Journal articles: 'Patterns missing'

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Nadimi-Shahraki, Mohammad H., Saeed Mohammadi, Hoda Zamani, Mostafa Gandomi, and Amir H. Gandomi. "A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis." Electronics 10, no. 24 (December 19, 2021): 3167. http://dx.doi.org/10.3390/electronics10243167.

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Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.

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Fernstad, Sara Johansson. "To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization." Information Visualization 18, no. 2 (July 25, 2018): 230–50. http://dx.doi.org/10.1177/1473871618785387.

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While missing data is a commonly occurring issue in many domains, it is a topic that has been greatly overlooked by visualization scientists. Missing data values reduce the reliability of analysis results. A range of methods exist to replace the missing values with estimated values, but their appropriateness often depend on the patterns of missingness. Increased understanding of the missingness patterns and the distribution of missing values in data may greatly improve reliability, as well as provide valuable insight into potential problems in data gathering and analyses processes, and better understanding of the data as a whole. Visualization methods have a unique possibility to support investigation and understanding of missingness patterns by making the missing values and their relationship to recorded values visible. This article provides an overview of visualization of missing data values and defines a set of three missingness patterns of relevance for understanding missingness in data. It also contributes a usability evaluation which compares visualization methods representing missing values and how well they help users identify missingness patterns. The results indicate differences in performance depending on the visualization method as well as missingness pattern. Recommendations for future design of missing data visualization are provided based on the outcome of the study.

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Orii, Hideaki, Hideaki Kawano, Hiroshi Maeda, and Norikazu Ikoma. "Image Completion Considering Local Orientations of Rotated Patterns." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 2 (March 20, 2010): 193–99. http://dx.doi.org/10.20965/jaciii.2010.p0193.

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Image completion yields whole images by producing plausible parts missing due to the removal of foreground or background elements. Conventionally, missing parts are produced by optimizing the objective function, defined based on pattern similarity between the missing region and the remaining image (data region). The resulting image may be compromised, however, by data region pattern variations. Augmenting data region pattern variations positively produced good results, but tends to cause processing search time to mushroom proportionately. To avoid this, we propose pattern extension based on rotating data region pattern variations and minimizing calculation time using the local orientation of rotated patterns. The effectiveness of this approach was demonstrated by comparing conventional and proposed methods.

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Chinn, Phyllis Zweig. "Inductive Patterns, Finite Differences, and a Missing Region." Mathematics Teacher 81, no. 6 (September 1988): 446–49. http://dx.doi.org/10.5951/mt.81.6.0446.

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The September 1985 Arithmetic Teacher contained an article entitled “On Patterns, Conjectures, and Proof: Developing Students' Mathematical Thinking.” In it, author Alba Thompson discusses some situations that can arise in exploring patterns as a problem-solving strategy. The third of her examples involves a fairly well known problem, sometimes referred to as the “problem of the missing region,” where the “obvious” pattern turns out to be incorrect (Gibbs 1973; Glenn 1968). Thompson mentions the problem and merely suggests it as an example to be used to illustrate the danger of assuming that patterns continue to hold just because they seem to be well established.

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Carpi, Laura C., Patricia M. Saco, and O. A. Rosso. "Missing ordinal patterns in correlated noises." Physica A: Statistical Mechanics and its Applications 389, no. 10 (May 2010): 2020–29. http://dx.doi.org/10.1016/j.physa.2010.01.030.

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Mew, John R. C. "Skeletodental patterns associated with missing teeth." American Journal of Orthodontics and Dentofacial Orthopedics 125, no. 3 (March 2004): A20. http://dx.doi.org/10.1016/j.ajodo.2004.01.013.

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Zhao, Jianyong, Jiachen Qiu, Danfeng Sun, and Baiping Chen. "RAEF: An Imputation Framework Based on a Gated Regulator Autoencoder for Incomplete IIoT Time-Series Data." Complexity 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/3320402.

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The number of intelligent applications available for IIoT environments is growing, but when the time-series data these applications rely on are incomplete, their performance suffers. Unfortunately, incomplete data are all too frequent to a phenomenon in the world of IIoT. A common workaround is to use imputation. However, the current methods are largely designed to reconstruct a single missing pattern, where a robust and flexible imputation framework would be able to handle many different missing patterns. Hence, the framework presented in this study, RAEF, is capable of processing multiple missing patterns. Based on a recurrent autoencoder, RAEF houses a novel neuron structure, called a gated regulator, which reduces the negative impact of different missing patterns. In a comparison of the state-of-the-art time-series imputation frameworks at a range of different missing rates, RAEF yielded fewer errors than all its counterparts.

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Ogut, Funda, Fikret Isik, Steven McKeand, and Ross Whetten. "Imputing missing genotypes: effects of methods and patterns of missing data." BMC Proceedings 5, Suppl 7 (2011): P61. http://dx.doi.org/10.1186/1753-6561-5-s7-p61.

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Lin, Jie, NianHua Li, Md Ashraful Alam, and Yuqing Ma. "Data-driven missing data imputation in cluster monitoring system based on deep neural network." Applied Intelligence 50, no. 3 (October 19, 2019): 860–77. http://dx.doi.org/10.1007/s10489-019-01560-y.

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Abstract Due to cluster instability, not in the cluster monitoring system. This paper focuses on the missing data imputation processing for the cluster monitoring application and proposes a new hybrid multiple imputation framework. This new imputation approach is different from the conventional multiple imputation technologies in the fact that it attempts to impute the missing data for an arbitrary missing pattern with a model-based and data-driven combination architecture. Essentially, the deep neural network, as the data model, extracts deep features from the data and deep features are further calculated then by a regression or data-driven strategies and used to create the estimation of missing data with the arbitrary missing pattern. This paper gives evidence that if we can train a deep neural network to construct the deep features of the data, imputation based on deep features is better than that directly on the original data. In the experiments, we compare the proposed method with other conventional multiple imputation approaches for varying missing data patterns, missing ratios, and different datasets including real cluster data. The result illustrates that when data encounters larger missing ratio and various missing patterns, the proposed algorithm has the ability to achieve more accurate and stable imputation performance.

