Bibliografías temáticas / Patterns missing

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Literatura académica sobre el tema "Patterns missing"

Autor: Grafiati

Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Patterns missing"

Nadimi-Shahraki, Mohammad H., Saeed Mohammadi, Hoda Zamani, Mostafa Gandomi y Amir H. Gandomi. "A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis". Electronics 10, n.º 24 (19 de diciembre de 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, n.º 2 (25 de julio de 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 y Norikazu Ikoma. "Image Completion Considering Local Orientations of Rotated Patterns". Journal of Advanced Computational Intelligence and Intelligent Informatics 14, n.º 2 (20 de marzo de 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, n.º 6 (septiembre de 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 y O. A. Rosso. "Missing ordinal patterns in correlated noises". Physica A: Statistical Mechanics and its Applications 389, n.º 10 (mayo de 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, n.º 3 (marzo de 2004): A20. http://dx.doi.org/10.1016/j.ajodo.2004.01.013.

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Zhao, Jianyong, Jiachen Qiu, Danfeng Sun y Baiping Chen. "RAEF: An Imputation Framework Based on a Gated Regulator Autoencoder for Incomplete IIoT Time-Series Data". Complexity 2021 (8 de diciembre de 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 y 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 y Yuqing Ma. "Data-driven missing data imputation in cluster monitoring system based on deep neural network". Applied Intelligence 50, n.º 3 (19 de octubre de 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 y Y. Ann Chen. "GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis". Bioinformatics 36, n.º 1 (14 de junio de 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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Tesis sobre el tema "Patterns missing"

Huang, Fang. "Modeling patterns of small scale spatial variation in soil". Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-011106-155345/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: spatial variations; nested random effects models; semivariogram models; kriging methods; multiple logistic regression models; missing; multiple imputation. Includes bibliographical references (p. 35-36).

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Lindén-Tunhult, Åsa. "Brides For Sale : A Qualitative Analysis of Missing Women, Skewed Sex Ratios and Bride Trafficking in Haryana, Northern India". Thesis, Uppsala universitet, Statsvetenskapliga institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445140.

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Population control programs such as family planning and the introduction of sex identification technologies has helped to create skewed sex ratios in northern India and particularly in the state of Haryana. Due to a surplus of men and the numbers of missing females, an organized business of bride trafficking has emerged where poor women from eastern and northeastern states of India are bought and brought to Haryana for the purpose of marriage. This thesis explores how skewed sex ratios have contributed to the phenomenon of bride trafficking in Haryana guided by the theoretical framework of violences of development which argues that there is a hidden paradox within development. This was done by conducting a conventional content analysis in order to create a deeper understanding of the phenomenon. There is scarce research on bride trafficking, therefore this study contributes with extended knowledge in order to shed a light on the increasing trade with females.

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Josifovski, Ljubomir. "Robust automatic speech recognition with missing and unreliable data". Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275021.

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Debure, Jonathan. "Détection de comportements et identification de rôles dans les réseaux sociaux". Electronic Thesis or Diss., Paris, CNAM, 2021. http://www.theses.fr/2021CNAM1290.

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Les réseaux sociaux sont devenus des outils de communication primordiaux et sont utilisés quotidiennement par des centaines de millions d’utilisateurs. Tous ces utilisateurs n’ont pas le même comportement sur ces réseaux. Si certains ont une faible activité, publient rarement des messages et suivent peu d’utilisateurs, d’autres, à l’opposé, ont une activité importante, avec de nombreux abonnés et très publient régulièrement. Le rôle important de ces utilisateurs influents en font des cibles intéressantes pour de nombreuses applications, par exemple pour la surveillance ou la publicité. Après une étude des méta-donées de ces utilisateurs, afin de d´détecter des comptes anormaux, nous présentons une approche permettant de d´détecter des utilisateurs devenant populaires. Notre approche s’appuie sur une modélisation de l’évolution de la popularité sous la forme de motifs fréquents. Ces motifs décrivent les comportements de gain en popularité. Nous proposons un modèle de matching des motifs permettant d’être utilisé avec un flux de données et, nous montrons sa capacité à passer à l’échelle en le comparant à des modèles classiques. Enfin, nous présentons une approche de clustering basé sur le PageRank. Ces travaux permettent d’identifier des groupes d’utilisateurs partageant le même rolex, en utilisant les graphes ’interactions qu’ils génèrent
Social networks (SN) are omnipresent in our lives today. Not all users have the same behavior on these networks. If some have a low activity, rarely posting messages and following few users, some others at the other extreme have a significant activity, with many followers and regularly posts. The important role of these popular SN users makes them the target of many applications for example for content monitoring or advertising. After a study of the metadata of these users, in order to detect abnormal accounts, we present an approach allowing to detect users who are becoming popular. Our approach is based on modeling the evolution of popularity in the form of frequent patterns. These patterns describe the behaviors of gaining popularity. We propose a pattern matching model which can be used with a data stream and we show its scalability and its performance by comparing it to classic models. Finally, we present a clustering approach based on PageRank. This work allow to identify groups of users sharing the same role, using the interaction graphs

