NSTL国家科技图书文献中心

1. Surface data imputation with stochastic processes NSTL国家科技图书文献中心

Jawaid, A. | Schmidt, S.... - 《Measurement Science & Technology》 - 2025,36(4) - 046131.1～046131.11 - 共11页

摘要： features of the surface. Therefore, data imputation* must* | . Traditional surface data imputation* methods rely on simple* | imputation. This approach, which originates from surface | Spurious measurements frequently occur in | surface data from technical components. Excluding or

关键词： surface | imputation | interpolation | Gaussian processes

2. Generative adversarial learning for missing data imputation NSTL国家科技图书文献中心

Xinyang,Wang | Hongyu,Chen ... - 《Neural computing & applications》 - 2025,37(3) - 1403～1416 - 共14页

摘要： have been proposed for missing data imputation* and* | . Nevertheless, the inputs of imputation* networks of these* | , these methods may not provide satisfactory imputation | data imputation. The method is based on the | imputation network, namely a generator, is not incomplete

关键词： Missing data imputation | Neural networks | Deep learning | Industrial process

3. MetaLIRS: Meta-learning for Imputation and Regression Selection NSTL国家科技图书文献中心

Isil Baysal Erez | Jan Flokstra ... - 《Intelligent Data Engineering and Automated Learning - IDEAL 2024,Part I》 - International Conference on Intelligent Data Engineering and Automated Learning - 2025, - 155～166 - 共12页

摘要： handle missing data directly, imputation* methods are* | imputation method is non-trivial, however. Moreover, our | imputation method does not always result in the best | Learning Imputation* and Regression Selection) framework* | resource-friendly recommendation on which imputation* and*

关键词： Missing data | Imputation | Regression

4. Assessing Adversarial Effects of Noise in Missing Data Imputation NSTL国家科技图书文献中心

Arthur Dantas Mangus... | Ricardo Cardoso Pere...... - 《Intelligent Systems,Part I》 - Brazilian Conference on Intelligent Systems - 2025, - 200～214 - 共15页

摘要： imputation strategies emerged as a possible solution for | value imputation* (MVI) methods is the presence of* | imputation using seven state-of-the-art MVI methods. Our | performing the imputation* task and evaluating the quality* | of the imputation* directly. Additionally, we*

关键词： Missing data imputation | Noise filtering

5. Evaluating Algorithms for Missing Value Imputation in Real Battery Data NSTL国家科技图书文献中心

Dauda Nanman Sheni | Anton Herman Basson ... - 《Artificial Intelligence XLI,Part II》 - SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence - 2025, - 75～87 - 共13页

摘要： imputation techniques for missing value imputation on real | the multiple imputation* by chained equations* | algorithm are evaluated as imputation* techniques. The* | imputation tasks. This research informs the selection and | imputation and error correction, with k-nearest neighbour

关键词： Error correction | Missing value imputation

6. Multiple Imputation for Longitudinal Data: A Tutorial NSTL国家科技图书文献中心

Rushani Wijesuriya | Margarita Moreno‐Bet...... - 《Statistics in medicine.》 - 2025,44(3/4) - e10274～e10274 - 共24页 - 被引量：1

摘要：. Multiple imputation* (MI) is widely used to handle missing* | that the imputation* model is compatible with the* | model should be reflected in the imputation* process* | using generalized linear mixed imputation* models* | use of advanced imputation* procedures. In this*

关键词： clustered data | fully conditional specification | joint modeling | longitudinal data | missing data | multiple imputation

7. Multiple imputation with competing risk outcomes NSTL国家科技图书文献中心

Austin, Peter C. - 《Computational statistics》 - 2025,40(2) - 929～949 - 共21页

摘要： Imputation (MI) is a popular method to address the presence | of missing data. MI uses an imputation* model to* | is multivariate imputation* using chained equations* | functions were included in the imputation* models (the* | In time-to-event analyses, a competing risk is

关键词： Competing risks | Survival analysis | Missing data | Multiple imputation | Monte Carlo simulations

8. Comparison of imputation methods for univariate categorical longitudinal data NSTL国家科技图书文献中心

Kevin,Emery | Matthias,Studer ... - 《Quality & Quantity》 - 2025,59(2) - 1767～1791 - 共25页

摘要： multiple imputation* methods proposed so far in the* | multiple imputation* method improves the quality of* | imputation in trajectories subject to time-varying | Abstract The life course paradigm emphasizes | the need to study not only the situation at a given

关键词： Fully conditional specification | Imputation | Life course data | Missing data | Multiple imputation for categorical time series

