Relevant bibliographies by topics / Cluster Analysis. Models, Statistical

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cluster Analysis. Models, Statistical'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Cluster Analysis. Models, Statistical"

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Kovacova, M., K. Valaskova, P. Durana, and J. Kliestikova. "Innovation Management of the Bankruptcy: Case Study of Visegrad Group Countries." Marketing and Management of Innovations, no. 4 (2019): 241–51. http://dx.doi.org/10.21272/mmi.2019.4-19.

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Since the first bankruptcy prediction models developed in the 60th of 20th century numerous different models have been constructed through the world. These individual models for bankruptcy prediction have been created in different time and space using different methods and variables. During this period various statistical methods have been used starting with the most popular univariate, linear and multivariate discriminant analysis, logistic regression, probit regression, decision trees, neural networks, rough sets, linear programming, principal component analysis, data envelopment analysis, s
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WARTENBERG, DANIEL, and MICHAEL GREENBERG. "DETECTING DISEASE CLUSTERS: THE IMPORTANCE OF STATISTICAL POWER." American Journal of Epidemiology 132, supp1 (1990): 156–66. http://dx.doi.org/10.1093/oxfordjournals.aje.a115778.

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Abstract A variety of methods and models have been proposed for the statistical analysis of disease excesses, yet rarely are these methods compared with respect to their ability to detect possible clusters. Evaluation of statistical power is one approach for comparing different methods. In this paper, the authors study the probability that a test will reject the null hypothesis, given that the null hypothesis is indeed false. They present a discussion of some considerations involved in power studies of cluster methods and review two methods for detecting space-time clusters of disease, one bas
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Kovacova, Maria, Tomas Kliestik, Katarina Valaskova, Pavol Durana, and Zuzana Juhaszova. "Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries." Oeconomia Copernicana 10, no. 4 (2019): 743–72. http://dx.doi.org/10.24136/oc.2019.034.

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Research background: Since the first bankruptcy prediction models were developed in the 60’s of the 20th century, numerous different models have been constructed all over the world. These individual models of bankruptcy prediction have been developed in different time and space using different methods and variables. Therefore, there is a need to analyse them in the context of various countries, while the question about their suitability arises.
 Purpose of the article: The analysis of more than 100 bankruptcy prediction models developed in V4 countries confirms that enterprises in each co
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Rahman, Ayesha S., and Ataur Rahman. "Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia." Water 12, no. 3 (2020): 781. http://dx.doi.org/10.3390/w12030781.

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This paper examines the applicability of principal component analysis (PCA) and cluster analysis in regional flood frequency analysis. A total of 88 sites in New South Wales, Australia are adopted. Quantile regression technique (QRT) is integrated with the PCA to estimate the flood quantiles. A total of eight catchment characteristics are selected as predictor variables. A leave-one-out validation is applied to determine the efficiency of the developed statistical models using an ensemble of evaluation diagnostics. It is found that the PCA with QRT model does not perform well, whereas cluster/
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McLachlan, Geoffrey J., Sharon X. Lee, and Suren I. Rathnayake. "Finite Mixture Models." Annual Review of Statistics and Its Application 6, no. 1 (2019): 355–78. http://dx.doi.org/10.1146/annurev-statistics-031017-100325.

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The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models. Because of their flexibility, mixture models are being increasingly exploited as a convenient, semiparametric way in which to model unknown distributional shapes. This is in addition to their obvious applications w
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Židanavičiūtė, Jurgita, and Audrius Vaitkus. "Application of Mixed Linear Models in the Analysis of Road Surface Features." Lietuvos statistikos darbai 54, no. 1 (2015): 101–9. http://dx.doi.org/10.15388/ljs.2015.13885.

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The data were collected by researchers at the Road Research Institute, in a study investigating the impact of differentfactors on road surface strength. In this statistical analysis, we apply linear mixed models (LMMs) to clustered longitudinal data, inwhich the units of analysis (points in the road) are nested within clusters (sample of four different road segments), and repeatedmeasures of road strength in these different points are collected over time with unequally spaced time intervals. The data arebalanced – each cluster has the same number of units, which are measured at the same number
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Jiménez, Carlos Miranda, and J. Bernardo Royo Díaz. "Statistical Model Estimates Potential Yields in Pear Cultivars `Blanquilla' and `Conference' before Bloom." Journal of the American Society for Horticultural Science 128, no. 4 (2003): 452–57. http://dx.doi.org/10.21273/jashs.128.4.0452.

