Inference in General Statistical Models
Estimated GLS Estimation
(Sprache: Englisch)
In this book, an attempt has been made by proposing some new inferential procedures for linear regression models with different autoregressive schemes for disturbances. These estimation procedures have used iterative methods based on studentized residuals....
Leider schon ausverkauft
versandkostenfrei
Buch
71.90 €
Produktdetails
Produktinformationen zu „Inference in General Statistical Models “
Klappentext zu „Inference in General Statistical Models “
In this book, an attempt has been made by proposing some new inferential procedures for linear regression models with different autoregressive schemes for disturbances. These estimation procedures have used iterative methods based on studentized residuals. It proposes some new inferential methods for linear statistical models with first, second and fourth order autoregressive disturbances. A new estimated iterative restricted GLS estimator has been derived for linear regression model with first order autoregressive disturbances. Later it has been applied for testing the general linear hypothesis. The linear statistical models have been specified with AR (1), AR (2) and AR (4) disturbances. The EGLS methods of estimation have been developed with particular AR (2) and AR (4) disturbances by using Iterative procedures. Here, Studentized residuals have been used in the place of OLS residuals. The parametric tests for particular second order and fourth order autocorrelations also havebeen discussed in this book
Bibliographische Angaben
- Autoren: B. Narasimhulu , M. Bhupathi Naidu , Balasiddamuni Pagadala
- 2013, 196 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659389773
- ISBN-13: 9783659389771
Sprache:
Englisch
Kommentar zu "Inference in General Statistical Models"
0 Gebrauchte Artikel zu „Inference in General Statistical Models“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Inference in General Statistical Models".
Kommentar verfassen