Autocorrelation (also called serial correlation) is violation of the assumption that the error terms are
not correlated, i.e., with autocorrelation E(∈i, ∈j) ≠ 0 (∈i ≠ ∈j). That is, the error in the period t is not
independent of previous errors.
Since we do not know the population line, we do not know the actual errors (∈s), but we estimate
them by the residuals (e). Hence a look at the residual plot for a regression that (i) has no
autocorrelation; (ii) has positive autocorrelation, and, (iii) has negative autocorrelation.
This is a survey of non-linear regression models, with an emphasis on the theory
of estimation and hypothesis testing rather than computation and applications,
although there will be some discussion of the last two topics. For a general
discussion of computation the reader is referred to Chapter 12 of this Handbook
by Quandt. My aim is to present the gist of major results; therefore, I will
sometimes omit proofs and less significant assumptions. For those, the reader
must consult the original sources....
My first contact with speech coding was in 1993 when I was a Field Application
Engineer at Texas Instruments, Inc. Soon after joining the company I was assigned
to design a demo prototype for the digital telephone answering device project.
Initially I was in charge of hardware including circuit design and printed circuit
board layout. The core of the board consisted of a microcontroller sending
commands to a mixed signal processor, where all the signal processing tasks—
including speech coding—were performed.
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Conjugate-Cyclic-Autocorrelation Projection-Based Algorithm for Signal Parameter Estimation
Time Series Forecasting (Part II) povides about Stationary and nonstationary processes, Autocorrelation function, Autoregressive models AR, Moving Average models MA, ARMA models, Estimating and checking ARIMA models(Box-Jenkins Methodology).
Chapter 5 - Classical linear regression model assumptions and diagnostics. In this chapter, students will be able to understand: Describe the steps involved in testing regression residuals for heteroscedasticity and autocorrelation, explain the impact of heteroscedasticity or autocorrelation on the optimality of OLS parameter and standard error estimation, distinguish between the Durbin--Watson and Breusch--Godfrey tests for autocorrelation,...