9. Iterative Time Series Imputation by Maintaining Dependency Consistency NSTL国家科技图书文献中心

HANWEN HU | SHIYOU QIAN ... - 《ACM transactions on knowledge discovery from data》 - 2025,19(1) - 6.1～6.24 - 共24页

摘要：Data imputation* is crucial in the analysis of* | series imputation* lies in preventing the model from* | for data imputation. Based on this idea, we propose | learning-validation imputation* paradigm. Firstly, IRM* | -the-art imputation* models. The experiment results*

关键词： Time Series Imputation | Iterative Reconstruction | Incomplete Representation

10. GIG: Graph Data Imputation With Graph Differential Dependencies NSTL国家科技图书文献中心

Jiang Hua | Michael Bewong ... - 《Databases Theory and Applications》 - Australasian Database Conference - 2025, - 347～358 - 共12页

摘要：Data imputation* addresses the challenge of* | graph data imputation* approach called GIG which relies* | the data imputation* process making it more reliable* | imputing missing values in database instances, ensuring | consistency with the overall semantics of the dataset

摘要： features of the surface. Therefore, data imputation* must* | . Traditional surface data imputation* methods rely on simple* | imputation. This approach, which originates from surface | Spurious measurements frequently occur in | surface data from technical components. Excluding or

关键词： surface | imputation | interpolation | Gaussian processes

摘要： have been proposed for missing data imputation* and* | . Nevertheless, the inputs of imputation* networks of these* | , these methods may not provide satisfactory imputation | data imputation. The method is based on the | imputation network, namely a generator, is not incomplete

关键词： Missing data imputation | Neural networks | Deep learning | Industrial process

摘要： handle missing data directly, imputation* methods are* | imputation method is non-trivial, however. Moreover, our | imputation method does not always result in the best | Learning Imputation* and Regression Selection) framework* | resource-friendly recommendation on which imputation* and*

关键词： Missing data | Imputation | Regression

摘要： imputation strategies emerged as a possible solution for | value imputation* (MVI) methods is the presence of* | imputation using seven state-of-the-art MVI methods. Our | performing the imputation* task and evaluating the quality* | of the imputation* directly. Additionally, we*

关键词： Missing data imputation | Noise filtering

摘要： imputation techniques for missing value imputation on real | the multiple imputation* by chained equations* | algorithm are evaluated as imputation* techniques. The* | imputation tasks. This research informs the selection and | imputation and error correction, with k-nearest neighbour

关键词： Error correction | Missing value imputation

摘要：. Multiple imputation* (MI) is widely used to handle missing* | that the imputation* model is compatible with the* | model should be reflected in the imputation* process* | using generalized linear mixed imputation* models* | use of advanced imputation* procedures. In this*

关键词： clustered data | fully conditional specification | joint modeling | longitudinal data | missing data | multiple imputation

摘要： Imputation (MI) is a popular method to address the presence | of missing data. MI uses an imputation* model to* | is multivariate imputation* using chained equations* | functions were included in the imputation* models (the* | In time-to-event analyses, a competing risk is

关键词： Competing risks | Survival analysis | Missing data | Multiple imputation | Monte Carlo simulations

摘要： multiple imputation* methods proposed so far in the* | multiple imputation* method improves the quality of* | imputation in trajectories subject to time-varying | Abstract The life course paradigm emphasizes | the need to study not only the situation at a given

关键词： Fully conditional specification | Imputation | Life course data | Missing data | Multiple imputation for categorical time series

摘要：Data imputation* is crucial in the analysis of* | series imputation* lies in preventing the model from* | for data imputation. Based on this idea, we propose | learning-validation imputation* paradigm. Firstly, IRM* | -the-art imputation* models. The experiment results*

关键词： Time Series Imputation | Iterative Reconstruction | Incomplete Representation

摘要：Data imputation* addresses the challenge of* | graph data imputation* approach called GIG which relies* | the data imputation* process making it more reliable* | imputing missing values in database instances, ensuring | consistency with the overall semantics of the dataset

关键词： Data imputation | Transformer | Graph difference dependency

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： features of the surface. Therefore, data imputation must | . Traditional surface data imputation methods rely on simple | imputation. This approach, which originates from surface | Spurious measurements frequently occur in | surface data from technical components. Excluding or

关键词： surface | imputation | interpolation | Gaussian processes

摘要： have been proposed for missing data imputation and | . Nevertheless, the inputs of imputation networks of these | , these methods may not provide satisfactory imputation | data imputation. The method is based on the | imputation network, namely a generator, is not incomplete

关键词： Missing data imputation | Neural networks | Deep learning | Industrial process

