Results 271 to 280 of about 1,318,109 (301)

The genetic architecture of fibromyalgia across 2.5 million individuals

Kerrebijn I, Bjornsdottir G, Arbabi K, Urpa L, Haapaniemi H, Thorleifsson G, Stefansdottir L, Frangakis S, Valliere J, Kunorozva L, Abner E, Ji C, Aagaard B, Bliddal H, Brunak S, Bruun MT, Didriksen M, Erikstrup C, Geirsson AJ, Gudbjartsson DF, Hansen TF, Jonsdottir I, Knight S, Knowlton KU, Mikkelsen C, Nadauld LD, Olafsdottir TA, Ostrowski SR, Pedersen OB, Saevarsdottir S, Skuladottir AT, Sørensen E, Stefansson H, Sulem P, Sveinsson OA, Thorlacius GE, Thorsteinsdottir U, Ullum H, Vikingsson A, Werge TM, Chronic Pain Genomics Consortium, FinnGen, DBDS Genomic Consortium, Estonian Biobank Research Team, Genes & Health Research Team, Saxena R, Stefansson K, Brummett CM, Glintborg B, Clauw DJ, Thorgeirsson TE, Williams FM, Sinnott-Armstrong N, Ollila HM, Wainberg M.   +54 more
europepmc   +1 more source

Statistical fine-mapping and summary statistics imputation in genome-wide association studies

, 2019
In genome-wide association studies (GWASs), a large number of trait-associated single-nucleotide polymorphisms (SNPs) have been detected. Among these associations, not all SNPs are the causal ones due to the correlation between SNPs. Many statistical fine-mapping methods have been proposed to identify SNPs that mechanistically affect a disease/trait ...
Yu Pu
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Multiple imputation score tests and an application to Cochran‐Mantel‐Haenszel statistics

Statistics in Medicine, 2020
The standard multiple imputation technique focuses on parameter estimation. In this study, we describe a method for conducting score tests following multiple imputation.
K. Lu
semanticscholar   +1 more source

Routine multiple imputation in statistical databases

Seventh International Working Conference on Scientific and Statistical Database Management, 2002
This paper deals with problems concerning missing data in statistical databases. Multiple imputation is a statistically sound technique for handling incomplete data. Two problems should be addressed before the routine application of the technique becomes feasible.
Buuren, S. van, Mulligen, E.M. van, Brand, J.P.L.   +2 more
openaire   +3 more sources

Imputation of missing data using multi auxiliary information under ranked set sampling

Communications in statistics. Simulation and computation, 2023
In this paper, we intend to utilize the multi auxiliary information available under RSS for the imputation of missing data. The mean imputation, regression imputation methods, and power transformation imputation method are identified as special cases of ...
Shashi Bhushan, Anoop Kumar
semanticscholar   +1 more source

Imputation for statistical inference with coarse data

Canadian Journal of Statistics, 2012
AbstractCoarse data is a general type of incomplete data that includes grouped data, censored data, and missing data. The likelihood‐based estimation approach with coarse data is challenging because the likelihood function is in integral form. The Monte Carlo EM algorithm of Wei & Tanner [Wei & Tanner (1990).Journal of the American Statistical ...
Kim, Jae Kwang, Hong, Minki
openaire   +2 more sources

Flexible Imputation of Missing Data, 2nd ed.

Journal of the American Statistical Association, 2019
Missing data are frequently encountered in practice. A broader class of missing data is called incomplete data, which includes data with measurement error, multilevel data with latent variables, and potential outcomes in causal inference.
Shu Yang
semanticscholar   +1 more source

Response to Comments by Seaman et al. on “Analysis of Longitudinal Trials With Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation,” Journal of Biopharmaceutical Statistics 23:1352–1371

Journal of Biopharmaceutical Statistics, 2014
J. Carpenter, J. Roger, S. Cro, M. Kenward   +3 more
semanticscholar   +2 more sources

Pooling test statistics across multiply imputed datasets for nonnormal items

Behavior Research Methods, 2023
In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al.
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LSPT-D: Local Similarity Preserved Transport for Direct Industrial Data Imputation

IEEE Transactions on Automation Science and Engineering
Accurate imputation of missing data is pivotal in real-world industrial applications. Traditional direct imputers, which utilize basic statistics to replace missing elements, offer a practical solution but struggle to adapt to the complex patterns in ...
Hao Wang, Xinggao Liu, Zhaoran Liu, Haozhe Li, Yilin Liao, Yuxin Huang, Zhichao Chen   +6 more
semanticscholar   +1 more source