東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Bayesian semiparametric methods for ...

Bigelow, Jamie Lynn.

FindBook

Google Book

Amazon

博客來

Bayesian semiparametric methods for functional data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian semiparametric methods for functional data./
作者:	Bigelow, Jamie Lynn.
面頁冊數:	133 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0963.
Contained By:	Dissertation Abstracts International67-02B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3207397
ISBN:	9780542547324

Bayesian semiparametric methods for functional data.
Bigelow, Jamie Lynn.

Bayesian semiparametric methods for functional data. - 133 p.

Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0963.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2006.

Motivated by studies of reproductive hormone profiles in the menstrual cycle, we develop methods for hierarchical functional data analysis. The data come from the North Carolina Early Pregnancy Study, in which measurements of urinary progesterone metabolites are available from a cohort of women who were trying to become pregnant. Methods for menstrual hormone data are needed that avoid standardizing menstrual cycle lengths while also allowing for flexible relationships between the hormones and covariates. In addition, it is necessary to account for within-woman dependency in the hormone trajectories from multiple cycles. All of the methods are developed for and applied to menstrual hormone data, but they are general enough to be applied in many other settings.

ISBN: 9780542547324Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Bayesian semiparametric methods for functional data.
LDR:03097nmm 2200313 4500 001 1830611
005 20070430073130.5
008 130610s2006 eng d
020 $a 9780542547324
035 $a (UnM)AAI3207397
035 $a AAI3207397
040 $a UnM $c UnM
100 1 $a Bigelow, Jamie Lynn. $3 1919435
245 1 0 $a Bayesian semiparametric methods for functional data.
300 $a 133 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0963.
500 $a Advisers: Amy Herring; David Dunson.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2006.
520 $a Motivated by studies of reproductive hormone profiles in the menstrual cycle, we develop methods for hierarchical functional data analysis. The data come from the North Carolina Early Pregnancy Study, in which measurements of urinary progesterone metabolites are available from a cohort of women who were trying to become pregnant. Methods for menstrual hormone data are needed that avoid standardizing menstrual cycle lengths while also allowing for flexible relationships between the hormones and covariates. In addition, it is necessary to account for within-woman dependency in the hormone trajectories from multiple cycles. All of the methods are developed for and applied to menstrual hormone data, but they are general enough to be applied in many other settings.
520 $a The statistical approach is based on a hierarchical generalization of Bayesian multivariate adaptive regression splines. The generalization allows for an unknown set of basis functions characterizing both the overall trajectory means and woman-specific covariate effects and allows for the complex dependency structure of the data. To relax distributional assumptions, we use a Dirichlet process prior on the unknown distribution of the random basis coefficients in the spline model. This requires the development of methodology for the use of the Dirichlet process on the distribution of a parameter of varying dimension. While modeling the curves nonparametrically, the Dirichlet process also identifies clusters of similar curves. Finally, we combine our approach with Bayesian methods for generalized linear models, developing a procedure that clusters trajectories while jointly estimating the response distribution of each cluster.
520 $a In all of the models, a reversible jump Markov chain Monte Carlo algorithm is developed for posterior computation. Applying the methods to the progesterone data, we investigate differences in progesterone profiles between conception and non-conception cycles, identify clusters of pre-ovulatory progesterone, and demonstrate the ability of the joint model to distinguish early pregnancy losses from clinical pregnancies.
590 $a School code: 0153.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 0 $a The University of North Carolina at Chapel Hill. $3 1017449
773 0 $t Dissertation Abstracts International $g 67-02B.
790 1 0 $a Herring, Amy, $e advisor
790 1 0 $a Dunson, David, $e advisor
790 $a 0153
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3207397