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

Comparing the performance of two est...

Dimassi, Hani.

FindBook

Google Book

Amazon

博客來

Comparing the performance of two estimation procedures, the weighted least squares and the generalized estimating equations, in analyzing repeated observations with time nesting.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Comparing the performance of two estimation procedures, the weighted least squares and the generalized estimating equations, in analyzing repeated observations with time nesting./
作者:	Dimassi, Hani.
面頁冊數:	495 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0515.
Contained By:	Dissertation Abstracts International65-02B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3121876
ISBN:	0496692276

Comparing the performance of two estimation procedures, the weighted least squares and the generalized estimating equations, in analyzing repeated observations with time nesting.
Dimassi, Hani.

Comparing the performance of two estimation procedures, the weighted least squares and the generalized estimating equations, in analyzing repeated observations with time nesting. - 495 p.

Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0515.

Thesis (Ph.D.)--The University of Oklahoma Health Sciences Center, 2004.

Public health researchers are increasingly encountering repeated categorical observations. Analyzing this type of data requires techniques that account for their inherent properties. Two estimation procedures are used in the analysis of repeated observations: the Weighted Least Squares (WLS) and the Generalized Estimating Equations (GEE). Using a longitudinal model with time nesting effects, we studied the performance of these two procedures under different conditions. We found disparity in estimation bias between the two procedures. On the other hand, both techniques were comparable in terms of efficiency and precision. We looked at the relation between the bias and the study factors and found few associations. We also compared estimates from the GEE using four different working correlation structures with estimates from the logistic regression and found no significant differences. Simulation technique and results are also presented.

ISBN: 0496692276Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Comparing the performance of two estimation procedures, the weighted least squares and the generalized estimating equations, in analyzing repeated observations with time nesting.
LDR:01983nmm 2200289 4500 001 1838028
005 20050509101617.5
008 130614s2004 eng d
020 $a 0496692276
035 $a (UnM)AAI3121876
035 $a AAI3121876
040 $a UnM $c UnM
100 1 $a Dimassi, Hani. $3 1926455
245 1 0 $a Comparing the performance of two estimation procedures, the weighted least squares and the generalized estimating equations, in analyzing repeated observations with time nesting.
300 $a 495 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0515.
500 $a Major Professor: Willis Owen.
502 $a Thesis (Ph.D.)--The University of Oklahoma Health Sciences Center, 2004.
520 $a Public health researchers are increasingly encountering repeated categorical observations. Analyzing this type of data requires techniques that account for their inherent properties. Two estimation procedures are used in the analysis of repeated observations: the Weighted Least Squares (WLS) and the Generalized Estimating Equations (GEE). Using a longitudinal model with time nesting effects, we studied the performance of these two procedures under different conditions. We found disparity in estimation bias between the two procedures. On the other hand, both techniques were comparable in terms of efficiency and precision. We looked at the relation between the bias and the study factors and found few associations. We also compared estimates from the GEE using four different working correlation structures with estimates from the logistic regression and found no significant differences. Simulation technique and results are also presented.
590 $a School code: 0361.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Public Health. $3 1017659
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0573
690 $a 0463
710 2 0 $a The University of Oklahoma Health Sciences Center. $3 1023966
773 0 $t Dissertation Abstracts International $g 65-02B.
790 1 0 $a Owen, Willis, $e advisor
790 $a 0361
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3121876