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We consider the residual empirical process in random design regression with long memory errors. We establish its limiting behaviour, showing that its rates of convergence are different from the rates ...
One of the key challenges in sensor networks is the extraction of information by fusing data from a multitude of distinct, but possibly unreliable sensors. Recovering information from the maximum numb...
This paper deals with the nonparametric density estimation of the regression error term assuming its independence with the covariate. The difference between the feasible estimator which uses the estim...
The inspection of residuals is a fundamental step to investigate the quality of adjustment of a parametric model to data. For spatial point processes, the concept of residuals has been recently prop...
The question whether a time series behaves as a random walk or as a stationary process is an important and delicate problem, particularly arising in financial statistics, econometrics, and engineeri...
Under the symmetric a-stable distributional assumption for the disturbances, Blattberg and Sargent [3] consider unbiased linear estimators for a regression model with non-stochastic regressors. We ...
Various methods to control the influence of a covariate on a response variable are compared. In particular,ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal-Wallis (K-W) tests...

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