To estimate cell probabilities for ordered sparse contingency tables several smooth- ing techniques have been investigated. It has been recognized that nonparametric smoothing methods provide estimators of cell probabilities that have better performance than the pure frequency estimators. With...

The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods are based on the sample forecast errors. In this...

The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods are based on the sample forecast errors. In this...

To estimate cell probabilities for ordered sparse contingency tables several smooth- ing techniques have been investigated. It has been recognized that nonparametric smoothing methods provide estimators of cell probabilities that have better performance than the pure frequency estimators. With...

To estimate cell probabilities for ordered sparse contingency tables several smoothing techniques have been investigated. It has been recognized that nonparametric smoothing methods provide estimators of cell probabilities that have better performance than the pure frequency estimators. With the...

The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods are based on the sample forecast errors. In this...