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Li, Qian, Kate Fisher, Wenjun Meng, Bin Fang, Eric Welsh, Eric B. Haura, John M. Koomen, Steven A. Eschrich, Brooke L. Fridley, and Y. Ann Chen. "GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis." Bioinformatics 36, no. 1 (June 14, 2019): 257–63. http://dx.doi.org/10.1093/bioinformatics/btz488.

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Abstract Motivation Missingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns. Results Three proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMSimpute using simulated data (with a wide range of 80 missing patterns) and metabolomics data from the Cancer Genome Atlas breast cancer and clear cell renal cell carcinoma studies. Using Pearson correlation and normalized root mean square errors between the true and imputed abundance, we compare its performance to K-nearest neighbors’ type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum values. The results indicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across different missing patterns. In addition, GMSimpute is able to identify the features in downstream differential expression analysis with high accuracy when applied to the Cancer Genome Atlas datasets. Availability and implementation GMSimpute is on CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Neuilly, Melanie-Angela, Ming-Li Hsieh, Alex Kigerl, and Zachary K. Hamilton. "Data Missingness Patterns in Homicide Datasets: An Applied Test on a Primary Data Set." Violence and Victims 35, no. 4 (August 1, 2020): 589–614. http://dx.doi.org/10.1891/vv-d-17-00189.

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Research on homicide missing data conventionally posits a Missing At Random pattern despite the relationship between missing data and clearance. The latter, however, cannot be satisfactorily modeled using variables traditionally available in homicide datasets. For this reason, it has been argued that missingness in homicide data follows a Nonignorable pattern instead. Hence, the use of multiple imputation strategies as recommended in the field for ignorable patterns would thus pose a threat to the validity of results obtained in such a way. This study examines missing data mechanisms by using a set of primary data collected in New Jersey. After comparing Listwise Deletion, Multiple Imputation, Propensity Score Matching, and Log-Multiplicative Association Models, our findings underscore that data in homicide datasets are indeed Missing Not At Random.

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Lu, Yue, Long Zhao, Zhao Li, and Xiangjun Dong. "Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA." Symmetry 12, no. 12 (December 16, 2020): 2090. http://dx.doi.org/10.3390/sym12122090.

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Similarity analysis of DNA sequences can clarify the homology between sequences and predict the structure of, and relationship between, them. At the same time, the frequent patterns of biological sequences explain not only the genetic characteristics of the organism, but they also serve as relevant markers for certain events of biological sequences. However, most of the aforementioned biological sequence similarity analysis methods are targeted at the entire sequential pattern, which ignores the missing gene fragment that may induce potential disease. The similarity analysis of such sequences containing a missing gene item is a blank. Consequently, some sequences with missing bases are ignored or not effectively analyzed. Thus, this paper presents a new method for DNA sequence similarity analysis. Using this method, we first mined not only positive sequential patterns, but also sequential patterns that were missing some of the base terms (collectively referred to as negative sequential patterns). Subsequently, we used these frequent patterns for similarity analysis on a two-dimensional plane. Several experiments were conducted in order to verify the effectiveness of this algorithm. The experimental results demonstrated that the algorithm can obtain various results through the selection of frequent sequential patterns and that accuracy and time efficiency was improved.

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FANG, HUA, KIMBERLY ANDREWS ESPY, MARIA L. RIZZO, CHRISTIAN STOPP, SANDRA A. WIEBE, and WALTER W. STROUP. "PATTERN RECOGNITION OF LONGITUDINAL TRIAL DATA WITH NONIGNORABLE MISSINGNESS: AN EMPIRICAL CASE STUDY." International Journal of Information Technology & Decision Making 08, no. 03 (September 2009): 491–513. http://dx.doi.org/10.1142/s0219622009003508.

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Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical generality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data.

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Li, Lingling, Changyu Shen, Xiaochun Li, and James M. Robins. "On weighting approaches for missing data." Statistical Methods in Medical Research 22, no. 1 (June 24, 2011): 14–30. http://dx.doi.org/10.1177/0962280211403597.

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We review the class of inverse probability weighting (IPW) approaches for the analysis of missing data under various missing data patterns and mechanisms. The IPW methods rely on the intuitive idea of creating a pseudo-population of weighted copies of the complete cases to remove selection bias introduced by the missing data. However, different weighting approaches are required depending on the missing data pattern and mechanism. We begin with a uniform missing data pattern (i.e. a scalar missing indicator indicating whether or not the full data is observed) to motivate the approach. We then generalise to more complex settings. Our goal is to provide a conceptual overview of existing IPW approaches and illustrate the connections and differences among these approaches.

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Ruddle, Roy A., Muhammad Adnan, and Marlous Hall. "Using set visualisation to find and explain patterns of missing values: a case study with NHS hospital episode statistics data." BMJ Open 12, no. 11 (November 2022): e064887. http://dx.doi.org/10.1136/bmjopen-2022-064887.

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ObjectivesMissing data is the most common data quality issue in electronic health records (EHRs). Missing data checks implemented in common analytical software are typically limited to counting the number of missing values in individual fields, but researchers and organisations also need to understand multifield missing data patterns to better inform advanced missing data strategies for which counts or numerical summaries are poorly suited. This study shows how set-based visualisation enables multifield missing data patterns to be discovered and investigated.DesignDevelopment and evaluation of interactive set visualisation techniques to find patterns of missing data and generate actionable insights. The visualisations comprised easily interpretable bar charts for sets, heatmaps for set intersections and histograms for distributions of both sets and intersections.Setting and participantsAnonymised admitted patient care health records for National Health Service (NHS) hospitals and independent sector providers in England. The visualisation and data mining software was run over 16 million records and 86 fields in the dataset.ResultsThe dataset contained 960 million missing values. Set visualisation bar charts showed how those values were distributed across the fields, including several fields that, unexpectedly, were not complete. Set intersection heatmaps revealed unexpected gaps in diagnosis, operation and date fields because diagnosis and operation fields were not filled up sequentially and some operations did not have corresponding dates. Information gain ratio and entropy calculations allowed us to identify the origin of each unexpected pattern, in terms of the values of other fields.ConclusionsOur findings show how set visualisation reveals important insights about multifield missing data patterns in large EHR datasets. The study revealed both rare and widespread data quality issues that were previously unknown, and allowed a particular part of a specific hospital to be pinpointed as the origin of rare issues that NHS Digital did not know exist.

