The Maximum likelihood estimation (MLE) is the most widely used method to estimate the parameters of a GARCH(p,q) process. This is owed to the fact that the MLE, among other properties, is asymptotically efficient. Even though the MLE is sensitive to outliers, which can occur in time series. In...

The Maximum likelihood estimation (MLE) is the most widely used method to estimate the parameters of a GARCH(p,q) process. This is owed to the fact that the MLE, among other properties, is asymptotically efficient. Even though the MLE is sensitive to outliers, which can occur in time series. In...

<Para ID="Par1">We motivate this paper by showing through Monte Carlo simulation that ignoring the skewness of the response variable distribution in non-linear regression models may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. Then, we propose a...</para>

We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full...

In this paper we discuss estimation and diagnostic procedures in semiparametric additive models with symmetric errors in order to permit distributions with heavier and lighter tails than the normal ones, such as Student-t, Pearson VII, power exponential, logistics I and II, and contaminated...

Normalization of expression levels applied to microarray data can help in reducing measurement error. Different methods, including cyclic loess, quantile normalization and median or mean normalization, have been utilized to normalize microarray data. Although there is considerable literature...