In a binary response regression model, classical residuals are diicult to deene and interpret due to the discrete nature of the response variable. In contrast , Bayesian residuals have continuous-valued posterior distributions which can be graphed to learn about outlying observations. Two deenitions of Bayesian residuals are proposed for binary regression data. Plots of the posterior distributi...

ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...

Direct Neural Network Residual Kriging (DNNRK) is a two step algorithm (Kanevsky et al. 1995). The first step includes estimating large scale structures by using artificial neural networks (ANN) with simple sum of squares error function. ANN, being universal approximators, model overall non-linear spatial pattern fairly well. ANN are model free estimators and depend only on their architecture a...

Types of land disposed residuals from selected metal smelting and refining industries are described, as are the origin and disposition of land disposed residuals from the primary copper industry as an example. Quantities of land-disposed or stored residuals, including slags, sludges, and dusts, are given per unit of metal production for most primary and secondary metal smelting and refining ind...

In a binary response regression model, classical residuals are diicult to deene and interpret due to the discrete nature of the response variable. In contrast, Bayesian residuals have continuous-valued posterior distributions which can be graphed to learn about outlying observations. Two deenitions of Bayesian residuals are proposed for binary regression data. Plots of the posterior distributio...