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Desli, Despoina, and Dimitra Gaitaneri. "Η κατανόηση των μαθηματικών μοτίβων από παιδιά Γ’ και Δ’ δημοτικού και οι στρατηγικές σκέψης τους." Preschool and Primary Education 5, no. 1 (March 6, 2017): 63. http://dx.doi.org/10.12681/ppej.10216.

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The aim of the present study was to examine how children attending the middle years of the primary school understand and extend mathematical patterns. A total of 90 students coming from grades C (N=48) and D (N=42) were asked 21 pattern tasks that were designed on the basis of two main categories (visual patterns and number patterns) and were further divided into: a) repeating visual and repeating number patterns, and b) growing visual and growing number patterns. Participants were asked to identify the pattern rules and extend the patterns by filling the missing steps. They also had to make a pattern on their own. Overall results showed similarly high performance on visual and number pattern tasks. However, the majority of the participants had a higher rate of success in repeating visual patterns and repeating number patterns compared to growing visual patterns and growing number patterns. The analysis of the strategies that children implemented in patterning revealed a great differentiation between their use and the type of pattern. More specifically, students mainly justified their pattern extensions by making reasonable connections within successive steps in the growing pattern tasks, whereas they tended to use techniques related to random predictions following repetitions of the pattern’s parts in the repeating pattern tasks. Last, participants’ preference for repeating visual patterns was found when making their own patterns.

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Agudelo, Gerardo Ernesto Rolong, Carlos Enrique Montenegro Marin, and Paulo Alonso Gaona-Garcia. "Computational Model to Support the Detection of Profiles of Missing Person in Colombia." Inteligencia Artificial 24, no. 67 (April 26, 2021): 121–28. http://dx.doi.org/10.4114/intartif.vol24iss67pp121-128.

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In the world and some countries like Colombia, the number of missing person is a phenome very worrying and growing, every year, thousands of people are reported missing all over the world, the fact that this keeps happening might indicate that there are still analyses that have not been done and tools that have not been considered in order to find patterns in the information of missing person. The present article presents a study of the way informatics and computational tools can be used to help find missing person and what patterns can be found in missing person datasets using as a study case open data about missing person in Colombia in 2017. The goal of this study is to review how computational tools like data mining and image analysis can be used to help find missing person and draw patterns in the available information about missing person. For this, first it will be review of the state of art of image analysis in real world applications was made in order to explore the possibilities when studying the photos of missing person, then a data mining process with data of missing person in Colombia was conducted to produce a set of decision rules that can explain the cause of the disappearance, as a result is generated decision rules algorithm suggest links between socioeconomic stratification, age, gender and specific locations of Colombia and the missing person reports. In conclusion, this work reviews what information about missing person is available publicly and what analysis can me made with them, showing that data mining and face recognition can be useful tools to extract patterns and identify patterns in missing person data.

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Hughes, Philip D., Philip L. Gibbard, and Jürgen Ehlers. "The “missing glaciations” of the Middle Pleistocene." Quaternary Research 96 (February 4, 2020): 161–83. http://dx.doi.org/10.1017/qua.2019.76.

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AbstractGlobal glaciations have varied in size and magnitude since the Early–Middle Pleistocene transition (~773 ka), despite the apparent regular and high-amplitude 100 ka pacing of glacial–interglacial cycles recorded in marine isotope records. The evidence on land indicates that patterns of glaciation varied dramatically between different glacial–interglacial cycles. For example, Marine Isotope Stages (MIS) 8, 10, and 14 are all noticeably absent from many terrestrial glacial records in North America and Europe. However, globally, the patterns are more complicated, with major glaciations recorded in MIS 8 in Asia and in parts of the Southern Hemisphere, such as Patagonia, for example. This spatial variability in glaciation between glacial–interglacial cycles is likely to be driven by ice volume changes in the West Antarctic Ice Sheet and associated interhemispheric connections through ocean–atmosphere circulatory changes. The weak global glacial imprint in some glacial–interglacial cycles is related to the pattern of global ice buildup. This is caused by feedback mechanisms within glacial systems themselves that partly result from long-term orbital changes driven by eccentricity.

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Weiskott, Eric. "Systematicity, a missing term in historical metrics." Language and Literature: International Journal of Stylistics 25, no. 4 (November 2016): 328–42. http://dx.doi.org/10.1177/0963947016660229.

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This essay identifies two persistent problems in the historical study of meter—nonconformant metrical patterns and metrical change—and offers a new term as a conceptual tool for understanding their interdependence. The term ‘systematic’ denotes metrical patterns that conform to synchronically operant metrical principles. The corresponding term ‘asystematic’ denotes the minority of actually occurring metrical patterns that fall outside the metrical system as such for historical reasons. All systematic patterns are necessarily metrical, but not all metrical patterns are systematic. It is argued that the systematicity/metricality distinction in historical metrics is analogous to the regularity/grammaticality distinction in historical linguistics and similarly fundamental to historical analysis. By introducing a new technical term, this essay seeks to shift the metrist’s object of study from the metrical system qua system to meter as a complex historical experience. The value of the concept of systematicity is illustrated through three case studies in asystematic metrical patterns from early English poetic traditions: verses with three metrical positions in Beowulf, lines with masculine ending in Middle English alliterative verse, and the infamous ‘broken-backed lines’ in the pentameter of John Lydgate. In each case, it is argued that the contrast between systematic and asystematic metrical patterns illuminates the diverse historical and perceptual negotiations that inevitably lie behind metered texts.

