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18,646
General State-Space Models
, 2011
"... State-space 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 ..."
Abstract
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State-space 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
Switching State-Space 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 ..."
Abstract
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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
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 ..."
Abstract
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Cited by 3 (1 self)
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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
Nonlinear State-Space Models with State-Dependent Variances
- JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2001
"... Nonlinear state-space models with state dependent variances (SDV) are commonly used in financial... ..."
Abstract
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Cited by 17 (5 self)
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Nonlinear state-space models with state dependent variances (SDV) are commonly used in financial...
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 ∗ ..."
Abstract
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Cited by 2 (2 self)
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State space modeling in multichannel active control systems ∗
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
Smoothing for Nonlinear and Non-Gaussian State-Space Models
, 2007
"... Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space models ..."
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Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space 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 well-known 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 well-known 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
State-space models for optical imaging
, 2006
"... Measurement of stimulus-induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging, to gather data capturing underlying signals of interest in t ..."
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words: linear state-space models, Kalman filtering, functional neuroimaging, optical imaging, orientation columns. 1
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
Results 1 - 10
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18,646
