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Bayesian inference for autoregressiv...
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Zhang, Peng.
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Bayesian inference for autoregressive panel data models.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Bayesian inference for autoregressive panel data models./
作者:
Zhang, Peng.
面頁冊數:
76 p.
附註:
Adviser: Dylan Small.
Contained By:
Dissertation Abstracts International67-08B.
標題:
Statistics. -
電子資源:
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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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.
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