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Kim, Eunjin, Soyoung Park, Eungyung Lee, Taesung Jeong, and Jonghyun Shin. "Prevalence and Patterns of Congenitally Missing Teeth among Pediatric Patients Aged 8 - 16 in Pusan National University Dental Hospital." JOURNAL OF THE KOREAN ACADEMY OF PEDTATRIC DENTISTRY 50, no. 2 (May 31, 2023): 179–91. http://dx.doi.org/10.5933/jkapd.2023.50.2.179.

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The purpose of this study was to investigate the prevalence and patterns of congenitally missing teeth in permanent teeth excluding third molars, in patients aged 8 to 16 years who visited Pusan National University Dental Hospital from January 2010 to February 2021. This retrospective study evaluated tooth agenesis and the pattern of missing teeth represented by the tooth agenesis code by reviewing panoramic radiographs and electronic medical records of 11,759 patients, including 5,548 females and 6,211 males. The prevalence of congenitally missing teeth was 10.74% (females 11.95%, males 9.66%, <i>p</i> < 0.0001). Patients with tooth agenesis had an average of 2.22 missing teeth, and congenitally missing teeth occurred more frequently in the mandible (8.39%) than in the maxilla (4.52%, <i>p</i> < 0.0001). The mandibular second premolar (58.19%) was the most frequently missing tooth. The second premolar was the most frequently missing tooth in all quadrants (30.10%, 31.67%, 43.14%, and 35.59%) when a single tooth was absent, while the first and second premolars were the most commonly absent teeth (11.69%, 11.47%, 5.94%, and 5.24%) when two or more teeth were missing. In the relationship between maxillarymandibular antagonistic quadrants and full mouth, the 1st to 4th place of the missing patterns were all involved with the 1st and 2nd premolars. This study can be clinically helpful in establishing a treatment plan for patients with missing teeth. In addition, it can be used as basic data for molecular biological research to find out the relationship between tooth agenesis and specific genes.

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Farley, Dan, Daniel Anderson, P. Shawn Irvin, and Gerald Tindal. "Modeling Reading Growth in Grades 3 to 5 With an Alternate Assessment." Remedial and Special Education 38, no. 4 (November 19, 2016): 195–206. http://dx.doi.org/10.1177/0741932516678661.

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Modeling growth for students with significant cognitive disabilities (SWSCD) is difficult due to a variety of factors, including, but not limited to, missing data, test scaling, group heterogeneity, and small sample sizes. These challenges may account for the paucity of previous research exploring the academic growth of SWSCD. Our study represents a unique context in which a reading assessment, calibrated to a common scale, was administered statewide to students in consecutive years across Grades 3 to 5. We used a nonlinear latent growth curve pattern-mixture model to estimate students’ achievement and growth while accounting for patterns of missing data. While we observed significant intercept differences across disability subgroups, there were no significant slope differences. Incorporating missing data patterns into our models improved model fit. Limitations and directions for future research are discussed.

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Aguilera, Héctor, Carolina Guardiola-Albert, and Carmen Serrano-Hidalgo. "Estimating extremely large amounts of missing precipitation data." Journal of Hydroinformatics 22, no. 3 (February 28, 2020): 578–92. http://dx.doi.org/10.2166/hydro.2020.127.

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Abstract Accurate estimation of missing daily precipitation data remains a difficult task. A wide variety of methods exists for infilling missing values, but the percentage of gaps is one of the main factors limiting their applicability. The present study compares three techniques for filling in large amounts of missing daily precipitation data: spatio-temporal kriging (STK), multiple imputation by chained equations through predictive mean matching (PMM), and the random forest (RF) machine learning algorithm. To our knowledge, this is the first time that extreme missingness (>90%) has been considered. Different percentages of missing data and missing patterns are tested in a large dataset drawn from 112 rain gauges in the period 1975–2017. The results show that both STK and RF can handle extreme missingness, while PMM requires larger observed sample sizes. STK is the most robust method, suitable for chronological missing patterns. RF is efficient under random missing patterns. Model evaluation is usually based on performance and error measures. However, this study outlines the risk of just relying on these measures without checking for consistency. The RF algorithm overestimated daily precipitation outside the validation period in some cases due to the overdetection of rainy days under time-dependent missing patterns.

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Yin, Yi, Xi Wang, Qiang Li, Pengjian Shang, and Fengzhen Hou. "Quantifying interdependence using the missing joint ordinal patterns." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 7 (July 2019): 073114. http://dx.doi.org/10.1063/1.5084034.

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Park, Sungwoo, Hyungwoo Lee, Muyoung Kim, Taegyeom Kim, Byunghoon Lee, and Maenghyo Cho. "Multiscale simulations for exploring photo-chemical processes to mitigate the critical dimension variability of contact holes in EUV lithography." Journal of Materials Chemistry C 9, no. 26 (2021): 8189–203. http://dx.doi.org/10.1039/d1tc00891a.

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In extreme ultraviolet lithography (EUVL), the critical dimension (CD) variability of contact hole patterns results in pattern failures such as bridging or missing holes, which affects production yield.

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Birmingham, Jolene, Andrea Rotnitzky, and Garrett M. Fitzmaurice. "Pattern-mixture and selection models for analysing longitudinal data with monotone missing patterns." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 1 (January 28, 2003): 275–97. http://dx.doi.org/10.1111/1467-9868.00386.

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Yue Xu, Selene, Sandahl Nelson, Jacqueline Kerr, Suneeta Godbole, Ruth Patterson, Gina Merchant, Ian Abramson, John Staudenmayer, and Loki Natarajan. "Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity." Statistical Methods in Medical Research 27, no. 4 (July 10, 2016): 1168–86. http://dx.doi.org/10.1177/0962280216657119.