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Spring frosts are usual in many of Spain's fruit-growing areas, so it is common to insure crops against frost damage. After a frost, crop loss must be evaluated, by comparing what crop is left with the amount that would have been obtained under normal conditions. Potential crop must be evaluated quickly through the use of measurements obtainable at the beginning of the tree's growth cycle. During 1996 and 1997 and in 95 commercial plots of `Blanquilla' and `Conference' pear (Pyrus communis L.), the following measurements were obtained: trunk cross-sectional area (TCA, cm2), space allocated per
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Villarroel, Luis, Guillermo Marshall, and Anna E. Barón. "Cluster analysis using multivariate mixed effects models." Statistics in Medicine 28, no. 20 (2009): 2552–65. http://dx.doi.org/10.1002/sim.3632.

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Banicescu, Ioana, Ricolindo L. Cariño, Jane L. Harvill, and John Patrick Lestrade. "Vector Nonlinear Time-Series Analysis of Gamma-Ray Burst Datasets on Heterogeneous Clusters." Scientific Programming 13, no. 2 (2005): 67–77. http://dx.doi.org/10.1155/2005/674158.

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The simultaneous analysis of a number of related datasets using a single statistical model is an important problem in statistical computing. A parameterized statistical model is to be fitted on multiple datasets and tested for goodness of fit within a fixed analytical framework. Definitive conclusions are hopefully achieved by analyzing the datasets together. This paper proposes a strategy for the efficient execution of this type of analysis on heterogeneous clusters. Based on partitioning processors into groups for efficient communications and a dynamic loop scheduling approach for load balan
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Jiménez, Carlos Miranda, and J. Bernardo Royo Díaz. "Statistical Model Estimates Potential Yields in `Golden Delicious' and `Royal Gala' Apples before Bloom." Journal of the American Society for Horticultural Science 129, no. 1 (2004): 20–25. http://dx.doi.org/10.21273/jashs.129.1.0020.

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Spring frosts are usual in many of Spain's fruit-growing areas, so it is common to insure crops against frost damage. After a frost, crop loss must be evaluated, by comparing what crop is left with the amount that would have been obtained under normal conditions. Potential crop must be evaluated quickly through the use of measurements obtainable at the beginning of the tree's growth cycle. During the years 1998 and 1999 and in 62 commercial plots of `Golden Delicious' and `Royal Gala' apple (Malus ×domestica Borkh.), the following measurements were obtained: trunk cross-sectional area (TCA, cm
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Dissertations / Theses on the topic "Cluster Analysis. Models, Statistical"

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Santiago, Calderón José Bayoán. "On Cluster Robust Models." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cgu_etd/132.

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Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may inf
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Chung, Hyoju. "GEE with large cluster sizes : high-dimensional working correlation models /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/9545.

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French, Benjamin. "Analysis of aggregate longitudinal data with time-dependent exposure /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/9569.

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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
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Gao, Dexiang. "Analysis of clustered longitudinal count data /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2007.

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Thesis (Ph.D. in Analytic Health Sciences, Department of Preventive Medicine and Biometrics) -- University of Colorado Denver, 2007.<br>Typescript. Includes bibliographical references (leaves 75-77). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
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Xiong, Yimin. "Time series clustering using ARMA models /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20XIONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 49-55). Also available in electronic version. Access restricted to campus users.
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Harvey, Eric Scott. "Normal Mixture Models for Gene Cluster Identification in Two Dimensional Microarray Data." VCU Scholars Compass, 2003. http://scholarscompass.vcu.edu/etd/1309.

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This dissertation focuses on methodology specific to microarray data analyses that organize the data in preliminary steps and proposes a cluster analysis method which improves the interpretability of the cluster results. Cluster analysis of microarray data allows samples with similar gene expression values to be discovered and may serve as a useful diagnostic tool. Since microarray data is inherently noisy, data preprocessing steps including smoothing and filtering are discussed. Comparing the results of different clustering methods is complicated by the arbitrariness of the cluster labels.
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Glaman, Ryan. "Comparing Three Approaches for Handling a Fourth Level of Nesting Structure in Cluster-Randomized Trials." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1011881/.

