Non-linear/ non-Gaussian models
Fast smoothing in switching approximations of non-linear and non-Gaussian models
... Abstract Statistical smoothing in general non-linear non-Gaussian systems is a challenging problem. A new smoothing method based on approximating the original system by a recent switching ...
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Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models
... the non-parametric approach to estimate the forecast distribution for log realized volatility the aim is to capture the impact of the jump variation in a computational simple way, rather than modelling price jumps ...
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Estimation in Non Linear Non Gaussian State Space Models with Precision Based Methods
... for linear Gaussian state space models, and we present a quick method to obtain a Gaussian or a student t approximation for the condi- tional density of the states p ( j ; ...a Gaussian ...
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Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods
... for linear Gaussian state space models, and we present a quick method to obtain a Gaussian or a student t approximation for the condi- tional density of the states p ( j ; ...a Gaussian ...
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Causal Structure Learning and Effect Identification in Linear Non-Gaussian Models and Beyond
... The preferred approach to causal inference is to carry out controlled exper- iments. However, such experiments are not always possible due to ethical, financial or technical restrictions. An important problem is thus the ...
89
VAR models with non-Gaussian shocks
... the models depending on the time period in ...simpler linear BVAR model performs relatively better than our proposed models during the highly volatile 1970s and ...our models do much better ...
22
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
... canonical models is that when searching for explanations of some given data we can, without loss of generality, restrict the search to such ...canonical models. In such cases, to the extent that these ...
17
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
... We also provide visualisations of the data using the range of algorithms we reviewed in the intro- duction. In Figure 1(c) we show the result of non-metric MDS using the stress criterion of Kruskal (1964). Figure ...
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Non Gaussian structural time series models
... with and Pt/t-1 as (1-2.9a) and (1.2.9b) , respectively. A particular feature of the Gaussian-linear model is that both the measurement and predictive densities belong to the same family of distribution. ...
249
A Linear Non-Gaussian Acyclic Model for Causal Discovery
... Unfortunately, however, it is never possible to completely confirm the assumptions (and hence the found causal model) purely from observational data. Controlled experiments, where the individ- ual variables are ...
28
A Novel Stastical Particle Filtering Approach for Non Linear and Non Gaussian System Identification
... and non- Gaussianity in order to model accurately the underlying dynamics of a physical ...system models arise in various applications in control and signal ...and Gaussian Sum Filter, this approach ...
6
Emulating dynamic non-linear simulators using Gaussian processes
... of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is ...computer models simulate the evolution of some real-world phenomenon over time, for example ...
19
Non-linear evolution of the tidal angular momentum of protostructures II: non-gaussian initial conditions
... the linear angular momentum, presumably indicating that higher-order terms, not discussed in this paper, are ...in models with this kind of underlying statistic appear analytically ...lowest-order ...
20
Copula selection models for non-Gaussian responses that are missing not at random
... There are some aspects of the proposed modelling approach that have scope for improvement and provide direction for future research. Firstly, for comparative purposes we have assumed linear covariate effects ...
31
Gaussian Processes and Limiting Linear Models
... Abstract Gaussian processes (GPs) retain the linear model (LM) either as a spe- cial case, or in the ...limiting linear model (LLM) in ...partition models to yield a highly efficient ...
8
Gaussian and non Gaussian models for financial bubbles via econophysics
... Abstract We develop a rational expectations model of financial bubbles and study how the risk-return interplay is incorporated into prices. We retain the interpretation of the leading Johansen-Ledoit-Sornette model: ...
18
Gaussian and non-Gaussian models for financial bubbles via econophysics
... This paper builds on the now well-established analogy between financial crashes and phase transitions in critical phenomena. In a stochastic version of the original model of Johansen et al. (2000) crashes are seen to ...
18
Image Filtering using Linear and Non Linear Filter for Gaussian Noise
... a linear combination of neighborhood values, which can produce blur in the ...popular non-linear ...work Gaussian noise used and image filtering performed by Linear and Non ...
6
Non linear models: applications in economics
... how non-linear modelling can be useful to investigate the behavioural of dynamic economic ...adequate non-linear models could be a good way to find more refined solutions to actually ...
29
Tracking with Non-Linear Dynamic Models
... FIGURE 2.6: If a weak motion model is used to track a person with a particle filter, the likelihood function can create serious problems. This is because the state is high- dimensional, and there are many local peaks in ...
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