Regression Models for Non-Gaussian Data
Gaussian process regression for binned data
... Finally, in Section 4, we looked at approximation methods for non-cuboid, disjoint input regions. First we implemented the point-based approximation of Kyr- iakidis (2004). Although it did not achieve a particu- ...
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Multivariate Regression with Incremental Learning of Gaussian Mixture Models
... redundant non-linear maps by the cumulative acquisition of data from input-output ...from data batches without the need of keeping them in memory ...Incremental Gaussian Mixture ...of ...
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VAR models with non-Gaussian shocks
... the models depending on the time period in ...proposed models during the highly volatile 1970s and ...our models do much better during the relatively more tranquil periods such as the Great ...
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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: ...
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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 ...
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An approach for constructing parsimonious generalized Gaussian kernel regression models
... generalized Gaussian kernel model, in which each kernel regressor has an individually tuned diagonal covariance ...kernel regression model has the potential of improving modeling capability and producing ...
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Scalable Hierarchical Gaussian Process Models for Regression and Pattern Classification
... 5.1. Introduction unstructured environments, where the pedestrian lanes can have arbitrary sur- faces with no painted markers. In this approach, a deep learning architecture is proposed to segment a scene image into lane ...
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Training Gaussian Process Regression Models Using Optimized Trajectories
... 5.3 Experimental Results The Pelican quadrotor described in Section 4.1.2 was used to test the trajectory opti- mization experimentally. The quadrotor flew both randomly generated trajectories and trajectories where the ...
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Bivariate Gaussian models for wind vectors in a distributional regression framework
... the regression coefficients for the correlation parameter offline in a pre-processing step for a separate year, either for a single site or a group of ...univariate models are fitted for the components of ...
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Non Gaussian structural time series models
... a Gaussian model could not be regarded as a reasonable ...a Gaussian model can be fitted to the logarithms of the ob s e r v a t i o n s ...count data model. However, it is clear from doing this that ...
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Gaussian process functional regression modelling for batch data.
... 1 Introduction We begin by discussing a motivating example. The application concerns data collected during standing-up manoeuvres of paraplegic patients. The outputs are the trajectories of the body centre of mass ...
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Regression With Gaussian Measures
... the data because this is the standard in regression ...of data and predictions which can be articulated in terms of events involving finitely many continuous linear functionals on ...
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Testing Gaussian and Non-Gaussian Break Point Models: V4 Stock Markets
... analyzed Gaussian and Non-Gaussian break point models in a case of stock markets in V4 ...recommend Non-Gaussian models as more suitable for a detection of structural ...
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Computationally Efficient Estimation of Non-stationary Gaussian Process Models for Large Spatial Data.
... In computing candidate partitions, when the base partition has equal blocks, it is possible to vectorize the likelihood function so that it can be efficiently estimated, and its base partition parameter estimates need ...
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High-Dimensional Non-Gaussian Data Clustering using Variational Learning of Mixture Models
... complex models and then causes over-fitting and pure Bayesian tech- niques which penalizes complex models, but unfortunately require intensive computations and are generally ...
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Testing the Specification of Econometric Models in Regression and Non-Regression Directions
... of regression models is easy to ...The regression specification is, however, very ...their models in non-regression as well as regression ...above, regression ...
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Distributional results for thresholding estimators in high dimensional Gaussian regression models
... Distributional results for thresholding estimators in high-dimensional Gaussian regression models. Pötscher, Benedikt M[r] ...
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Distributional results for thresholding estimators in high-dimensional Gaussian regression models
... Abstract We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sam- ple size n and may diverge with n. ...
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Fast smoothing in switching approximations of non-linear and non-Gaussian models
... triplet models called “stationary conditionally Gaussian observed Markov switching models” (SCGOMSMs) (see ...switching models” (CMSHLMs) in which fast filtering is feasible [9], and we show ...
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Bayesian Estimation of Non Gaussian Stochastic Volatility Models
... with non-Gaussian ...suitable non-Gaussian distribution error for the data base considered in our study among different non-Gaussian distribution that has been considered ...
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