東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Fractional imputation method in hand...

Yang, Shu.

Linked to FindBook

Google Book

Amazon

博客來

Fractional imputation method in handling missing data and spatial statistics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Fractional imputation method in handling missing data and spatial statistics./
Author:	Yang, Shu.
Description:	185 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3627524
ISBN:	9781321028430

Fractional imputation method in handling missing data and spatial statistics.
Yang, Shu.

Fractional imputation method in handling missing data and spatial statistics. - 185 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--Iowa State University, 2014.

This thesis has two themes. One is missing data analysis, and the other is spatial data analysis.

ISBN: 9781321028430Subjects--Topical Terms:

517247
Statistics.

Fractional imputation method in handling missing data and spatial statistics.
LDR:02882nam a2200289 4500 001 1965443
005 20141022133324.5
008 150210s2014 ||||||||||||||||| ||eng d
020 $a 9781321028430
035 $a (MiAaPQ)AAI3627524
035 $a AAI3627524
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yang, Shu. $3 2102101
245 1 0 $a Fractional imputation method in handling missing data and spatial statistics.
300 $a 185 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Advisers: Alexander Roitershtein; Jae Kwang Kim.
502 $a Thesis (Ph.D.)--Iowa State University, 2014.
520 $a This thesis has two themes. One is missing data analysis, and the other is spatial data analysis.
520 $a Missing data frequently occur in many statistics problems. It can arise naturally in many applications. For example, in many surveys there are data that could have been observed are missing due to non-response. It can also be a deliberate modeling choice. For example, a mixed effects model can include random variables that are not observable (called latent variables or random effects). Imputation is often used to facilitate parameter estimation in the presence of missing data, which allows one to use the complete sample estimators on the imputed data set. Parametric fractional imputation (PFI) is an imputation method proposed by Kim (2011), which simplifies the computation associated with the EM algorithm for maximum likelihood estimation with missing data. In this thesis we study four extensions of the PFI methods: 1. The use of PFI to handle non-ignorable non-response problem in linear and generalized linear mixed models. 2. Application of PFI method for quantile estimation with missing data. 3. Likelihood-based inference for missing data using PFI. 4. A semiparametric fractional imputation method for handling missing covariate.
520 $a The second theme is spatial data analysis. Estimation of the covariance structure of spatial processes is of fundamental importance in spatial statistics. The difficulty arises when spatial process exhibits non-stationarity or the observed spatial data is irregularly spaced. We propose estimation methods targeting to solve these two difficulties. 1. We propose a non-stationary spatial modeling, study the theoretical properties of estimation and plug-in kriging prediction of a non-stationary spatial process, and explore the connection between kriging under non-stationary models and spatially adaptive non-parametric smoothing methods. 2. A semiparametric estimation of spectral density function for irregular spatial data.
590 $a School code: 0097.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a Iowa State University. $b Statistics. $3 1020860
773 0 $t Dissertation Abstracts International $g 75-10B(E).
790 $a 0097
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3627524