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Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.

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Babuta, Alexander, and Aiden Sidebottom. "Missing Children: On the Extent, Patterns, and Correlates of Repeat Disappearances by Young People." Policing: A Journal of Policy and Practice 14, no. 3 (September 20, 2018): 698–711. http://dx.doi.org/10.1093/police/pay066.

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Abstract Missing persons investigations are arguably the most common and costly non-crime problem the police are expected to handle, with a large proportion of all cases attributable to young people. This article investigates the prevalence, time course, distance, and correlates of repeat disappearances by children (under the age of 18 years). Using data from one UK police force for the period January 2011 to May 2013 (n = 1,885), we find that (1) nearly two-thirds of all missing child reports are repeat disappearances, (2) a small proportion of children who go missing repeatedly (15%) account for over half of all missing persons incidents, (3) children who go missing repeatedly tend to travel shorter distances than children reported missing once, and (4) the likelihood of a child going missing on multiple occasions is associated with age, being in care, a history of family conflict, and if going missing was judged to be ‘out of character’. The implications of our findings for the prevention of repeat disappearances by young people are discussed.

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Liu, Tongyu, Ju Fan, Yinqing Luo, Nan Tang, Guoliang Li, and Xiaoyong Du. "Adaptive data augmentation for supervised learning over missing data." Proceedings of the VLDB Endowment 14, no. 7 (March 2021): 1202–14. http://dx.doi.org/10.14778/3450980.3450989.

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Real-world data is dirty, which causes serious problems in (supervised) machine learning (ML). The widely used practice in such scenario is to first repair the labeled source (a.k.a. train) data using rule-, statistical- or ML-based methods and then use the "repaired" source to train an ML model. During production, unlabeled target (a.k.a. test) data will also be repaired, and is then fed in the trained ML model for prediction. However, this process often causes a performance degradation when the source and target datasets are dirty with different noise patterns , which is common in practice. In this paper, we propose an adaptive data augmentation approach, for handling missing data in supervised ML. The approach extracts noise patterns from target data, and adapts the source data with the extracted target noise patterns while still preserving supervision signals in the source. Then, it patches the ML model by retraining it on the adapted data, in order to better serve the target. To effectively support adaptive data augmentation, we propose a novel generative adversarial network (GAN) based framework, called DAGAN, which works in an unsupervised fashion. DAGAN consists of two connected GAN networks. The first GAN learns the noise pattern from the target, for target mask generation. The second GAN uses the learned target mask to augment the source data, for source data adaptation. The augmented source data is used to retrain the ML model. Extensive experiments show that our method significantly improves the ML model performance and is more robust than the state-of-the-art missing data imputation solutions for handling datasets with different missing value patterns.

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Wang, Guobo, Minglu Ma, Lili Jiang, Fengyun Chen, and Liansheng Xu. "Multiple imputation of maritime search and rescue data at multiple missing patterns." PLOS ONE 16, no. 6 (June 18, 2021): e0252129. http://dx.doi.org/10.1371/journal.pone.0252129.

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Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns. Probability density curves and overimputation diagnostics were used to explore the effects of multiple imputation. The results showed that the Data Augmentation (DA) algorithm had the characteristics of high operation efficiency and good imputation effect, but the algorithm was not suitable for data imputation when there was a high data missing rate. The EMB algorithm effectively restored the distribution of datasets with different data missing rates, and was less affected by the missing position; the EMB algorithm could obtain a good imputation effect even when there was a high data missing rate. Overimputation diagnostics could not only reflect the data imputation effect, but also show the correlation between different datasets, which was of great importance for deep data mining and imputation effect improvement. The Expectation-Maximization with Bootstrap (EMB) algorithm had a poor estimation effect on extreme data and failed to reflect the dataset’s variability characteristics.

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Agrawal, Jagottam Das. "ANN in forecasting Missing Rainfall Data." E3S Web of Conferences 405 (2023): 04017. http://dx.doi.org/10.1051/e3sconf/202340504017.

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ANN has been used to estimate rainfall data by analysing patterns and mapping the correlation between historical data and weather patterns. ANNs are a type of machine learning algorithm that is modelled on the structure of the human brain, which makes them particularly effective for solving complex problems that involve large amounts of data. Overall, the use of ANNs for estimating rainfall data is a promising area of research that has the potential to provide valuable insights into weather patterns and their impacts on the environment and human society. The potential of ANN in the estimation of missing precipitation values has been investigated in detail. This study proposes to compare the performance of conventional methods as well as different algorithms of the Artificial Neural Network method to predict missing rainfall in the Upper Tapi catchment area in the West region of India. It was found that the ANN method has an edge over the conventional methods and proved to be a better method of finding the missing rainfall data values.

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Ibrahim, Fowzia, Brian D. M. Tom, David L. Scott, and Andrew Toby Prevost. "Characterization of missing data patterns and mechanisms in longitudinal composite outcome trial in rheumatoid arthritis." Therapeutic Advances in Musculoskeletal Disease 14 (January 2022): 1759720X2211141. http://dx.doi.org/10.1177/1759720x221114103.

