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Relevant bibliographies by topics / Imputation multiple hot-deck

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Academic literature on the topic 'Imputation multiple hot-deck'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Imputation multiple hot-deck"

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Reilly, Marie. "Data Analysis Using Hot Deck Multiple Imputation." Statistician 42, no. 3 (1993): 307. http://dx.doi.org/10.2307/2348810.

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Butera, Nicole M., Siying Li, Kelly R. Evenson, et al. "Hot Deck Multiple Imputation for Handling Missing Accelerometer Data." Statistics in Biosciences 11, no. 2 (2018): 422–48. http://dx.doi.org/10.1007/s12561-018-9225-4.

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REILLY, MARIE, and MARGARET PEPE. "THE RELATIONSHIP BETWEEN HOT-DECK MULTIPLE IMPUTATION AND WEIGHTED LIKELIHOOD." Statistics in Medicine 16, no. 1 (1997): 5–19. http://dx.doi.org/10.1002/(sici)1097-0258(19970115)16:1<5::aid-sim469>3.0.co;2-8.

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Stavseth, Marianne Riksheim, Thomas Clausen, and Jo Røislien. "How handling missing data may impact conclusions: A comparison of six different imputation methods for categorical questionnaire data." SAGE Open Medicine 7 (January 2019): 205031211882291. http://dx.doi.org/10.1177/2050312118822912.

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Objectives: Missing data is a recurrent issue in many fields of medical research, particularly in questionnaires. The aim of this article is to describe and compare six conceptually different multiple imputation methods, alongside the commonly used complete case analysis, and to explore whether the choice of methodology for handling missing data might impact clinical conclusions drawn from a regression model when data are categorical. Methods: In addition to the commonly used complete case analysis, we tested the following six imputation methods: multiple imputation using expectation–maximizat

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Imbert, Alyssa, Armand Valsesia, Caroline Le Gall, et al. "Multiple hot-deck imputation for network inference from RNA sequencing data." Bioinformatics 34, no. 10 (2017): 1726–32. http://dx.doi.org/10.1093/bioinformatics/btx819.

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Siddique, Juned, and Thomas R. Belin. "Multiple imputation using an iterative hot-deck with distance-based donor selection." Statistics in Medicine 27, no. 1 (2007): 83–102. http://dx.doi.org/10.1002/sim.3001.

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Wang, Chia-Ning, Roderick Little, Bin Nan, and Siobán D. Harlow. "A Hot-Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories." Biometrics 67, no. 4 (2011): 1573–82. http://dx.doi.org/10.1111/j.1541-0420.2011.01558.x.

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Little, Roderick J., Matheos Yosef, Kevin C. Cain, Bin Nan, and Siobán D. Harlow. "A hot-deck multiple imputation procedure for gaps in longitudinal data on recurrent events." Statistics in Medicine 27, no. 1 (2007): 103–20. http://dx.doi.org/10.1002/sim.2939.

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Cranmer, Skyler J., and Jeff Gill. "We Have to Be Discrete About This: A Non-Parametric Imputation Technique for Missing Categorical Data." British Journal of Political Science 43, no. 2 (2012): 425–49. http://dx.doi.org/10.1017/s0007123412000312.

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Missing values are a frequent problem in empirical political science research. Surprisingly, the match between the measurement of the missing values and the correcting algorithms applied is seldom studied. While multiple imputation is a vast improvement over the deletion of cases with missing values, it is often unsuitable for imputing highly non-granular discrete data. We develop a simple technique for imputing missing values in such situations, which is a variant of hot deck imputation, drawing from the conditional distribution of the variable with missing values to preserve the discrete mea

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Kleinke, Kristian. "Multiple Imputation by Predictive Mean Matching When Sample Size Is Small." Methodology 14, no. 1 (2018): 3–15. http://dx.doi.org/10.1027/1614-2241/a000141.

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Abstract. Predictive mean matching (PMM) is a state-of-the-art hot deck multiple imputation (MI) procedure. The quality of its results depends, inter alia, on the availability of suitable donor cases. Applying PMM in small sample scenarios often found in psychological or medical research could be problematic, as there might not be many (or any) suitable donor cases in the data set. So far, there has not been any systematic research that examined the performance of PMM, when sample size is small. The present study evaluated PMM in various multiple regression scenarios, where sample size, missin

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Dissertations / Theses on the topic "Imputation multiple hot-deck"

1

Siddique, Juned. "Multiple imputation using an iterative hot-deck with distance-based donor selection." Diss., Restricted to subscribing institutions, 2005. http://proquest.umi.com/pqdweb?did=990278551&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Imbert, Alyssa. "Intégration de données hétérogènes complexes à partir de tableaux de tailles déséquilibrées." Thesis, Toulouse 1, 2018. http://www.theses.fr/2018TOU10022/document.

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Les avancées des nouvelles technologies de séquençage ont permis aux études cliniques de produire des données volumineuses et complexes. Cette complexité se décline selon diverses modalités, notamment la grande dimension, l’hétérogénéité des données au niveau biologique (acquises à différents niveaux de l’échelle du vivant et à divers moments de l’expérience), l’hétérogénéité du type de données, le bruit (hétérogénéité biologique ou données entachées d’erreurs) dans les données et la présence de données manquantes (au niveau d’une valeur ou d’un individu entier). L’intégration de différentes d

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Sullivan, Danielle M. "A Hot Deck Imputation Procedure for Multiply Imputing Nonignorable Missing Data: The Proxy Pattern-Mixture Hot Deck." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1387301284.

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Book chapters on the topic "Imputation multiple hot-deck"

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Aktaş, Mehmet S., Sinan Kaplan, Hasan Abacı, Oya Kalipsiz, Utku Ketenci, and Umut O. Turgut. "Data Imputation Methods for Missing Values in the Context of Clustering." In Big Data and Knowledge Sharing in Virtual Organizations. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7519-1.ch011.

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Missing data is a common problem for data clustering quality. Most real-life datasets have missing data, which in turn has some effect on clustering tasks. This chapter investigates the appropriate data treatment methods for varying missing data scarcity distributions including gamma, Gaussian, and beta distributions. The analyzed data imputation methods include mean, hot-deck, regression, k-nearest neighbor, expectation maximization, and multiple imputation. To reveal the proper methods to deal with missing data, data mining tasks such as clustering is utilized for evaluation. With the experimental studies, this chapter identifies the correlation between missing data imputation methods and missing data distributions for clustering tasks. The results of the experiments indicated that expectation maximization and k-nearest neighbor methods provide best results for varying missing data scarcity distributions.

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