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This study compared 3 approaches for handling a fourth level of nesting structure when analyzing data from a cluster-randomized trial (CRT). CRTs can include 3 levels of nesting: repeated measures, individual, and cluster levels. However, above the cluster level, there may sometimes be an additional potentially important fourth level of nesting (e.g., schools, districts, etc., depending on the design) that is typically ignored in CRT data analysis. The current study examined the impact of ignoring this fourth level, accounting for it using a model-based approach, and accounting it using a desi
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Kersten, Stefan. "Statistical modelling and resynthesis of environmental texture sounds." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/400395.

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Environmental texture sounds are an integral, though often overlooked, part of our daily life. They constitute those elements of our sounding environment that we tend to perceive subconsciously but which we miss when they are missing. Those sounds are also increasingly important for adding realism to virtual environments, from immersive artificial worlds through computer games to mobile augmented reality systems. This work spans the spectrum from data-driven stochastic sound synthesis methods to distributed virtual reality environments and their aesthetic and technological implications. We pro
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Hill, Evelyn June. "Applying statistical and syntactic pattern recognition techniques to the detection of fish in digital images." University of Western Australia. School of Mathematics and Statistics, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0070.

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This study is an attempt to simulate aspects of human visual perception by automating the detection of specific types of objects in digital images. The success of the methods attempted here was measured by how well results of experiments corresponded to what a typical human’s assessment of the data might be. The subject of the study was images of live fish taken underwater by digital video or digital still cameras. It is desirable to be able to automate the processing of such data for efficient stock assessment for fisheries management. In this study some well known statistical pattern classif
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Books on the topic "Cluster Analysis. Models, Statistical"

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Sutradhar, Brajendra C. Dynamic mixed models for familial longitudinal data. Springer, 2011.

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D, Nagel, and Sator H, eds. Cluster analysis in clinical chemistry: A model. Wiley, 1987.

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McEwan, J. A. Cluster analysis and preference mapping. Campden & Chorleywood Food Research Association, 1998.

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Nonlinear statistical models. Wiley, 1987.

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Pázman, Andrej. Nonlinear statistical models. Kluwer Academic Publishers, 1993.

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Keefe, Ryan. Resource-constrained spatial hot spot identification. RAND, 2011.

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1965-, Sullivan Thomas, ed. Resource-constrained spatial hot spot identification. RAND, 2011.

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Retherford, Robert D. Statistical models for causal analysis. Wiley, 1993.

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Giudici, Paolo, Salvatore Ingrassia, and Maurizio Vichi, eds. Statistical Models for Data Analysis. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00032-9.

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Retherford, Robert D., and Minja Kim Choe. Statistical Models for Causal Analysis. John Wiley & Sons, Inc., 1993. http://dx.doi.org/10.1002/9781118033135.

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Book chapters on the topic "Cluster Analysis. Models, Statistical"

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Stecking, Ralf, and Klaus B. Schebesch. "Symbolic Cluster Representations for SVM in Credit Client Classification Tasks." In Statistical Models for Data Analysis. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00032-9_40.

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Kharin, Yurij. "Cluster Analysis under Distorted Model Assumptions." In Robustness in Statistical Pattern Recognition. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8630-6_7.

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Fox, Isaac, Shaker Srinivasan, and Paul Vaaler. "A Descriptive Alternative to Cluster Analysis: Understanding Strategic Group Performance with Simulated Annealing." In Statistical Models for Strategic Management. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2614-5_4.

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Vannucci, Giulia, Anna Gottard, Leonardo Grilli, and Carla Rampichini. "Random effects regression trees for the analysis of INVALSI data." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.07.

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Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem &amp; Larocque, 2011; Sela &amp; Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci &amp; Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability.
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Sutradhar, Brajendra C. "On cluster regression and factor analysis models with elliptic $t$ errors." In Institute of Mathematical Statistics Lecture Notes - Monograph Series. Institute of Mathematical Statistics, 1994. http://dx.doi.org/10.1214/lnms/1215463809.

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Muller, Michael William. "Managing dialogue in a statistical expert assistant with a cluster-based user model." In Advances in Intelligent Data Analysis Reasoning about Data. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052827.

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Bozdogan, Hamparsum. "Mixture-Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity." In Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0800-3_3.

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Baragona, Roberto, Francesco Battaglia, and Irene Poli. "Cluster Analysis." In Evolutionary Statistical Procedures. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16218-3_7.

