The lasso procedure is an estimator-shrinkage and variable selection method. This paper shows that there always exists an interval of tuning parameter values such that the corresponding mean squared prediction error for the lasso estimator is smaller than for the ordinary least squares...

In this paper we consider the non-linear time series model xt=[var epsilon]t([alpha]0+[alpha]1xt-1r[beta]+...+[alpha]pxt-pr[beta])1/r. When r = 2 it is called the [beta]-ARCH(p) model. We examine the geometric ergodicity and the existence of higher-order moments for this model.

This paper proposes an estimation method for superposed spatial point patterns of Neyman–Scott cluster processes of different distance scales and cluster sizes. Unlike the ordinary single Neyman–Scott model, the superposed process of Neyman–Scott models is not identified solely by the...