Segmenting the acoustic signal in the TIMIT database by a switching state Kalman filter model is reported in this paper. According to the assumption that the high dimensional acoustic feature vector of the LSF (Line Spectrum Frequency) of the speech signal is probably embedded in a low dimensional space, a two dimensional vector is used to represent the continuous state vector in this model. Th...

We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a reproducing kernel. We place a mixture of a point-mass distribution and Silverman’s g-prior on the regression vector of a generalized kernel model (GKM). This mixture prior allows a fraction of the components of the regres...

The sound and sustainable development of the international monetary system is cornerstone stable global economy. This paper takes digital currency in China as its research object utilizes a regime-switching transition auto-regression (STAR) model nonlinear time-varying parameter–stochastic volatility–vector auto regression (TVP-SV-VAR) to empirically analyze relationship between RMB, RMB intern...

Abstract Minimizing bounds of leave-one-out (loo) errors is an important and efficient approach for support vector machine (SVM) model selection. Past research focuses on their use for classification but not regression. In this article, we derive various loo bounds for support vector regression (SVR) and discuss the difference from those for classification. Experiments demonstrate that the prop...

Statistical modelling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. We develop nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik’s 2-insensitive loss function, based on reprodu...