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Background: Composite measures, like the Disease Activity Score for 28 joints (DAS28), are key primary outcomes in rheumatoid arthritis (RA) trials. DAS28 combines four different components in a continuous measure. When one or more of these components are missing the overall composite score is also missing at intermediate or trial endpoint assessments. Objectives: This study examined missing data patterns and mechanisms in a longitudinal RA trial to evaluate how best to handle missingness when analysing composite outcomes. Design: The Tumour-Necrosis-Factor Inhibitors against Combination Intensive Therapy (TACIT) trial was an open label, pragmatic randomized multicentre two arm non-inferiority study. Patients were followed up for 12 months, with monthly measurement of the composite outcome and its components. Active RA patients were randomized to conventional disease modifying drugs (cDMARDs) or Tumour Necrosis Factor-α inhibitors (TNFis). Methods: The TACIT trial was used to explore the extent of missing data in the composite outcome, DAS28. Patterns of missing data in components and the composite outcome were examined graphically. Longitudinal multivariable logistic regression analysis assessed missing data mechanisms during follow-up. Results: Two hundred and five patients were randomized: at 12 months 59/205 (29%) had unobserved composite outcome and 146/205 (71%) had an observed DAS28 outcome; however, 34/146 had one or more intermediate assessments missing. We observed mixed missing data patterns, especially for the missing composite outcome due to one component missing rather than patient not attending thier visit. Age and gender predicted missingness components, providing strong evidence the missing observations were unlikely to be Missing Completely at Random (MCAR). Conclusion: Researchers should undertake detailed evaluations of missing data patterns and mechanisms at the final and intermediate time points, whether or not the outcome variable is a composite outcome. In addition, the impact on treatment estimates in patients who only provide data at milestone assessments need to be assessed. Trial Registration ISRCTN Number: 37438295

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Tang, Xianfeng, Huaxiu Yao, Yiwei Sun, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang. "Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5956–63. http://dx.doi.org/10.1609/aaai.v34i04.6056.

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Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of øurs for MTS forecasting with missing values and its robustness under various missing ratios.

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Stempfle, Lena, Ashkan Panahi, and Fredrik D. Johansson. "Sharing Pattern Submodels for Prediction with Missing Values." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 9882–90. http://dx.doi.org/10.1609/aaai.v37i8.26179.

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Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patterns, fitting separate pattern submodels have been proposed as a solution. However, fitting models independently does not make efficient use of all available data. Conversely, fitting a single shared model to the full data set relies on imputation which often leads to biased results when missingness depends on unobserved factors. We propose an alternative approach, called sharing pattern submodels (SPSM), which i) makes predictions that are robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels, and iii) has a short description, enabling improved interpretability. Parameter sharing is enforced through sparsity-inducing regularization which we prove leads to consistent estimation. Finally, we give conditions for when a sharing model is optimal, even when both missingness and the target outcome depend on unobserved variables. Classification and regression experiments on synthetic and real-world data sets demonstrate that our models achieve a favorable tradeoff between pattern specialization and information sharing.

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Schonberger, Shirley, Yehoshua Shapira, Aikaterini Maria Pavlidi, and Tamar Finkelstein. "Prevalence and Patterns of Permanent Tooth Agenesis among Orthodontic Patients—Treatment Options and Outcome." Applied Sciences 12, no. 23 (November 30, 2022): 12252. http://dx.doi.org/10.3390/app122312252.

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(1) Background: tooth agenesis is a very common dental anomaly of the human dentition most often affecting the maxillary anterior region, mandibular and maxillary premolar regions. (2) Purpose: the present study was aimed to evaluate the prevalence and patterns between bilateral and unilateral tooth agenesis among orthodontic individuals and to illustrate the treatment options for missing teeth and the outcome. (3) Materials and methods: Pre-treatment records, photographs and radiographs, of 3000 consecutively treated orthodontic individuals from the post-graduate clinic of Tel Aviv University were surveyed to detect permanent tooth agenesis in either dental arch. The data was recorded according to gender, and location and quantified between unilateral and bilateral agenesis. Descriptive and comparative statistical analysis were performed with t-test and Chi-square test (p < 0.05). (4) Results: permanent teeth agenesis, excluding third molars, was found in 326 individuals (11%), 139 males (43%) and 187 females (57%). Of them, 59% were missing in the maxilla and (41%) were missing in the mandible. A higher prevalence rate of bilateral missing lateral incisors in the maxilla (62 cases), followed by bilateral missing second premolars in the mandible (44 cases) compared with unilateral missing teeth. (5) Conclusions: this study found an overall prevalence of missing permanent teeth in orthodontic patients to be 11%. The female: male prevalence ratio was roughly 3:2, with a greater tendency in the maxilla than in the mandible. A higher prevalence of bilateral missing maxillary lateral incisors and mandibular second premolar than unilateral missing teeth.

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Sun, Tuo, Shihao Zhu, Ruochen Hao, Bo Sun, and Jiemin Xie. "Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms." Mathematics 10, no. 14 (July 21, 2022): 2544. http://dx.doi.org/10.3390/math10142544.

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A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.

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Zhang, Daojun, and Frits Agterberg. "Modified Weights-of-Evidence Modeling with Example of Missing Geochemical Data." Complexity 2018 (November 1, 2018): 1–12. http://dx.doi.org/10.1155/2018/7945960.

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Weights of evidence (WofE) and logistic regression (LR) are two loglinear methods for mineral potential mapping. Both models are limited by their respective basic assumptions in application. Ideally, WofE indicator patterns have the property of conditional independence (CI) with respect to the point pattern of mineral deposits to be predicted; in LR, there supposedly are no interactions between the point pattern and two or more of the indicator patterns. If the CI assumption is satisfied, estimated LR coefficients become approximately equal to WofE contrasts and the two methods produce similar results; additionally, bias then is avoided in that the sum of all estimated posterior probabilities becomes approximately equal to the number of observed discrete events. WofE allows construction of input layers that have missing data as a separate category in addition to known presence-absence type input, while logistic regression as such is not capable of handling missing data. As an improved WofE model based on LR, modified weights of evidence (MWofE) inherit the advantages of both LR and WofE, i.e., eliminates bias due to lack of CI and can handle missing data as well. Pixel or unit area input for MWofE consists of positive and negative weights for presence and absence of a pattern plus zeros for missing data. MWofE first is illustrated by application to simple examples. Next, it is applied to a study area with 20 known gold occurrences in southwestern Nova Scotia in relation to four input layers based on geological and lake geochemical data. Assuming that geochemical data were missing for the northern part of the study area, MWofE, like WofE but unlike LR, provides posterior probabilities for the entire area.

