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

Statistical Methods for Multivariate...

Agniel, Denis Madison.

FindBook

Google Book

Amazon

博客來

Statistical Methods for Multivariate and Complex Phenotypes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Methods for Multivariate and Complex Phenotypes./
作者:	Agniel, Denis Madison.
面頁冊數:	98 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.
Contained By:	Dissertation Abstracts International76-03B(E).
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3644949
ISBN:	9781321334166

Statistical Methods for Multivariate and Complex Phenotypes.
Agniel, Denis Madison.

Statistical Methods for Multivariate and Complex Phenotypes. - 98 p.

Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.

Thesis (Ph.D.)--Harvard University, 2014.

Many important scientific questions can not be studied properly using a single measurement as a response. For example, many phenotypes of interest in recent clinical research may be difficult to characterize due to their inherent complexity. It may be difficult to determine the presence or absence of disease based on a single measurement, or even a few measurements, or the phenotype may only be defined based on a series of symptoms. Similarly, a set of related phenotypes or measurements may be studied together in order to detect a shared etiology. In this work, we propose methods for studying complex phenotypes of these types, where the phenotype may be characterized either longitudinally or by a diverse set of continuous, discrete, or not fully observed components. In chapter 1, we seek to identify predictors that are related to multiple components of diverse outcomes. We take up specifically the question of identifying a multiple regulator, where we seek a genetic marker that is associated with multiple biomarkers for autoimmune disease. To do this, we propose sparse multiple regulation testing (SMRT) both to estimate the relationship between a set of predictors and diverse outcomes and to provide a testing framework in which to identify which predictors are associated with multiple elements of the outcomes, while controlling error rates. In chapter 2, we seek to identify risk profiles or risk scores for diverse outcomes, where a risk profile is a linear combination of predictors. The risk profiles will be chosen to be highly correlated to latent traits underlying the outcomes. To do this, we propose semiparametric canonical correlation analysis (sCCA), an updated version of the classical canonical correlation analysis. In chapter 3, the scientific question of interest pertains directly to the progression of disease over time. We provide a testing framework in which to detect the association between a set of genetic markers and the progression of disease in the context of a GWAS. To test for this association while allowing for highly nonlinear longitudinal progression of disease, we propose functional principal variance component (FPVC) testing.

ISBN: 9781321334166Subjects--Topical Terms:

1002712
Biostatistics.

Statistical Methods for Multivariate and Complex Phenotypes.
LDR:03010nmm a2200265 4500 001 2064121
005 20151109121427.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781321334166
035 $a (MiAaPQ)AAI3644949
035 $a AAI3644949
040 $a MiAaPQ $c MiAaPQ
100 1 $a Agniel, Denis Madison. $3 3178681
245 1 0 $a Statistical Methods for Multivariate and Complex Phenotypes.
300 $a 98 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.
500 $a Adviser: Tianxi Cai.
502 $a Thesis (Ph.D.)--Harvard University, 2014.
520 $a Many important scientific questions can not be studied properly using a single measurement as a response. For example, many phenotypes of interest in recent clinical research may be difficult to characterize due to their inherent complexity. It may be difficult to determine the presence or absence of disease based on a single measurement, or even a few measurements, or the phenotype may only be defined based on a series of symptoms. Similarly, a set of related phenotypes or measurements may be studied together in order to detect a shared etiology. In this work, we propose methods for studying complex phenotypes of these types, where the phenotype may be characterized either longitudinally or by a diverse set of continuous, discrete, or not fully observed components. In chapter 1, we seek to identify predictors that are related to multiple components of diverse outcomes. We take up specifically the question of identifying a multiple regulator, where we seek a genetic marker that is associated with multiple biomarkers for autoimmune disease. To do this, we propose sparse multiple regulation testing (SMRT) both to estimate the relationship between a set of predictors and diverse outcomes and to provide a testing framework in which to identify which predictors are associated with multiple elements of the outcomes, while controlling error rates. In chapter 2, we seek to identify risk profiles or risk scores for diverse outcomes, where a risk profile is a linear combination of predictors. The risk profiles will be chosen to be highly correlated to latent traits underlying the outcomes. To do this, we propose semiparametric canonical correlation analysis (sCCA), an updated version of the classical canonical correlation analysis. In chapter 3, the scientific question of interest pertains directly to the progression of disease over time. We provide a testing framework in which to detect the association between a set of genetic markers and the progression of disease in the context of a GWAS. To test for this association while allowing for highly nonlinear longitudinal progression of disease, we propose functional principal variance component (FPVC) testing.
590 $a School code: 0084.
650 4 $a Biostatistics. $3 1002712
690 $a 0308
710 2 $a Harvard University. $b Biostatistics. $3 2104931
773 0 $t Dissertation Abstracts International $g 76-03B(E).
790 $a 0084
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3644949