Chapter 3 - Simple Linear Models

Abstract

This chapter discusses problems of prediction, that is, estimation of future random variables; filtering, that is, estimation of random variables in the presence of noise or super-imposed error; and parameter estimation for some simple discrete-time linear stochastic processes. By Wold's Decomposition Theorem, any regular stationary process without a singular component can be written as a linear process. The chapter describes only simple linear models such as autoregressive, moving average, autoregressive with moving average residuals, and autoregressive with superimposed error, that is, noise.