices imagery from Landsat data in a Mediterranean 481. ecosystem[r] ... See full document
22
Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
... autoregressive. Also, a method has been given for choosing the Liu biasing parameter. Furthermore, some diagnostic measures are studied to identify influential observations or outliers that may be involved in the data ... See full document
16
Empirical Likelihood Diagnosis of Modal Linear Regression Models
... Zhu and Ibrahim [17] utilized this method for statistical diagnostic, and they developed diagnostic measures for assessing the influence of individual observations when using empirical l[r] ... See full document
6
Robustness of maximum likelihood estimates for mixed Poisson regression models
... Gustafson (1996) used an influence function approach (Hampel et al., 1986) (Huber, 1981) to examine the robustness of maximum likelihood estimates for certain conjugate mixture models un[r] ... See full document
23
Exponential Ergodicity and β Mixing Property for Generalized Ornstein Uhlenbeck Processes
... generalized Ornstein-Uhlenbeck process is derived from a bivariate Lévy process and is suggested as a continuous time version of a stochastic recurrence equation ...generalized ... See full document
5
The Simulation and Approximation of the First Passage Time of the Ornstein--Uhlenbeck Process of Neuron
... When σ is 0.5, Figures 4.14 and 4.15 show that the mean and variance approximated using the Stein’s method are very close to the simulated results. This is true except when the threshold is 14 mvolt. This tells us the ... See full document
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