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Barnett, Adrian G., Paul McElwee, Andrea Nathan, Nicola W. Burton, and Gavin Turrell. "Identifying patterns of item missing survey data using latent groups: an observational study." BMJ Open 7, no. 10 (October 2017): e017284. http://dx.doi.org/10.1136/bmjopen-2017-017284.

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ObjectivesTo examine whether respondents to a survey of health and physical activity and potential determinants could be grouped according to the questions they missed, known as ‘item missing’.DesignObservational study of longitudinal data.SettingResidents of Brisbane, Australia.Participants6901 people aged 40–65 years in 2007.Materials and methodsWe used a latent class model with a mixture of multinomial distributions and chose the number of classes using the Bayesian information criterion. We used logistic regression to examine if participants’ characteristics were associated with their modal latent class. We used logistic regression to examine whether the amount of item missing in a survey predicted wave missing in the following survey.ResultsFour per cent of participants missed almost one-fifth of the questions, and this group missed more questions in the middle of the survey. Eighty-three per cent of participants completed almost every question, but had a relatively high missing probability for a question on sleep time, a question which had an inconsistent presentation compared with the rest of the survey. Participants who completed almost every question were generally younger and more educated. Participants who completed more questions were less likely to miss the next longitudinal wave.ConclusionsExamining patterns in item missing data has improved our understanding of how missing data were generated and has informed future survey design to help reduce missing data.

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Il Kim, Kwang, and Keon Myung Lee. "Mining of missing ship trajectory pattern in automatic identification system." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 167. http://dx.doi.org/10.14419/ijet.v7i2.12.11117.

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Background/Objectives: Ship trajectories in Vessel Traffic Service (VTS) system are generated by integrating the Automatic Identification System (AIS) or Radar system. However, the AIS system has missing data section caused by AIS device problems, radio jamming, and so on. These data have been confusing ship navigators and VTS operators.Methods/Statistical analysis: In order to extract missing AIS data, time intervals of sequent points from each ship trajectory are calculated. The section with missing AIS data is above a threshold time limit defined by characteristics. Using k-means algorithm, missing AIS data were clustered into several clusters stored by ship’s ID and sailing direction. Using association rule mining analysis, meaningful association pattern were calculated by missing AIS dataset.Findings: As a result of the association rule mining, we found several missing AIS situation patterns. In case of the west route, the probability of missing AIS situation is high when they enter the east and passenger routes. Also, the probability of missing AIS situation of passing the passenger route is high when that ship enter the LNG, east and west routes.Improvements/Applications: These results can be used to predict the probability of missing AIS data in VTS system.

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Tada, Mayu, Natsumi Suzuki, and Yoshifumi Okada. "Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset." Entropy 24, no. 2 (February 16, 2022): 286. http://dx.doi.org/10.3390/e24020286.

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Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on data trends over the entire feature space. However, individual missing values are likely to show similarity to data patterns in local feature space. In addition, most existing methods focus on single class data, while multiclass analysis is frequently required in various fields. Missing value imputation for multiclass data must consider the characteristics of each class. In this paper, we propose two methods based on closed itemsets, CIimpute and ICIimpute, to achieve missing value imputation using local feature space for multiclass matrix data. CIimpute estimates missing values using closed itemsets extracted from each class. ICIimpute is an improved method of CIimpute in which an attribute reduction process is introduced. Experimental results demonstrate that attribute reduction considerably reduces computational time and improves imputation accuracy. Furthermore, it is shown that, compared to existing methods, ICIimpute provides superior imputation accuracy but requires more computational time.

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Lin, Huazhen, Wei Liu, and Wei Lan. "Regression Analysis with Individual-Specific Patterns of Missing Covariates." Journal of Business & Economic Statistics 39, no. 1 (August 19, 2019): 179–88. http://dx.doi.org/10.1080/07350015.2019.1635486.

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Ben-Bassat, Yocheved, and Ilana Brin. "Skeletodental patterns in patients with multiple congenitally missing teeth." American Journal of Orthodontics and Dentofacial Orthopedics 124, no. 5 (November 2003): 521–25. http://dx.doi.org/10.1016/s0889-5406(03)00620-6.

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Haghighati, R., and A. Hassan. "Feature extraction in control chart patterns with missing data." Journal of Physics: Conference Series 1150 (January 2019): 012013. http://dx.doi.org/10.1088/1742-6596/1150/1/012013.

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Sprott, J. C. "A method for approximating missing data in spatial patterns." Computers & Graphics 28, no. 1 (February 2004): 113–17. http://dx.doi.org/10.1016/j.cag.2003.10.012.

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Zhong, Ming, Satish Sharma, and Pawan Lingras. "Matching Patterns for Updating Missing Values of Traffic Counts." Transportation Planning and Technology 29, no. 2 (April 2006): 141–56. http://dx.doi.org/10.1080/03081060600753461.

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Þórðarson, Andri Freyr, Andreas Baum, Mónica García, Sergio M. Vicente-Serrano, and Anders Stockmarr. "Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns." Remote Sensing 13, no. 19 (October 6, 2021): 4007. http://dx.doi.org/10.3390/rs13194007.

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Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in order to gap-fill missing information. EM Tucker belongs to a family of tensor decomposition methods that are known to offer a number of interesting properties, including the ability to directly analyze data stored in multidimensional arrays and to explicitly exploit their multiway structure, which is lost when traditional spatial-, temporal- and spectral-based methods are used. In order to evaluate the gap-filling accuracy of EM Tucker for NDVI images, we used three data sets based on advanced very-high resolution radiometer (AVHRR) imagery over the Iberian Peninsula with artificially added missing data as well as a data set originating from the Iberian Peninsula with natural missing data. The performance of EM Tucker was compared to a simple mean imputation, a spatio-temporal hybrid method, and an iterative method based on principal component analysis (PCA). In comparison, imputation of the missing data using EM Tucker consistently yielded the most accurate results across the three simulated data sets, with levels of missing data ranging from 10 to 90%.