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Yaddanapudi, Suryanarayana. "High-risk Patient Identification: Patient Similarity, Missing Data Analysis, and Pattern Visualization". University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1460731557.

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Vines, Roger. "MISSILE ANTENNA PATTERNS FOR WIDELY-SPACED MULTI-ELEMENT ARRAYS". International Foundation for Telemetering, 2005. http://hdl.handle.net/10150/605040.

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ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada
Multiple discrete antennas distributed around the circumference of a large missile and driven by one transmitter are sometimes used to radiate telemetry omnidirectionally. But driving discrete antennas separated by several wavelengths around the missile body with a single transmitter can result in an antenna pattern with deep nulls in the roll plane. Varying the relative signal phase or amplitude among the signals driving the antennas as well as the polarization of the antennas can be used to change the nulls in an attempt to decrease the null depth. In this paper the effects of phase, amplitude, and polarization on the roll-plane pattern are examined and measurement data presented.

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Josefsson, Maria. "Attrition in Studies of Cognitive Aging". Doctoral thesis, Umeå universitet, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-82514.

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Longitudinal studies of cognition are preferred to cross-sectional stud- ies, since they offer a direct assessment of age-related cognitive change (within-person change). Statistical methods for analyzing age-related change are widely available. There are, however, a number of challenges accompanying such analyzes, including cohort differences, ceiling- and floor effects, and attrition. These difficulties challenge the analyst and puts stringent requirements on the statistical method being used. The objective of Paper I is to develop a classifying method to study discrepancies in age-related cognitive change. The method needs to take into account the complex issues accompanying studies of cognitive aging, and specifically work out issues related to attrition. In a second step, we aim to identify predictors explaining stability or decline in cognitive performance in relation to demographic, life-style, health-related, and genetic factors. In the second paper, which is a continuation of Paper I, we investigate brain characteristics, structural and functional, that differ between suc- cessful aging elderly and elderly with an average cognitive performance over 15-20 years. In Paper III we develop a Bayesian model to estimate the causal effect of living arrangement (living alone versus living with someone) on cog- nitive decline. The model must balance confounding variables between the two living arrangement groups as well as account for non-ignorable attrition. This is achieved by combining propensity score matching with a pattern mixture model for longitudinal data. In paper IV, the objective is to adapt and implement available impu- tation methods to longitudinal fMRI data, where some subjects are lost to follow-up. We apply these missing data methods to a real dataset, and evaluate these methods in a simulation study.

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Fiero, Mallorie H. "Statistical Approaches for Handling Missing Data in Cluster Randomized Trials". Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612860.

