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Resampling in State Space Models ∗
"... Abstract. Resampling the innovations sequence of state space models has proved to be a useful tool in many respects. For example, while under general conditions, the Gaussian MLEs of the parameters of a state space model are asymptotically normal, several researchers have found that samples must be ..."
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Abstract. Resampling the innovations sequence of state space models has proved to be a useful tool in many respects. For example, while under general conditions, the Gaussian MLEs of the parameters of a state space model are asymptotically normal, several researchers have found that samples must
Wavelets in State Space Models
"... In this paper we consider the utilization of wavelets in conjunction with state space models. Specifically, the parameters in the system matrix are expanded in wavelet series and estimated via the Kalman Filter and the EM algorithm. In particular this approach is used for switching models. Two appli ..."
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In this paper we consider the utilization of wavelets in conjunction with state space models. Specifically, the parameters in the system matrix are expanded in wavelet series and estimated via the Kalman Filter and the EM algorithm. In particular this approach is used for switching models. Two
General StateSpace Models
, 2011
"... Statespace models also known as Hidden Markov models are ubiquitous time series models in ecology, econometrics, engineering, statistics etc. Let fXngn1 be a latent/hidden Markov process de
ned by X1 µθ () and Xn j (Xn1 = xn1) fθ ( j xn1). We only have access to a process fYngn1 such that, condi ..."
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Statespace models also known as Hidden Markov models are ubiquitous time series models in ecology, econometrics, engineering, statistics etc. Let fXngn1 be a latent/hidden Markov process de
ned by X1 µθ () and Xn j (Xn1 = xn1) fθ ( j xn1). We only have access to a process fYngn1 such that
State space modeling in multichannel active control systems
 In ACTIVE 99: The 1999 International Symposium on Active Control of Sound and Vibration
, 1999
"... State space modeling in multichannel active control systems ∗ ..."
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Cited by 2 (2 self)
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State space modeling in multichannel active control systems ∗
Variational learning for switching statespace models
 Neural Computation
, 1998
"... We introduce a new statistical model for time series which iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time series models  hidden Ma ..."
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Cited by 173 (5 self)
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that variational approximations are a viable method for inference and learning in switching statespace models.
State Space Modeling Using SAS
 Journal of Statistical Software
, 2011
"... This article provides a brief introduction to the state space modeling capabilities in SAS, a wellknown statistical software system. SAS provides state space modeling in a few different settings. SAS/ETS, the econometric and time series analysis module of the SAS system, contains many procedures th ..."
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Cited by 4 (0 self)
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This article provides a brief introduction to the state space modeling capabilities in SAS, a wellknown statistical software system. SAS provides state space modeling in a few different settings. SAS/ETS, the econometric and time series analysis module of the SAS system, contains many procedures
Switching StateSpace Models
 King’s College Road, Toronto M5S 3H5
, 1996
"... We introduce a statistical model for times series data with nonlinear dynamics which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of those regimes. This model combines and generalizes two of the most widely used stochastic time se ..."
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Cited by 46 (2 self)
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, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact Expectation Maximization (EM) alogithm cannot be applied. However, we present a variational approximation which
Fitting State Space Models with EViews
"... This paper demonstrates how state space models can be fitted in EViews. We first briefly introduce EViews as an econometric software package. Next we fit a local level model to the Nile data. We then show how a multivariate “latent risk ” model can be developed, making use of the EViews programming ..."
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This paper demonstrates how state space models can be fitted in EViews. We first briefly introduce EViews as an econometric software package. Next we fit a local level model to the Nile data. We then show how a multivariate “latent risk ” model can be developed, making use of the EViews programming
Fitting State Space Models with EViews
"... This paper demonstrates how state space models can be fitted in EViews. We first briefly introduce EViews as an econometric software package. Next we fit a local level model to the Nile data. We then show how a multivariate “latent risk ” model can be developed, making use of the EViews programming ..."
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This paper demonstrates how state space models can be fitted in EViews. We first briefly introduce EViews as an econometric software package. Next we fit a local level model to the Nile data. We then show how a multivariate “latent risk ” model can be developed, making use of the EViews programming
Nonlinear StateSpace Models with StateDependent Variances
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2001
"... Nonlinear statespace models with state dependent variances (SDV) are commonly used in financial... ..."
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Cited by 17 (5 self)
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Nonlinear statespace models with state dependent variances (SDV) are commonly used in financial...
Results 1  10
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