Diagnostics for normal errors in regression currently utilize ordinary residuals, despite the failure of assumptions validating their use. Case studies here show that such misuse may be critical even in samples of size exceeding currently accepted guidelines. A remedy is to employ recovered errors having the required properties.

In regression analysis, model misspecification can produce spurious spatial correlation in the residuals. By means of Monte Carlo simulations, I show that the RESET test can help to disentangle this conundrum in large samples. Small samples can pose a serious challenge to finding the correct model.

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and leptokurtic caused by the nature of energy data. In this paper we propose a novel approach to estimate the time-varying conditional variance of th...

Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for model selection in ensembles of Neural Networks, in which we propose the use of REC curves in order to sele...

A two-phased method for prediction in spatialtemporal domains is proposed. After an ordinary regression model is trained on spatial data, a prediction is adjusted by incorporating autoregressive modeling of residuals in time. The prediction accuracy of the proposed method is evaluated on simulated agricultural data with a significant improvement of accuracy for both linear and non-linear regres...

Usually in multivariate regression problem it is assumed that residuals of outputs are independent of each other. In many applications a more realistic model would allow dependencies between the outputs. In this paper we show how a Bayesian treatment using Markov Chain Monte Carlo (MCMC) method can allow for a full covariance matrix with Multi Layer Perceptron (MLP) neural networks.

HAC estimators are known to produce test statistics that reject too frequently in finite samples. One neglected reason comes from using the OLS residuals when constructing the HAC estimator. If the regression matrix contains high leverage points, such as from outliers, then the OLS residuals will be negatively biased. This reduces the variance of the OLS residuals and the HAC estimator takes th...

BACKGROUND Regression models for survival data have traditionally been based on the Cox regression model. However, its validity relies heavily on assumption of proportional hazards. Another restriction of the Cox model is insufficiency in dealing with time-varying covariate effects, since the regression coefficients are assumed constant. These weaknesses have generated interest in alternative a...

For the final reduced multivariate Cox proportional hazards model presented in Table 3 of the main text, we assessed the proportional hazards assumption via the Schoenfeld residuals. We examined plots of the residuals for each variable against time to check for a non-­‐zero slope, which indicates that the proportional hazards assumption is violated (Hosmer et al. 1999). We then performed statis...