Two-stage Procedure in P-Order Autoregressive Process

Autoregressive process modeling via the Lasso procedure

The Lasso is a popular model selection and estimation procedure for linear models that enjoys nice theoretical properties. In this paper, we study the Lasso estimator for fitting autoregressive time series models. We adopt a double asymptotic framework where the maximal lag may increase with the sample size. We derive theoretical results establishing various types of consistency. In particular,...

Mean Queue Size in a Queue With Discrete Autoregressive Gaussian Process Arrivals of Order P

The class of Gaussian processes is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space, or time and space. The popularity of such processes stems primarily from two essential properties. First, a Gaussian process is completely determined by its mean and covariance functions. This property facilitates model fitting as only the fir...

An Autoregressive Order 1 Process with Time Varying Coefficient

( ) ( ) ( ) t t / L L e 1 1 e t + − = ρ and 1 Y L − + = t t β α , that is, we investigate the nonlinear least squares estimator. Starting with the simplest case 0 = β , we find that ( ) ( ) ( ) 1 e 1 e t + − = α α ρ / which is just a constant so the estimator that minimizes the error sum of squares must be ( ) ( ) ( ) ρ ρ α ˆ / ˆ ˆ − + = 1 1 ln where ρ̂ is the usual regression estimate of (the c...

A bivariate first-order signed integer-valued autoregressive process

Autoregressive Gaussian Random Vectors of First Order

An Enhanced Two - Stage Selection Procedure

This paper discusses implementation of a two-stage procedure to determine the simulation run length for selecting the best of k designs. We purpose an Enhanced Two-Stage Selection (ETSS) procedure. The number of additional replications at the second stage for each design is determined by both the variances of the sample means and the differences of the sample means of alternative designs. We sh...