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In cluster randomized trials (CRTs), groups of participants are randomized as opposed to individual participants. This design is often chosen to minimize treatment arm contamination or to enhance compliance among participants. In CRTs, we cannot assume independence among individuals within the same cluster because of their similarity, which leads to decreased statistical power compared to individually randomized trials. The intracluster correlation coefficient (ICC) is crucial in the design and analysis of CRTs, and measures the proportion of total variance due to clustering. Missing data is a common problem in CRTs and should be accommodated with appropriate statistical techniques because they can compromise the advantages created by randomization and are a potential source of bias. In three papers, I investigate statistical approaches for handling missing data in CRTs. In the first paper, I carry out a systematic review evaluating current practice of handling missing data in CRTs. The results show high rates of missing data in the majority of CRTs, yet handling of missing data remains suboptimal. Fourteen (16%) of the 86 reviewed trials reported carrying out a sensitivity analysis for missing data. Despite suggestions to weaken the missing data assumption from the primary analysis, only five of the trials weakened the assumption. None of the trials reported using missing not at random (MNAR) models. Due to the low proportion of CRTs reporting an appropriate sensitivity analysis for missing data, the second paper aims to facilitate performing a sensitivity analysis for missing data in CRTs by extending the pattern mixture approach for missing clustered data under the MNAR assumption. I implement multilevel multiple imputation (MI) in order to account for the hierarchical structure found in CRTs, and multiply imputed values by a sensitivity parameter, k, to examine parameters of interest under different missing data assumptions. The simulation results show that estimates of parameters of interest in CRTs can vary widely under different missing data assumptions. A high proportion of missing data can occur among CRTs because missing data can be found at the individual level as well as the cluster level. In the third paper, I use a simulation study to compare missing data strategies to handle missing cluster level covariates, including the linear mixed effects model, single imputation, single level MI ignoring clustering, MI incorporating clusters as fixed effects, and MI at the cluster level using aggregated data. The results show that when the ICC is small (ICC ≤ 0.1) and the proportion of missing data is low (≤ 25\%), the mixed model generates unbiased estimates of regression coefficients and ICC. When the ICC is higher (ICC > 0.1), MI at the cluster level using aggregated data performs well for missing cluster level covariates, though caution should be taken if the percentage of missing data is high.

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Bishop, Brenden. "Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153.

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Ridley, Gary J. "Leadership development in native Alaskan churches teaching biblical leadership principles in the light of an analysis of traditional patterns of leadership /". Theological Research Exchange Network (TREN), 1990. http://www.tren.com.

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Libros sobre el tema "Patterns missing"

Bartlett, Sheryl Anne. Predictive and posterior distributions for normal multivariate data with missing monotone patterns. Toronto: University of Toronto, Dept. of Statistics, 1985.

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H, Gray Alastair y Wright David F, eds. Local church evangelism: Patterns and approaches. Edinburgh: Saint Andrew, 1987.

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Contextualization in the New Testament: Patterns for theology and mission. Downers Grove, Ill: InterVarsity Press, 2005.

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Flemming, Dean E. Contextualization in the New Testament: Patterns for theology and mission. Downers Grove, IL: InterVarsity Press, 2005.

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Lois, Barrett, ed. Treasure in clay jars: Patterns in missional faithfulness. Grand Rapids, Mich: Eerdmans, 2004.

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Jones, E. Stanley. The reconstruction of the church, on what pattern? Nashville: Abingdon Press, 1992.

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William, Gibson. Pattern recognition. New York: G.P. Putnam's Sons, 2003.

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Gibson, William. Pattern recognition. London: Viking, 2003.

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Gibson, William. Pattern Recognition. USA: RB large print, 2003.

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William, Gibson. Pattern Recognition. London: Penguin Group UK, 2009.

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Capítulos de libros sobre el tema "Patterns missing"

Inenaga, Shunsuke, Teemu Kivioja y Veli Mäkinen. "Finding Missing Patterns". En Lecture Notes in Computer Science, 463–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30219-3_39.

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Arora, Teena, Venki Balasubramanian y Andrew Stranieri. "Missing Health Data Pattern Matching Technique for Continuous Remote Patient Monitoring". En Digital Health Transformation, Smart Ageing, and Managing Disability, 130–43. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_11.

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AbstractRemote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns.

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van Stein, Bas, Wojtek Kowalczyk y Thomas Bäck. "Analysis and Visualization of Missing Value Patterns". En Information Processing and Management of Uncertainty in Knowledge-Based Systems, 187–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40581-0_16.

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Ottenberg, Karsten. "Brightness-contrast diffusion and the grouping of missing angles". En Computer Analysis of Images and Patterns, 133–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57233-3_18.

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Becker, Stefan, Ronny Hug, Wolfgang Huebner, Michael Arens y Brendan T. Morris. "Handling Missing Observations with an RNN-based Prediction-Update Cycle". En Computer Analysis of Images and Patterns, 311–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89128-2_30.

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Little, Roderick J. A. y Donald B. Rubin. "Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse". En Statistical Analysis with Missing Data, 164–89. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119013563.ch8.

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Silvertown, Jonathan y Bridget Smith. "Gaps in the canopy: the missing dimension in vegetation dynamics". En Temporal and Spatial Patterns of Vegetation Dynamics, 57–60. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2275-4_6.

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Dorasamy, Kribashnee, Leandra Webb-Ray y Jules-Raymond Tapamo. "A Missing Singular Point Resistant Fingerprint Classification Technique, Based on Directional Patterns". En Computer Analysis of Images and Patterns, 178–89. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64698-5_16.

