… shows spatially nonstationary feature. How to appropriately specify a nonstationary covariance
function for inherent spatial … based on an eigen decomposition to model the underlying …

… vector regression (SVR) model and the strong non-stationary … -EMD-SVR model for the
short-term forecast of nonlinear and … 6 gives the covariance function and the expected values of …

… those of GLMs and models based on generalized estimating … a method that allows for spatial
nonstationarity. The technique … the model, we present an algorithm to estimate regression …

… Building hierarchical spatial orders for environmental data is impracticable because they
have nonlinear and nonstationary spatial trends and stronger spatial correlation, and they are …

… Investigating spatial non-stationary and scale-dependent relationships between urban
surface temperature and environmental factors using geographically weighted regression [J]. …

… model is established based on the algorithms of least square support vector regression (…
study through the identification of non-stationary energy consumption and the identification …

… In this paper we think that the similarity of random non-stationary time series data shows the
local similarity of series much … When a time-varying index is smooth, the covariance function …

… The model performance was evaluated by a linear regression between predictions and
observations and quantified by determination coefficients (R2), regressive ecoefficiency (slope). …

… , in other words volatility, from a nonlinear nonstationary time series viewpoint. Much of the
… Instead, we apply an extension of the study of the nonlinear regression with integrated time …

… (Nonstationary)… model is recommended. The covariates are limited, but when the sample
density is high, the random forest-regression kriging model may be a good choice for spatial …