We simplify simplicial depth for regression and autoregressive growth processes in two directions. At first we show that often simplicial depth reduces to counting the subsets with alternating signs of the residuals. The second simplification is given by not regarding all subsets of residuals. By consideration of only special subsets of residuals, the asymptotic distributions of the simplified ...

Let {Y :n ~ o} be a Harris-recurrent Markov chain on a general state n space. It is shown that {Y J is strong mixing, provided there exists a n stationary probability distribution ~(.) for {Y}. We use this result to n establish that certain stationary autoregressive moving average processes are strong mixing. Necessary and sufficient conditions for a first order autoregressive process to be uni...

Autoregressive models are commonly employed to analyze empirical time series. In practice, however, any autoregressive model will only be an approximation to reality and in order to achieve a reasonable approximation and allow for full generality the order of the autoregression, h say, must be allowed to go to infinity with T , the sample size. Although results are available on the estimation o...

In what concerns extreme values modeling, heavy tailed autoregressive processes defined with the minimum or maximum operator have proved to be good alternatives to classical linear ARMA with heavy tailed marginals (Davis and Resnick [8], Ferreira and Canto e Castro [13]). In this paper we present a complete characterization of the tail behavior of the autoregressive Pareto process known as Yeh–...