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Bayesian inference for autoregressiv...

Zhang, Peng.

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Bayesian inference for autoregressive panel data models.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian inference for autoregressive panel data models./
Author:	Zhang, Peng.
Description:	76 p.
Notes:	Adviser: Dylan Small.
Contained By:	Dissertation Abstracts International67-08B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3225572
ISBN:	9780542800474

Bayesian inference for autoregressive panel data models.
Zhang, Peng.

Bayesian inference for autoregressive panel data models. - 76 p.

Adviser: Dylan Small.

Thesis (Ph.D.)--University of Pennsylvania, 2006.

We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit's process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. Further, in longitudinal data, a unit's responses often exhibit autocorrelation. We developed a Bayesian random coefficient dynamic longitudinal data model. The model applies a Metropolis-within-Gibbs procedure and allows for unequally spaced data. A simulation data set is studied to get the Bayesian inference and the real data of Pakistani children growth is used to check the prediction properties. Expansion to higher order autoregressive structure is also considered.

ISBN: 9780542800474Subjects--Topical Terms:

517247
Statistics.

Bayesian inference for autoregressive panel data models.
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020 $a 9780542800474
035 $a (UnM)AAI3225572
035 $a AAI3225572
040 $a UnM $c UnM
100 1 $a Zhang, Peng. $3 1259627
245 1 0 $a Bayesian inference for autoregressive panel data models.
300 $a 76 p.
500 $a Adviser: Dylan Small.
500 $a Source: Dissertation Abstracts International, Volume: 67-08, Section: B, page: 4509.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2006.
520 $a We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit's process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. Further, in longitudinal data, a unit's responses often exhibit autocorrelation. We developed a Bayesian random coefficient dynamic longitudinal data model. The model applies a Metropolis-within-Gibbs procedure and allows for unequally spaced data. A simulation data set is studied to get the Bayesian inference and the real data of Pakistani children growth is used to check the prediction properties. Expansion to higher order autoregressive structure is also considered.
590 $a School code: 0175.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 0 $a University of Pennsylvania. $3 1017401
773 0 $t Dissertation Abstracts International $g 67-08B.
790 $a 0175
790 1 0 $a Small, Dylan, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3225572