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Härdle, Wolfgang Karl, and Léopold Simar. "Cluster Analysis." In Applied Multivariate Statistical Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26006-4_13.

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Härdle, Wolfgang Karl, and Léopold Simar. "Cluster Analysis." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_13.

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Conference papers on the topic "Cluster Analysis. Models, Statistical"

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Erilli, Necati Alp, and Çağatay Karaköy. "Classification of Turkish Republics with Specific Economic Indicators in Fuzzy Clustering Analysis." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01253.

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Economic indicators in economic policies have an important place in determining the levels of development. Determining and classifying the existing social and economic structures of countries is very important for examining the development states and possible development tendencies of countries and forming regional development policies in line with these. The aim in cluster analysis, is to classify datas in to similarity and perform useful knowledge for the researcher. Cluster analysis, which became more popular among the subjects of statistical classification in recent years, can give more re
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Xin Zhang, Jun Feng, Hui-Ya Wang, and Gui-ping Xu. "Clustered calcification analysis and detection for mammographic images based on statistical texture models." In 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405791.

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Zvyagin, Petr, and Kirill Sazonov. "Analysis and Probabilistic Modeling of the Unstationary Ice Loads Stochastic Process, Based on Experiments With Models of Offshore Structures." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41619.

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Experiments with models of platforms and offshore structures with vertical and inclined panels, which were conducted at Krylov Research Center (St. Petersburg), demonstrated that sometimes ice loads time series registered in these experiments cannot be considered as stationary. At the same time until nowadays methods and algorithms of probabilistic modeling were mainly based on the assumption of ice loads time series stationarity. That is because the analysis and modeling for stationary stochastic process is easier than for those unstationary. In the paper the method for determining the presen
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Buot, Max-Louis G., and Donald St P. Richards. "Statistical Models for Globular Cluster Luminosity Distribution." In Nonparametric Statistics and Mixture Models - A Festschrift in Honor of Thomas P Hettmansperger. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814340564_0005.

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KHASAEV, Gabibulla, Alexandr VLASOV, Dariya VASILIEVA, and Velta PARSOVA. "CRITERIA OF ECONOMIC EFFICIENCY OF LAND STOCK MANAGEMENT." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.250.

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One of preconditions for sustainable socio-economic development of the region can be observed as much as possible involvement of land resources in economic turnover and increasing of efficiency of their use. On the example of Samara region which is the subject of the Russian Federation are made proposals for establishment of criteria for assessment of economic efficiency of land management in specific area. Statistical data on collection of land payments (land tax and leasehold payment) in 27 municipalities of Samara region in 2004-2014 are analysed. There is investigated common information on
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Jiang, Zhen, Wei Chen, and Craig Burkhart. "A Hybrid Approach to 3D Porous Microstructure Reconstruction via Gaussian Random Field." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71173.

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Obtaining an accurate three-dimensional (3D) structure of a porous microstructure is important for assessing the material properties based on finite element analysis. While directly obtaining 3D images of the microstructure is impractical under many circumstances, two sets of methods have been developed in the literature to generate (reconstruct) 3D microstructure from its 2D images: one characterizes the microstructure based on certain statistical descriptors, typically two-point correlation function and cluster correlation function, and then performs an optimization process to build a 3D str
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Liu, Jialin, and Yong Chen. "Fast data analysis with integrated statistical metadata in scientific datasets." In 2013 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2013. http://dx.doi.org/10.1109/cluster.2013.6702623.

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Indrawan, Natarianto, Rupendranath Panday, Lawrence J. Shadle, and Umesh K. Chitnis. "Data Analytics Applied to Coal Fired Boilers for Detecting Leaks." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16912.

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Abstract Data analytics were used to detect boiler leaks from five different coal-fired boilers including both subcritical and supercritical systems. Discriminant functions were developed that detected leaks up to two weeks prior to forced plant shutdowns for repairs. The leaks were identified to occur at different sections of the boiler for each plant, including waterwalls, economizer and superheater using conventional process measurement data. Leaking conditions were detected with a high degree of confidence (≪ 1% misclassified observations) and were able to distinguish normal operations fro
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Nelson, Bryan, and Yann Quéméner. "Fatigue Life Analysis of Offshore Wind Turbine Support Structures in an Offshore Wind Farm." In ASME 2018 1st International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/iowtc2018-1061.

