Mixture estimation with state-space components and Markov model of switching
نویسندگان
چکیده
منابع مشابه
Mixture Estimation with State-Space Components and Markov Model of Switching
The paper proposes a recursive algorithm for estimation of mixtures with state-space components and a dynamic model of switching. Bayesian methodology is adopted. The main features of the presented approach are: (i) recursiveness that enables a real-time performance of the algorithm; (ii) one-pass elaboration of the data sample; (iii) dynamic nature of the model of switching active components; ...
متن کاملState Space Markov Switching Models Using Wavelets
We propose a state space model with Markov switching, whose regimes are associated with the model parameters and regime transition probabilities are time-dependent. The estimation is based on maximum likelihood method using the EM algorithm. The distribution of the estimators is assessed using bootstrap. To evaluate the state variables and regime probabilities, the Kalman filter and a probabili...
متن کاملOnline State Space Model Parameter Estimation in Synchronous Machines
The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is...
متن کاملA General Autoregressive Model with Markov Switching: Estimation and Consistency
In this paper, a general autoregressive model with Markov switching is considered, where the autoregression may be of an infinite order. The consistency of the maximum likelihood estimators for this model is obtained under regular assumptions. Examples of finite and infinite order Markov switching AR models are discussed. The simulation study with these examples illustrates the consistency and ...
متن کاملFads Models with Markov Switching Hetroskedasticity: decomposing Tehran Stock Exchange return into Permanent and Transitory Components
Stochastic behavior of stock returns is very important for investors and policy makers in the stock market. In this paper, the stochastic behavior of the return index of Tehran Stock Exchange (TEDPIX) is examined using unobserved component Markov switching model (UC-MS) for the 3/27/2010 until 8/3/2015 period. In this model, stock returns are decomposed into two components; a permanent componen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Mathematical Modelling
سال: 2013
ISSN: 0307-904X
DOI: 10.1016/j.apm.2013.05.038