A non parametric Auto-Regressive Conditional Heteroscedastic model for financial returns series is considered in which the conditional mean and volatility functions are estimated non-parametrically using Nadaraya Watson kernel. test statistic unknown abrupt change point takes into consideration heteroskedasticity, dependence, heterogeneity fourth moment of returns, since kurtosis a function con...

This study investigates the daily co-movements in commodity prices over period 2006–2020 using a novel approach based on time-varying Gerber correlation. The statistic is computed considering set of probabilities estimated via non-traditional models that give structure to measure. results indicate there are several across commodities, these change time, and they tendentially positive. Condition...

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on ...

A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don’t have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is...

A general approach for modeling the volatility process in continuous-time is based on the convolution of a kernel with a non-decreasing Lévy process, which is non-negative if the kernel is non-negative. Within the framework of Continuous-time Auto-Regressive Moving-Average (CARMA) processes, we derive a necessary condition for the kernel to be non-negative, and propose a numerical method for ch...

The Intra-Voxel Incoherent Motion (IVIM) model allows to estimate water diffusion and perfusion-related coefficients in biological tissues using weighted MR images. Among the available approaches fit IVIM bi-exponential decay, a segmented Bayesian algorithm with Conditional Auto-Regressive (CAR) prior spatial regularization has been recently proposed produce more reliable coefficient estimation...

Estimation of the long-term health effects of air pollution is a challenging task, especially when modelling small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial correlation structure in these data, which is accounted for using random effects modelled by a globally smooth conditional autoregressive model. These smooth random...