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This study evaluated, by time-domain simulations, the fatigue lives of several jacket support structures for 4 MW wind turbines distributed throughout an offshore wind farm off Taiwan’s west coast. An in-house RANS-based wind farm analysis tool, WiFa3D, has been developed to determine the effects of the wind turbine wake behaviour on the flow fields through wind farm clusters. To reduce computational cost, WiFa3D employs actuator disk models to simulate the body forces imposed on the flow field by the target wind turbines, where the actuator disk is defined by the swept region of the rotor in
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Brandt, J. M., A. C. Gentile, Y. M. Marzouk, and P. P. Pebay. "Meaningful Automated Statistical Analysis of Large Computational Clusters." In 2005 IEEE International Conference on Cluster Computing. IEEE, 2005. http://dx.doi.org/10.1109/clustr.2005.347090.

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Reports on the topic "Cluster Analysis. Models, Statistical"

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Sclove, Stanley L. Statistical Models and Methods for Cluster Analysis and Image Segmentation. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada169145.

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Percival, Donald B. Stochastic Models and Statistical Analysis for Clock Noise. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada427776.

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Pebay, Philippe Pierre, Janine Camille Bennett, Hemanth Kolla, and Giulio Borghesi. Scalability of Several Asynchronous Many-Task Models for In Situ Statistical Analysis. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1367233.

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Mathew, Thomas. Statistical Inference Problems in Some Multivariate Linear Models with Applications to Multivariate Calibration and Meta-Analysis. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada291125.

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Osnes, J. D., A. Winberg, J. E. Andersson, and N. A. Larsson. Analysis of well test data---Application of probabilistic models to infer hydraulic properties of fractures. [Contains list of standardized terminology or nomenclatue used in statistical models]. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5178741.

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Newman, Ken. Design and Analysis of Samonid Tagging Studies in the Columbia Basin. Volume III, Experiment Designs and Statistical Models to Estimate the Effect of Transportation on Survival of Columbia River System Salmonids. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/927608.

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&amp;R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting
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Mirel, Lisa, Cindy Zhang, Christine Cox, Ye Yeats, Félix Suad El Burai, and Golden Cordell. Comparative analysis of the National Health and Nutrition Examination Survey public-use and restricted-use linked mortality files. Centers for Disease Control and Prevention (U.S.), 2021. http://dx.doi.org/10.15620/cdc:104744.

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"Objectives—Linking national survey data with administrative data sources enables researchers to conduct analyses that would not be possible with each data source alone. Recently, the Data Linkage Program at the National Center for Health Statistics (NCHS) released updated Linked Mortality Files, including the National Health and Nutrition Examination Survey data linked to the National Death Index mortality files. Two versions of the files were released: restricted-use files available through NCHS and Federal Statistical Research Data Centers and public-use files. To reduce the reidentificatio
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Edwards, Susan L., Marcus E. Berzofsky, and Paul P. Biemer. Addressing Nonresponse for Categorical Data Items Using Full Information Maximum Likelihood with Latent GOLD 5.0. RTI Press, 2018. http://dx.doi.org/10.3768/rtipress.2018.mr.0038.1809.

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Full information maximum likelihood (FIML) is an important approach to compensating for nonresponse in data analysis. Unfortunately, only a few software packages implement FIML and even fewer have the capability to compensate for missing not at random (MNAR) nonresponse. One of these packages is Statistical Innovations’ Latent GOLD; however, the user documentation for Latent GOLD provides no mention of this capability. The purpose of this paper is to provide guidance for fitting MNAR FIML models for categorical data items using the Latent GOLD 5.0 software. By way of comparison, we also provid
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Tucker-Blackmon, Angelicque. Engagement in Engineering Pathways “E-PATH” An Initiative to Retain Non-Traditional Students in Engineering Year Three Summative External Evaluation Report. Innovative Learning Center, LLC, 2020. http://dx.doi.org/10.52012/tyob9090.

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The summative external evaluation report described the program's impact on faculty and students participating in recitation sessions and active teaching professional development sessions over two years. Student persistence and retention in engineering courses continue to be a challenge in undergraduate education, especially for students underrepresented in engineering disciplines. The program's goal was to use peer-facilitated instruction in core engineering courses known to have high attrition rates to retain underrepresented students, especially women, in engineering to diversify and broaden
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