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Ting, I.-Hsien, Chris Kimble y Daniel Kudenko. "A Pattern Restore Method for Restoring Missing Patterns in Server Side Clickstream Data". En Web Technologies Research and Development - APWeb 2005, 501–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31849-1_49.

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Bhagwati, Jagdish N. "The Pass-Through Puzzle: The Missing Prince from Hamlet". En International Economic Interdependence, Patterns of Trade Balances and Economic Policy Coordination, 103–13. London: Palgrave Macmillan UK, 1992. http://dx.doi.org/10.1007/978-1-349-22256-8_5.

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Actas de conferencias sobre el tema "Patterns missing"

Anand, Priya, Jungwoo Ryoo y Rick Kazman. "Vulnerability-Based Security Pattern Categorization in Search of Missing Patterns". En 2014 Ninth International Conference on Availability, Reliability and Security (ARES). IEEE, 2014. http://dx.doi.org/10.1109/ares.2014.71.

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Verdin, Berenice y Patrick Debroux. "Reconstruction of missing sections of radiation patterns using compressive sensing". En 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. IEEE, 2015. http://dx.doi.org/10.1109/aps.2015.7304777.

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Rocha, J. C. F. da, A. Campos Jr., R. M. Etto, C. W. Galvão, G. L. Fedacz, R. R. da Silva y A. S. S. Oliveira. "Exploring conditional missing patterns for automated bacteria identification using MALDI-TOF MS data". En Congresso Brasileiro de Agroinformática. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/sbiagro.2023.26562.

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Training classifiers for automated bacterial identification using MALDI-TOF fingerprints requires addressing class-conditional missingness patterns (CMPs). A CMP is a non-missing-at-random pattern that provides evidence for classification. One possible strategy to handle CMPs is feature stratification. This work evaluated the effectiveness of stratification in training naive Bayes classifiers for the proposed task through two experiments. The first experiment compared the predictive performance of categorical naive Bayes classifiers trained on stratified/discretized features with the performance of a Gaussian naive Bayes fitted on imputed data. The second experiment assessed the impact of class imbalance on the differences in the performance of Gaussian and categorical naive Bayes classifiers. The ANOVA results suggest that feature stratification can induce more accurate classifiers. Correlation analysis shows that class imbalance has a low influence on the difference in the performances of classifiers.

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Hassaan, Syed M., Qiang Shen y Sze Zheng Yong. "Bounded-Error Estimator Design with Missing Data Patterns via State Augmentation". En 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815097.

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Bechny, Michal, Florian Sobieczky, Jürgen Zeindl y Lisa Ehrlinger. "Missing Data Patterns: From Theory to an Application in the Steel Industry". En SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3468791.3468841.

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Ilhan, Fatih y Emre Mumcuoglu. "Performance Analysis of Semi-Supervised Learning Methods under Different Missing Label Patterns". En 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302465.

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Netto, Gustavo Marques y Manuel M. de Oliveira Neto. "Robust Point-Cloud Registration based on Dense Point Matching and Probabilistic Modeling". En Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sibgrapi.est.2023.27445.

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We present techniques for 3D point-cloud registration that are suited for scenarios where robustness to outliers and missing regions is necessary, besides being applicable to both rigid and non-rigid configurations. Our techniques exploit advantages from deep learning models for dense point matching and from recent advances in probabilistic modeling of point-cloud registration. Such a combination produces context awareness and resilience to outliers and missing information. We demonstrate their effectiveness by comparing them to state-of-the-art methods and showing that ours achieves superior results in general. For example, our approaches achieve registration error up to 45% smaller than these methods in partial point clouds for non-rigid registration, and up to 49% smaller on rigid registration.

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Lee, Dongjin y Kijung Shin. "Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers". En 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00078.

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Miyata, Rikuho, Hironori Washizaki, Kensuke Sumoto, Nobukazu Yoshioka, Yoshiaki Fukazawa y Takao Okubo. "Identifying missing relationships of CAPEC attack patterns by transformer models and graph structure". En 2023 IEEE/ACM 1st International Workshop on Software Vulnerability (SVM). IEEE, 2023. http://dx.doi.org/10.1109/svm59160.2023.00008.

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Kim, Yeo Jin y Min Chi. "Temporal Belief Memory: Imputing Missing Data during RNN Training". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/322.