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Konstantonis, Dimitrios, Maria Nassika, Maria Athanasiou, and Heleni Vastardis. "Subphenotypes in Non-Syndromic Orofacial Cleft Patients Based on the Tooth Agenesis Code (TAC)." Children 9, no. 3 (March 20, 2022): 437. http://dx.doi.org/10.3390/children9030437.

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Background: It was the aim of this study to investigate tooth agenesis patterns, which are expressed to different subphenotypes according to the TAC method in a spectrum of non-syndromic orofacial cleft patients. Methods: A total of 183 orofacial cleft patient records were assessed for tooth agenesis and TAC patterns. The association between TAC and sex, and cleft type was examined, and logistic regression models were additionally applied. Additionally, the distribution of missing teeth by cleft type and the tooth agenesis inter-quadrant association were examined. Results: The most frequent cleft type was CLPL (n = 72; 39.3%), while the maxillary left lateral incisor was the most frequently missing tooth that was strongly dependent on the cleft type (29.5%, p < 0.001). Of the 31 TAC patterns identified, four were the most prevalent and occurred in 80.8% of the sample, while 20 TAC patterns were unique. Cleft type contrary to sex (p = 0.405) was found to play a significant role in TAC distribution (p = 0.001). The logistic regression’s results suggested that overall, neither sex nor cleft type were associated with tooth agenesis. Prevalence of tooth agenesis in each quadrant clearly depended on cleft type; and there was a strong association found between tooth agenesis in different quadrants. Conclusions: Thirty-one different subphenotypes were identified in TAC patterns. The first four TAC patterns accounted for the 80.8% of the sample’s variability while twenty of the patterns were unique. A strong association was present between TAC pattern and cleft type. No association was found between the sex of the patient, tooth agenesis and TAC patterns. Tooth agenesis depended strongly on the cleft type, and the most frequently missing tooth was the maxillary left lateral incisor. The interquadrant association for tooth agenesis found suggests a genetic link in the etiology of clefts.

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Tan, Qihua, Mads Thomassen, Jacob v. B. Hjelmborg, Anders Clemmensen, Klaus Ejner Andersen, Thomas K. Petersen, Matthew McGue, Kaare Christensen, and Torben A. Kruse. "A Growth Curve Model with Fractional Polynomials for Analysing Incomplete Time-Course Data in Microarray Gene Expression Studies." Advances in Bioinformatics 2011 (September 27, 2011): 1–6. http://dx.doi.org/10.1155/2011/261514.

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Identifying the various gene expression response patterns is a challenging issue in expression microarray time-course experiments. Due to heterogeneity in the regulatory reaction among thousands of genes tested, it is impossible to manually characterize a parametric form for each of the time-course pattern in a gene by gene manner. We introduce a growth curve model with fractional polynomials to automatically capture the various time-dependent expression patterns and meanwhile efficiently handle missing values due to incomplete observations. For each gene, our procedure compares the performances among fractional polynomial models with power terms from a set of fixed values that offer a wide range of curve shapes and suggests a best fitting model. After a limited simulation study, the model has been applied to our human in vivo irritated epidermis data with missing observations to investigate time-dependent transcriptional responses to a chemical irritant. Our method was able to identify the various nonlinear time-course expression trajectories. The integration of growth curves with fractional polynomials provides a flexible way to model different time-course patterns together with model selection and significant gene identification strategies that can be applied in microarray-based time-course gene expression experiments with missing observations.

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Cui, Licong, Wei Zhu, Shiqiang Tao, James T. Case, Olivier Bodenreider, and Guo-Qiang Zhang. "Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT." Journal of the American Medical Informatics Association 24, no. 4 (February 19, 2017): 788–98. http://dx.doi.org/10.1093/jamia/ocw175.

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Abstract Objective: Quality assurance of large ontological systems such as SNOMED CT is an indispensable part of the terminology management lifecycle. We introduce a hybrid structural-lexical method for scalable and systematic discovery of missing hierarchical relations and concepts in SNOMED CT. Material and Methods: All non-lattice subgraphs (the structural part) in SNOMED CT are exhaustively extracted using a scalable MapReduce algorithm. Four lexical patterns (the lexical part) are identified among the extracted non-lattice subgraphs. Non-lattice subgraphs exhibiting such lexical patterns are often indicative of missing hierarchical relations or concepts. Each lexical pattern is associated with a potential specific type of error. Results: Applying the structural-lexical method to SNOMED CT (September 2015 US edition), we found 6801 non-lattice subgraphs that matched these lexical patterns, of which 2046 were amenable to visual inspection. We evaluated a random sample of 100 small subgraphs, of which 59 were reviewed in detail by domain experts. All the subgraphs reviewed contained errors confirmed by the experts. The most frequent type of error was missing is-a relations due to incomplete or inconsistent modeling of the concepts. Conclusions: Our hybrid structural-lexical method is innovative and proved effective not only in detecting errors in SNOMED CT, but also in suggesting remediation for these errors.

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Hossain, Tahera, Md Atiqur Rahman Ahad, and Sozo Inoue. "A Method for Sensor-Based Activity Recognition in Missing Data Scenario." Sensors 20, no. 14 (July 8, 2020): 3811. http://dx.doi.org/10.3390/s20143811.

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Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.

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Switzer, Fred S., Philip L. Roth, and Deborah M. Switzer. "Systematic Data Loss in HRM Settings: A Monte Carlo Analysis." Journal of Management 24, no. 6 (December 1998): 763–79. http://dx.doi.org/10.1177/014920639802400605.

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The accuracy of eight missing data techniques (MDTs) under conditions of systematically missing data was tested using a Monte Carlo analysis. Data were generated from a population correlation matrix, then deleted using several patterns that might be found in a human resource management (HRM) selection validation study. The results indicated that listwise and pairwise deletion were the most accurate methods, followed closely by imputation methods such as regression and hot-deck. Mean substitution was substantially inferior to the other methods tested. Future research that examines different missing data patterns is recommended.

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Journal articles on the topic 'Patterns missing'

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