搜索结果: 1-15 共查到“Imputation”相关记录17条 . 查询时间(0.059 秒)
Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population
Imputation Accuracy Linkage Disequilibrium Multibreed Dairy Cattle
2016/5/19
The objective of this study was to investigate the accuracy of imputation from low density (LDC) to moderate density SNP chips (MDC) in a Thai Holstein-Other multibreed dairy cattle population. Dairy ...
Imputation accuracy of bovine spongiform encephalopathy-associated PRNP indel polymorphisms from middle-density SNPs arrays
BSE cattle indel prion gene
2015/7/3
Statistical methods of imputation allow predicting genotypes of markers (which were not genotyped in the whole population) based on known linkage disequilibrium relationships between the flanking poly...
Analyzing Incomplete Political Science Data:An Alternative Algorithm for Multiple Imputation
Analyzing Incomplete Political Science Data Alternative Algorithm Multiple Imputation
2015/6/5
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agre...
Does the Missing Data Imputation Method Affect the Composition and Performance of Prognostic Models?
Data Multivariable imputation via chained equations Expectation maximum algorithm
2015/9/25
Background: We already showed the superiority of imputation of missing data (via Multivariable Imputation via Chained Equations (MICE) method) over exclusion of them; however, the methodology of MICE ...
Multiple Imputation in Survival Models:Applied on Breast Cancer Data
Prognostic model Missing data Multiple imputation Breast cancer
2015/9/23
Background: Missing data is a common problem in cancer research. While simple methods such as complete-case (C-C) analysis are commonly employed for handling this problem, several studies have shown t...
MissForest - nonparametric missing value imputation for mixed-type data
MissForest missing value imputation mixed-type data
2011/6/16
Modern data acquisition based on high-throughput technology is often facing the
problem of missing data. Algorithms commonly used in the analysis of such large-scale data often
depend on a complete ...
Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases
missing data imputation statistics corrected for missing data item performance behavioral databases model goodness of fit
2011/3/22
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases
missing data imputation statistics corrected for missing data item performance behavioral databases model goodness of fit
2011/3/23
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
Optimal method of imputation in survey sampling
Estimation of mean Missing data Imputation
2010/9/14
In this paper, we propose an optimal method of imputation which leads to an estimator of population mean with minimum mean squared error in survey sampling when the data values are missing completely ...
Bayesian Finite Population Imputation for Data Fusion
Confi dentiality disclosure matching multiple sharing synthetic
2014/3/20
In data fusion, data owners seek to combine datasets with disjoint observations and distinct variables to estimate relationships among the variables. One approach is to concatenate the files, sp...
The income and wealth imputation for the HILDA Survey was implemented using a
nearest neighbour regression method. A regression model for the variable of interest
was used to identify a record wit...
The Household, Income and Labour Dynamics in Australia (HILDA) Survey: Weighting and Imputation
The Household Income and Labour Dynamics Australia (HILDA) Survey
2009/11/4
The HILDA Survey aims to collect data from a representative sample of Australian
households and residents. However no survey can ensure that this collection process is
perfect and this is especial...
Towards an Imputation Strategy for Wave 1 of the HILDA Survey
an Imputation Strategy Wave 1 the HILDA Survey
2009/11/4
This paper discusses various issues surrounding the use of imputation in the
Household, Income and Labour Dynamics in Australia (HILDA) Survey and seeks a
way towards an imputation strategy. While...
Evaluation of Alternative Income Imputation Methods for the HILDA Survey
Alternative Income Imputation Methods the HILDA Survey
2009/11/4
All large-scale surveys, including longitudinal surveys, have non-response and various weighting and imputation strategies are employed to address this problem. There are three types of non-response i...
The impact of income imputation in the Consumer Expenditure Survey
income imputation Consumer Expenditure Survey
2009/9/17
With the release of 2004 data from the Consumer Expenditure Survey, the Bureau of Labor Statistics began implementing imputation for missing responses to questions about income;
imputation has brough...