Application of Sequential Regression Multivariate Imputation Method on Multivariate Normal Missing Data

Nurzaman,; Siswantining, Titin; Soemartojo, Saskya Mary; Sarwinda, Devvi

doi:10.1109/icicos48119.2019.8982423

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Recently, ML-based imputation methods have been gradually emerging: e.g., generative adversarial networks (GAN) for missing data [19], [20]. K-Harmonic mean imputation [21], sequential regression multivariate imputation [22], Fuzzy C-Means imputation [23], and predictive mean matching [24] are also noteworthy. These existing imputation methods often require expertlevel distributional assumptions that are difficult for general researchers.…”

Section: Introductionmentioning

confidence: 99%

Ultra Data-Oriented Parallel Fractional Hot-Deck Imputation With Efficient Linearized Variance Estimation

Yang

Kwon

Kim

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Parallel fractional hot-deck imputation (P-FHDI [1]) is a general-purpose, assumption-free tool for handling item nonresponse in big incomplete data by combining the theory of FHDI and parallel computing. FHDI cures multivariate missing data by filling each missing unit with multiple observed values (thus, hot-deck) without resorting to distributional assumptions. P-FHDI can tackle big incomplete data with millions of instances (big-n) or 10, 000 variables (big-p). However, handling ultra incomplete data (i.e., concurrently big-n and big-p) with tremendous instances and high dimensionality has posed problems to P-FHDI due to excessive memory requirement and execution time. To tackle the aforementioned challenges, we propose the ultra data-oriented P-FHDI (named UP-FHDI) capable of curing ultra incomplete data. In addition to the parallel Jackknife method, this paper enables a computationally efficient ultra data-oriented variance estimation using parallel linearization techniques. Results confirm that UP-FHDI can tackle an ultra dataset with one million instances and 10, 000 variables. This paper illustrates the special parallel algorithms of UP-FHDI and confirms its positive impact on the subsequent deep learning performance.

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Section: Introductionmentioning

confidence: 99%

Ultra Data-Oriented Parallel Fractional Hot-Deck Imputation With Efficient Linearized Variance Estimation

Yang

Kwon

Kim

et al. 2023

IEEE Trans. Knowl. Data Eng.

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“…It also offers variance estimation using the Jackknife replication method. Besides, other popular imputation methods include multiple imputation with predictive mean matching (PMM) by [18], sequential regression multivariate imputation by [19], Fuzzy C-Means (FCM) imputation by [20], and K-Harmonic mean imputation by [21].…”

Section: Introductionmentioning

confidence: 99%

Parallel Fractional Hot-Deck Imputation and Variance Estimation for Big Incomplete Data Curing

Yang

Kim

Cho

2022

IEEE Trans. Knowl. Data Eng.

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The fractional hot-deck imputation (FHDI) is a general-purpose, assumption-free imputation method for handling multivariate missing data by filling each missing item with multiple observed values without resorting to artificially created values. The corresponding R package FHDI [1] holds generality and efficiency, but it is not adequate for tackling big incomplete data due to the requirement of excessive memory and long running time. As a first step to tackle big incomplete data by leveraging the FHDI, we developed a new version of a parallel fractional hot-deck imputation (named as P-FHDI) program suitable for curing large incomplete datasets. Results show a favorable speedup when the P-FHDI is applied to big datasets with up to millions of instances or 10,000 of variables. This paper explains the detailed parallel algorithms of the P-FHDI for large instances (big-n) or high-dimensionality (big-p) datasets and confirms the favorable scalability. The proposed program inherits all the advantages of the serial FHDI and enables a parallel variance estimation, which will benefit a broad audience in science and engineering.

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“…They addressed the issue of overfitting by incorporating uncertainty into their approach. Principal component analysis (PCA), which involves eigenvalue decomposition of a matrix, is widely utilized in exploratory data mining analysis [49]. It shares similarities with factor analysis and is closely related to regression analysis.…”

Section: Principal Component Analysismentioning

confidence: 99%

Handling Missing values through Imputation techniques: A Quantitative Survey

Mohit,

Devpriya

2023

Preprint

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This study examines the various techniques used to address missing datain quantitative studies. It acknowledges that missing data poses challengesto data analysis and presents a range of solutions, from simple methodslike deletion and single imputation to advanced approaches involving mod-eling, multiple imputation, and machine learning. The survey takes both abottom-up and top-down approach in selecting an appropriate method forhandling missing data, where the analysis of the data helps narrow down theoptions for the selection process. The study explores the qualitative benefitsand drawbacks of each technique, considering factors such as the analyticalapproach, missing data handling strategy, sample size, and proportion ofmissing data to assess estimation precision. The researchers employed a the-oretical approach using fictitious data to evaluate the significance of missingdata treatment on estimate precision. The statistical analysis conducted inthis study provides practical guidelines for educational researchers, assistingthem in determining the optimal amount of data to collect, the type of anal-ysis to perform, and the appropriate strategy for addressing missing data toensure the reliability of their studies

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Application of Sequential Regression Multivariate Imputation Method on Multivariate Normal Missing Data

Cited by 10 publications

References 5 publications

Ultra Data-Oriented Parallel Fractional Hot-Deck Imputation With Efficient Linearized Variance Estimation

Ultra Data-Oriented Parallel Fractional Hot-Deck Imputation With Efficient Linearized Variance Estimation

Parallel Fractional Hot-Deck Imputation and Variance Estimation for Big Incomplete Data Curing

Handling Missing values through Imputation techniques: A Quantitative Survey

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