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We propose a bio-inspired approach named Temporal Belief Memory (TBM) for handling missing data with recurrent neural networks (RNNs). When modeling irregularly observed temporal sequences, conventional RNNs generally ignore the real-time intervals between consecutive observations. TBM is a missing value imputation method that considers the time continuity and captures latent missing patterns based on irregular real time intervals of the inputs. We evaluate our TBM approach with real-world electronic health records (EHRs) consisting of 52,919 visits and 4,224,567 events on a task of early prediction of septic shock. We compare TBM against multiple baselines including both domain experts' rules and the state-of-the-art missing data handling approach using both RNN and long-short term memory. The experimental results show that TBM outperforms all the competitive baseline approaches for the septic shock early prediction task.

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Informes sobre el tema "Patterns missing"

Eckert, Fabian, Teresa Fort, Peter Schott y Natalie Yang. Imputing Missing Values in the US Census Bureau's County Business Patterns. Cambridge, MA: National Bureau of Economic Research, enero de 2020. http://dx.doi.org/10.3386/w26632.

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Villoria, Nelson B. Estimation of Missing Intra-African Trade. GTAP Research Memoranda, diciembre de 2008. http://dx.doi.org/10.21642/gtap.rm12.

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Missing trade is defined as the exports and imports that may have taken place between two potential trading partners, but which are unknown to the researcher because neither partner reported them to the United Nation’s COMTRADE, the official global repository of trade statistics. In a comprehensive sample of African countries, over 40% of the potential trade flows fit this definition. For a continent whose trade integration remains an important avenue for development, this lack of information hinders the analysis of policy mechanisms -- such as the Economic Partnership Agreements with the EU -- that influence intra-regional trade patterns. This paper estimates the likely magnitude of the missing trade by modeling the manufacturing trade data in the GTAP Data Base using a gravity approach. The gravity approach employed here relates bilateral trade to country size, distance, and other trade costs while explicitly considering that high fixed costs can totally inhibit trade. This last feature provides an adequate framework to explain the numerous zero-valued flows that characterize intra-African trade. The predicted missing exports are valued at approximately 300 million USD. The incidence of missing trade is highest in the lowest income countries of Central and West Africa.

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Peterson, Brian, J. M. Hutchinson, Bijan Gurung, J. Beeco y Damon Joyce. Exploring spatial patterns of overflights at Badlands National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2303602.

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This study explored spatial patterns of overflights at Badlands National Park (BADL). Overflights were analyzed from July 13, 2022 to August 27, 2022 (46 days) using Automatic Dependent Surveillance-Broadcast (ADS-B) data. There were 26 days of data and 20 days of missing data due to technical difficulties with the ADS-B logger. The first phase of analysis focused on all overflights and found overflights above most areas of BADL. The second phase of analysis focused on low-level overflights that fly below 7,000ft mean sea level (MSL) and fly within 10-miles of the BADL boundary, which found a concentration of flights between 4,000 - 7,000 feet MSL near the eastern boundary of BADL. The third phase of analysis removed all overflights known not to be air tours. Figures were produced that display all the flights of the cleaned dataset, waypoints across AGL altitudes, waypoints across MSL altitudes, flightpaths for fixed wing single engine, flightpaths for rotorcraft, flight paths for fixed wing multi engine, and flightpaths for specific air tour operators. This information can be used for planning and management purposes and this study serves as a resource for future research.

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Peterson, Bian, J. Hutchinson, Bijan Gurung, Adam Beeco, Ashley Pipkin, Sharolyn Anderson y Damon Joyce. Exploring spatial patterns of overflights at Grand Canyon National Park. National Park Service, julio de 2023. http://dx.doi.org/10.36967/2299660.

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This study explored spatial patterns of overflights at Grand Canyon National Park (GRCA). Overflights were analyzed from February 5th, 2020 to January 9th, 2023 (975 days of data; 95 days of missing data) using Automatic Dependent Surveillance-Broadcast (ADS-B) data. The first phase of analysis focused on all overflights and found a high concentration of overflights above GRCA and definitive travel patterns across the park. The second phase of analysis focused on low-level overflights that fly below 10,000 ft mean sea level (MSL) and fly within 10-miles of the GRCA boundary. Phase 2 figures display yearly overflights segmented by aircraft type and show a concentration of flights between 7,000 – 10,000 ft MSL near the southeast boundary of GRCA. The third phase of analysis removed all overflights known to not be air tours. Kernel density analysis was conducted using waypoints segmented into 500 ft above ground level (AGL) altitude intervals. The altitude interval with the highest density of overflights was ‘3,001 – 3,500 ft AGL’. This information can be used for planning and management purposes and this study serves as a resource for future research that intends to use more advanced analytics.

