Results 21 to 30 of about 199,171 (299)

Statistical inference with large‐scale trait imputation

Statistics in Medicine, 2023
Recently a nonparametric method called LS‐imputation has been proposed for large‐scale trait imputation based on a GWAS summary dataset and a large set of genotyped individuals. The imputed trait values, along with the genotypes, can be treated as an individual‐level dataset for downstream genetic analyses, including those that cannot be done with GWAS
Jingchen Ren, Wei Pan
openaire   +2 more sources

Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

Algorithms, 2022
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis.
Yufan Qian, Limei Tian, Baichen Zhai, Shufan Zhang, Rui Wu   +4 more
doaj   +1 more source

Using machine learning to impute legal status of immigrants in the National Health Interview Survey

MethodsX, 2022
We describe a novel machine learning method of imputing legal status for immigrants using nationally representative survey data from the Survey of Income and Program Participation (SIPP) and the National Health Interview Survey (NHIS). K-nearest Neighbor
Simon A. Ruhnke, Fernando A. Wilson, Jim P. Stimpson   +2 more
doaj   +1 more source

CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data.
S. Äyrämö, I. Pölönen, M. A. Eskelinen   +2 more
doaj   +1 more source

Optimal method for determining the intraclass correlation coefficients of urinary biomarkers such as dialkylphosphates from imputed data

Environment International, 2022
Urinary biomarkers are commonly used in epidemiological studies as surrogates or indicators of exposure to chemical substances. Evaluating the reliability of a biomarker is highly important because use of an unreliable marker may lead to ...
Yukiko Nishihama, Yonghang Lai, Tomohiko Isobe, Shoji F. Nakayama   +3 more
doaj   +1 more source

Much Ado About Nothing: Multiple Imputation to Balance Unbalanced Designs for Two-Way Analysis of Variance

Methodology, 2020
In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type III sum of squares in two-way ANOVA.
Joost R. van Ginkel, Pieter M. Kroonenberg   +1 more
doaj   +1 more source

Nonparametric statistical inference and imputation for incomplete categorical data [PDF]

Statistics and Its Interface, 2020
9 pages, 2 ...
Wang, Chaojie, Shen, Linghao, Li, Han, Fan, Xiaodan   +3 more
openaire   +2 more sources

When Does Choice of Accuracy Measure Alter Imputation Accuracy Assessments? [PDF]

PLoS ONE, 2015
Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease.
Shelina Ramnarine, Juan Zhang, Li-Shiun Chen, Robert Culverhouse, Weimin Duan, Dana B Hancock, Sarah M Hartz, Eric O Johnson, Emily Olfson, Tae-Hwi Schwantes-An, Nancy L Saccone   +10 more
doaj   +1 more source

A genotype imputation method for de-identified haplotype reference information by using recurrent neural network.

PLoS Computational Biology, 2020
Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel.
Kaname Kojima, Shu Tadaka, Fumiki Katsuoka, Gen Tamiya, Masayuki Yamamoto, Kengo Kinoshita   +5 more
doaj   +2 more sources

Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics.

PLoS Computational Biology, 2022
Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one.
Marie Chion, Christine Carapito, Frédéric Bertrand   +2 more
doaj   +1 more source