Relevant bibliographies by topics / Partially missing datasets

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Partially missing datasets'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Partially missing datasets"

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Bottigliengo, Daniele, Giulia Lorenzoni, Honoria Ocagli, Matteo Martinato, Paola Berchialla, and Dario Gregori. "Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data." International Journal of Environmental Research and Public Health 18, no. 13 (2021): 6694. http://dx.doi.org/10.3390/ijerph18136694.

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(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective
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Lee, Jae-Hyun, Je-Hyeon Yun, Jung-Suk Han, In-Sung Luke Yeo, and Hyung-In Yoon. "Repeatability of Intraoral Scanners for Complete Arch Scan of Partially Edentulous Dentitions: An In Vitro Study." Journal of Clinical Medicine 8, no. 8 (2019): 1187. http://dx.doi.org/10.3390/jcm8081187.

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Research on whether the number or location of missing teeth affects the accuracy of intraoral scanners in partial edentulous patients is scarce. This study aimed to evaluate the precision of complete-arch scan data of various partial edentulous arches acquired by intraoral scanners. Five different maxillary models were scanned using Carestream CS3600 and Medit i500 scanners. The models employed here were control: Fully dentate; Case 1: Missing a right second premolar and a first molar; Case 2: Missing a right second premolar, a first molar, both left premolars, and a left first molar; Case 3:
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FU, ZHENG, and TAO JIANG. "COMPUTING THE BREAKPOINT DISTANCE BETWEEN PARTIALLY ORDERED GENOMES." Journal of Bioinformatics and Computational Biology 05, no. 05 (2007): 1087–101. http://dx.doi.org/10.1142/s0219720007003107.

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The total order of genes or markers on a chromosome is crucial for most comparative genomics studies. However, current gene mapping efforts might only suffice to provide a partial order of the genes on a chromosome. Several different genes or markers might be mapped at the same position due to the low resolution of gene mapping or missing data. Moreover, conflicting datasets might give rise to the ambiguity of gene order. In this paper, we consider the reversal distance and breakpoint distance problems for partially ordered genomes. We first prove that these problems are nondeterministic polyn
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Leyrat, Clémence, Shaun R. Seaman, Ian R. White, et al. "Propensity score analysis with partially observed covariates: How should multiple imputation be used?" Statistical Methods in Medical Research 28, no. 1 (2017): 3–19. http://dx.doi.org/10.1177/0962280217713032.

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Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We cal
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Bartlett, Christopher W., Brett G. Klamer, Steven Buyske, Stephen A. Petrill, and William C. Ray. "Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features." Genes 10, no. 9 (2019): 727. http://dx.doi.org/10.3390/genes10090727.

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Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordination across parties not-at-hand (i.e., informatics researchers in the future). Thus, incomplete concordance of measurements across datasets poses a major challenge for researchers seeking to combine public databases. In any given field, some measurements are fairly standard, but every organization col
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Mollentze, Nardus, Louis H. Nel, Sunny Townsend, et al. "A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data." Proceedings of the Royal Society B: Biological Sciences 281, no. 1782 (2014): 20133251. http://dx.doi.org/10.1098/rspb.2013.3251.

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We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate
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Bartlett, Jonathan W., James R. Carpenter, Kate Tilling, and Stijn Vansteelandt. "Improving upon the efficiency of complete case analysis when covariates are MNAR." Biostatistics 15, no. 4 (2014): 719–30. http://dx.doi.org/10.1093/biostatistics/kxu023.

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Abstract Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome.
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Shi, Jiarong, Wei Yang, Longquan Yong, and Xiuyun Zheng. "Low-Rank Representation for Incomplete Data." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/439417.

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Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries, gross corruptions, and outliers. As a significant component of LRMR, the model of low-rank representation (LRR) seeks the lowest-rank representation among all samples and it is robust for recovering subspace structures. This paper attempts to solve the problem of LRR with partially observed entries. Firstly, we construct a nonconvex minimization by taking the low rankness, robustness, and incompletion into consideration. Then we employ the technique of augmented Lagrange
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Koohafkan, Michael Connor, and Stanford Gibson. "Geomorphic trajectory and landform analysis using graph theory: A panel data approach to quantitative geomorphology." Progress in Physical Geography: Earth and Environment 42, no. 6 (2018): 679–96. http://dx.doi.org/10.1177/0309133318783143.