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Huang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen y Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), febrero de 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.

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Peterson, Brian, J. M. Hutchinson, Bijan Gurung, J. Beeco y Damon Joyce. Exploring spatial patterns of overflights at Hawai?i Volcanoes National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2303507.

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This study explored spatial patterns of overflights at Hawai?i Volcanoes National Park (HAVO). Overflights were analyzed from: 1) December 28th, 2020?May 20th, 2021; 2) August 30th, 2021?September 8th, 2021; and 3) January 7th, 2022?April 14th, 2023 (617 total days; 50 days of missing data) using Automatic Dependent Surveillance-Broadcast (ADS-B) data. Phase 1 of analysis focused on all overflights and found a high concentration of overflights above HAVO and definitive travel patterns across the east side of the park. Phase 2 of analysis focused on low-level overflights that fly below 15,000ft mean sea level (MSL) and fly within 10 miles of the HAVO boundary and found that above 6,000ft MSL, the number of waypoints decreases as altitude increases. Also, as altitude increases, the directionality of waypoints trends toward the northeast. Phase 3 of analysis removed all overflights that were civil patrol flights, major airlines, and survey flights. The remaining flights were low-level overflights, including air tours. Kernel density analysis was conducted using waypoints segmented into 500ft above ground level (AGL) altitude intervals. The altitude interval with the highest density of overflights was ?1,501?2,000ft AGL?. This information can be used for planning and management purposes and this study serves as a resource for future research that intends to use more advanced analytics.

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Badel, Alejandro y Mark Huggett. Interpreting Life-Cycle Inequality Patterns as an Efficient Allocation: Mission Impossible? Federal Reserve Bank of St. Louis, 2010. http://dx.doi.org/10.20955/wp.2010.046.

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Levy, Brian. How Political Contexts Influence Education Systems: Patterns, Constraints, Entry Points. Research on Improving Systems of Education (RISE), diciembre de 2022. http://dx.doi.org/10.35489/bsg-rise-2022/pe04.

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This paper synthesises the findings of a set of country studies commissioned by the RISE Programme to explore the influence of politics and power on education sector policymaking and implementation. The synthesis groups the countries into three political-institutional contexts: Dominant contexts, where power is centred around a political leader and a hierarchical governance structure. As the Vietnam case details, top-down leadership potentially can provide a robust platform for improving learning outcomes. However, as the case studies of Ethiopia, Indonesia, Nigeria, and Tanzania illustrate, all-too-often dominant leaders’ goals vis-à-vis the education sector can veer in other directions. In impersonal competitive contexts, a combination of strong formal institutions and effective processes of resolving disagreements can, on occasion, result in a shared commitment among powerful interests to improve learning outcomes—but in none of the case studies is this outcome evident. In Peru, substantial learning gains have been achieved despite messy top-level politics. But the Chilean, Indian, and South African case studies suggest that the all-too-common result of rule-boundedness plus unresolved political contestation over the education sector’s goals is some combination of exaggerated rule compliance and/or performative isomorphic mimicry. Personalised competitive contexts (Bangladesh, Ghana, and Kenya for example) lack the seeming strengths of either their dominant or their impersonal competitive contexts; there are multiple politically-influential groups and multiple, competing goals—but no credible framework of rules to bring coherence either to political competition or to the education bureaucracy. The case studies show that political and institutional constraints can render ineffective many specialised sectoral interventions intended to improve learning outcomes. But they also point to the possibility that ‘soft governance’ entry points might open up some context-aligned opportunities for improving learning outcomes. In dominant contexts, the focus might usefully be on trying to influence the goals and strategies of top-level leadership. In impersonal competitive contexts, it might be on strengthening alliances between mission-oriented public officials and other developmentally-oriented stakeholders. In personalised competitive contexts, gains are more likely to come from the bottom-up—via a combination of local-level initiatives plus a broader effort to inculcate a shared sense among a country’s citizenry of ‘all for education’.

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Morrison, Douglas. Resource Allocation Patterns at U.S. Community Colleges: An Evaluation of Priorities, Efficiency and the Historical Mission 1981-82 Through 1986-87. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.1137.

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