Sequential Monte Carlo (SMC) methods represent a classical set of techniques to simulate sequence probability measures through simple selection/mutation mechanism. However, the associated selection functions and mutation kernels usually depend on tuning parameters that are first importance for efficiency algorithm. A standard way address this problem is apply adaptive sequential (ASMC) methods,...

The conditional variance function in a heteroscedastic, nonparametric regression model is estimated by linear smoothing of squared residuals. Attention is focussed on local polynomial smoothers. Both the mean and variance functions are assumed to be smooth, but neither is assumed to be in a parametric family. The eeect of preliminary estimation of the mean is studied, and a \degrees of freedom"...

The wavelet variance provides a scale-based decomposition of the process variance for a time series or a random field and has been used to analyze various multi-scale processes. Examples of such processes include atmospheric pressure, deviations in time as kept by atomic clocks, soil properties in agricultural plots, snow fields in the polar regions and brightness temperature maps of South Paci...