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Comparing successive datasets of GIS polygons derived from remote-sensing data is a common approach to quantify morphological change. GIS-derived datasets capture instantaneous observations or “snapshots” of the state of a system at a given time but do not explicitly capture the temporal sequences needed to characterize system processes. Comparisons between these “temporally-naive” datasets can be used to infer properties and trends of the landscape as a whole, but tracking changes in the characteristics of individual landforms (e.g. sandbars, dunes, or other surface features of interest) acro
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Chen, Brian, Bo Wu, Alireza Zareian, Hanwang Zhang, and Shih-Fu Chang. "General Partial Label Learning via Dual Bipartite Graph Autoencoder." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10502–9. http://dx.doi.org/10.1609/aaai.v34i07.6621.

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We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level — a label set partially labels an instance — to group-level: 1) a label set partially labels a group of instances, where the within-group instance-label link annotations are missing, and 2) cross-group links are allowed — instances in a group may be partially linked to the label set from another group. Such ambiguous group-level supervision is more practical in real-world scenarios
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Dissertations / Theses on the topic "Partially missing datasets"

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Yasarer, Hakan. "Decision making in engineering prediction systems." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16231.

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Doctor of Philosophy<br>Department of Civil Engineering<br>Yacoub M. Najjar<br>Access to databases after the digital revolutions has become easier because large databases are progressively available. Knowledge discovery in these databases via intelligent data analysis technology is a relatively young and interdisciplinary field. In engineering applications, there is a demand for turning low-level data-based knowledge into a high-level type knowledge via the use of various data analysis methods. The main reason for this demand is that collecting and analyzing databases can be expensive and time
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Conference papers on the topic "Partially missing datasets"

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Yamaguchi, Yuto, and Kohei Hayashi. "Tensor Decomposition with Missing Indices." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/449.

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How can we decompose a data tensor if the indices are partially missing?Tensor decomposition is a fundamental tool to analyze the tensor data.Suppose, for example, we have a 3rd-order tensor X where each element Xijk takes 1 if user i posts word j at location k on Twitter.Standard tensor decomposition expects all the indices are observed but, in some tweets, location k can be missing.In this paper, we study a tensor decomposition problem where the indices (i, j, or k) of some observed elements are partially missing.Towards the problem, we propose a probabilistic tensor decomposition model that
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Zhang, Wendong, Junwei Zhu, Ying Tai, et al. "Context-Aware Image Inpainting with Learned Semantic Priors." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/183.

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Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still challenging to restore reasonable contents as the contextual information within the missing regions tends to be ambiguous. To tackle this problem, we introduce pretext tasks that are semantically meaningful to estimating the missing contents. In particular, we perform knowledge distillation on pretext models and adapt the features to image inpainting. The learned semantic priors ought to be partially invariant between t
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Liang, Kongming, Yuhong Guo, Hong Chang, and Xilin Chen. "Incomplete Attribute Learning with auxiliary labels." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/313.

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Visual attribute learning is a fundamental and challenging problem for image understanding. Considering the huge semantic space of attributes, it is economically impossible to annotate all their presence or absence for a natural image via crowd-sourcing. In this paper, we tackle the incompleteness nature of visual attributes by introducing auxiliary labels into a novel transductive learning framework. By jointly predicting the attributes from the input images and modeling the relationship of attributes and auxiliary labels, the missing attributes can be recovered effectively. In addition, the
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Zhao, Feipeng, and Yuhong Guo. "Learning Discriminative Recommendation Systems with Side Information." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/485.

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Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item r
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Pang, Guoliang, Xionghui Wang, Jian-Fang Hu, Qing Zhang, and Wei-Shi Zheng. "DBDNet: Learning Bi-directional Dynamics for Early Action Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/126.

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Predicting future actions from observed partial videos is very challenging as the missing future is uncertain and sometimes has multiple possibilities. To obtain a reliable future estimation, a novel encoder-decoder architecture is proposed for integrating the tasks of synthesizing future motions from observed videos and reconstructing observed motions from synthesized future motions in an unified framework, which can capture the bi-directional dynamics depicted in partial videos along the temporal (past-to-future) direction and reverse chronological (future-back-to-past) direction. We then em
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