摘要： handle missing data directly, imputation methods are | imputation method is non-trivial, however. Moreover, our | imputation method does not always result in the best | Learning Imputation and Regression Selection) framework | resource-friendly recommendation on which imputation and

关键词： Missing data | Imputation | Regression

摘要： imputation strategies emerged as a possible solution for | value imputation (MVI) methods is the presence of | imputation using seven state-of-the-art MVI methods. Our | performing the imputation task and evaluating the quality | of the imputation directly. Additionally, we

关键词： Missing data imputation | Noise filtering

摘要： imputation techniques for missing value imputation on real | the multiple imputation by chained equations | algorithm are evaluated as imputation techniques. The | imputation tasks. This research informs the selection and | imputation and error correction, with k-nearest neighbour

关键词： Error correction | Missing value imputation

摘要：. Multiple imputation (MI) is widely used to handle missing | that the imputation model is compatible with the | model should be reflected in the imputation process | using generalized linear mixed imputation models | use of advanced imputation procedures. In this

关键词： clustered data | fully conditional specification | joint modeling | longitudinal data | missing data | multiple imputation

摘要： Imputation (MI) is a popular method to address the presence | of missing data. MI uses an imputation model to | is multivariate imputation using chained equations | functions were included in the imputation models (the | In time-to-event analyses, a competing risk is

关键词： Competing risks | Survival analysis | Missing data | Multiple imputation | Monte Carlo simulations

摘要： multiple imputation methods proposed so far in the | multiple imputation method improves the quality of | imputation in trajectories subject to time-varying | Abstract The life course paradigm emphasizes | the need to study not only the situation at a given

关键词： Fully conditional specification | Imputation | Life course data | Missing data | Multiple imputation for categorical time series

摘要：Data imputation is crucial in the analysis of | series imputation lies in preventing the model from | for data imputation. Based on this idea, we propose | learning-validation imputation paradigm. Firstly, IRM | -the-art imputation models. The experiment results

关键词： Time Series Imputation | Iterative Reconstruction | Incomplete Representation

摘要：Data imputation addresses the challenge of | graph data imputation approach called GIG which relies | the data imputation process making it more reliable | imputing missing values in database instances, ensuring | consistency with the overall semantics of the dataset

关键词： Data imputation | Transformer | Graph difference dependency

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： features of the surface. Therefore, data imputation* must* | . Traditional surface data imputation* methods rely on simple* | imputation. This approach, which originates from surface | Spurious measurements frequently occur in | surface data from technical components. Excluding or

摘要： have been proposed for missing data imputation* and* | . Nevertheless, the inputs of imputation* networks of these* | , these methods may not provide satisfactory imputation | data imputation. The method is based on the | imputation network, namely a generator, is not incomplete

摘要： handle missing data directly, imputation* methods are* | imputation method is non-trivial, however. Moreover, our | imputation method does not always result in the best | Learning Imputation* and Regression Selection) framework* | resource-friendly recommendation on which imputation* and*

摘要： imputation strategies emerged as a possible solution for | value imputation* (MVI) methods is the presence of* | imputation using seven state-of-the-art MVI methods. Our | performing the imputation* task and evaluating the quality* | of the imputation* directly. Additionally, we*

摘要： imputation techniques for missing value imputation on real | the multiple imputation* by chained equations* | algorithm are evaluated as imputation* techniques. The* | imputation tasks. This research informs the selection and | imputation and error correction, with k-nearest neighbour

摘要：. Multiple imputation* (MI) is widely used to handle missing* | that the imputation* model is compatible with the* | model should be reflected in the imputation* process* | using generalized linear mixed imputation* models* | use of advanced imputation* procedures. In this*

摘要： Imputation (MI) is a popular method to address the presence | of missing data. MI uses an imputation* model to* | is multivariate imputation* using chained equations* | functions were included in the imputation* models (the* | In time-to-event analyses, a competing risk is

摘要： multiple imputation* methods proposed so far in the* | multiple imputation* method improves the quality of* | imputation in trajectories subject to time-varying | Abstract The life course paradigm emphasizes | the need to study not only the situation at a given

摘要：Data imputation* is crucial in the analysis of* | series imputation* lies in preventing the model from* | for data imputation. Based on this idea, we propose | learning-validation imputation* paradigm. Firstly, IRM* | -the-art imputation* models. The experiment results*

摘要：Data imputation* addresses the challenge of* | graph data imputation* approach called GIG which relies* | the data imputation* process making it more reliable* | imputing missing values in database instances, ensuring | consistency with the overall semantics of the dataset