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

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

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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Zhang, Ying, Semu Moges, and Paul Block. "Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia." Hydrology and Earth System Sciences 22, no. 1 (2018): 143–57. http://dx.doi.org/10.5194/hess-22-143-2018.

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Abstract. Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined thr

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ZHANG, FENG, and ZHUJUN WENG. "MIXTURE PRINCIPAL COMPONENT ANALYSIS MODEL FOR MULTIVARIATE PROCESSES MONITORING." Journal of Advanced Manufacturing Systems 04, no. 02 (2005): 151–66. http://dx.doi.org/10.1142/s0219686705000631.

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A mixture probabilistic principal component analysis model is proposed as a process monitoring tool in this paper. High-dimensional measurement data could be aggregated into some clusters based on the mixture distribution model, where the number of these clusters are automatically determined from the maximum likelihood estimation procedures. It was illustrated that the mixture PCA models conform to the multivariate data well in the experiments involving Gaussian mixtures. The multivariate statistical process monitoring mechanism is then developed first with the learning of a finite mixture mod

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Shaukat, Muhammad Arslan, Haafizah Rameeza Shaukat, Zakria Qadir, Hafiz Suliman Munawar, Abbas Z. Kouzani, and M. A. Parvez Mahmud. "Cluster Analysis and Model Comparison Using Smart Meter Data." Sensors 21, no. 9 (2021): 3157. http://dx.doi.org/10.3390/s21093157.

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Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters

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Li, Yun, Yoonseok Lee, Friedrich K. Port, and Bruce M. Robinson. "The impact of unmeasured within- and between-cluster confounding on the bias of effect estimatorsof a continuous exposure." Statistical Methods in Medical Research 29, no. 8 (2019): 2119–39. http://dx.doi.org/10.1177/0962280219883323.

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Unmeasured confounding almost always exists in observational studies and can bias estimates of exposure effects. Instrumental variable methods are popular choices in combating unmeasured confounding to obtain less biased effect estimates. However, we demonstrate that alternative methods may give less biased estimates depending on the nature of unmeasured confounding. Treatment preferences of clusters (e.g. physician practices) are the most frequently used instruments in instrumental variable analyses. These preference-based instrumental variable analyses are usually conducted on data clustered

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Carcieri, Stephen M., Adam L. Jacobs, and Sheila Nirenberg. "Classification of Retinal Ganglion Cells: A Statistical Approach." Journal of Neurophysiology 90, no. 3 (2003): 1704–13. http://dx.doi.org/10.1152/jn.00127.2003.

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Numerous studies have shown that retinal ganglion cells exhibit an array of responses to visual stimuli. This has led to the idea that these cells can be sorted into distinct physiological classes, such as linear versus nonlinear or on versus off. Although many classification schemes are widely accepted, few studies have provided statistical support to favor one scheme over another. Here we test whether some of the most widely used classification schemes can be statistically verified, using the mouse retina as the model system. We used a cluster analysis approach and focused on 4 standard resp

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Blume, C., and K. Matthes. "Understanding and forecasting polar stratospheric variability with statistical models." Atmospheric Chemistry and Physics Discussions 12, no. 2 (2012): 5659–78. http://dx.doi.org/10.5194/acpd-12-5659-2012.

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Abstract. The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA); a cluster method based on finite elements (FEM-VARX); a neural network, namely a multi-layer perceptron (MLP); and support vector regression (SVR). These methods model time series by incorporating all si

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Oguntona, Oluwaferanmi, Kay Ploetner, Marcia Urban, Raoul Rothfeld, and Mirko Hornung. "IMPACT OF AIRLINE BUSINESS MODELS, MARKET SEGMENTS AND GEOGRAPHICAL REGIONS ON AIRCRAFT CABIN CONFIGURATIONS." Journal of Air Transport Studies 10, no. 1 (2019): 1–38. http://dx.doi.org/10.38008/jats.v10i1.8.

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Besides the significance of estimating aircraft seat capacity for airline operating cost and yield estimation as well as for the conceptual design of aircraft, airline fleet planning requires an understanding of aircraft cabin configuration. This paper presents the impact of airline business models, market segments in terms of flight distances, and geographical regions on aircraft cabin configuration, i.e. aircraft seat capacities and installed seats per cabin class. Using the historical databases of global low-cost carriers and airline flight schedules between 2000 and 2016, two ABM clusters

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Trachenko, M. B., and O. D. Gaisha. "Evaluation of the Effectiveness of Financing Industrial Clusters." Russian Economic Journal, no. 5 (November 2019): 36–47. http://dx.doi.org/10.33983/0130-9757-2019-5-36-47.

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The article is solving an actual problem — development of a system of indicators to evaluate the effectiveness of financing industrial clusters in Russia. The article analyzes the cluster models of Russian and foreign authors, identifies their strengths and weaknesses. A universal information model of the cluster was developed, reflecting the interaction of the participants among themselves and with external stakeholders of the cluster development. The developed model has three control loops: internal cluster stakeholders, cluster, cluster's region. Each has the specificity of the movement of

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Dong, Nianbo, Jessaca Spybrook, Benjamin Kelcey, and Metin Bulus. "Power analyses for moderator effects with (non)randomly varying slopes in cluster randomized trials." Methodology 17, no. 2 (2021): 92–110. http://dx.doi.org/10.5964/meth.4003.

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Researchers often apply moderation analyses to examine whether the effects of an intervention differ conditional on individual or cluster moderator variables such as gender, pretest, or school size. This study develops formulas for power analyses to detect moderator effects in two-level cluster randomized trials (CRTs) using hierarchical linear models. We derive the formulas for estimating statistical power, minimum detectable effect size difference and 95% confidence intervals for cluster- and individual-level moderators. Our framework accommodates binary or continuous moderators, designs wit

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Kücken, Martin, Friedrich-Wilhelm Gerstengarbe, and Boris Orlowsky. "A Combination of Cluster Analysis and Kappa Statistic for the Evaluation of Climate Model Results." Journal of Applied Meteorology and Climatology 48, no. 9 (2009): 1757–65. http://dx.doi.org/10.1175/2009jamc2083.1.

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Abstract The authors present a combination of different statistical methods for the validation of climate simulation data with respect to observational data of the same spatial and temporal coverage. It is assumed that simulated data and observed data are both given as time series at locations such as grid cells or station locations. The aim of this approach is to quantify the agreement between the two spatial structures of observed and simulated data. These spatial structures consist of the spatial distributions of clusters (obtained from a cluster analysis) that contain climatologically simi

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Blume, C., and K. Matthes. "Understanding and forecasting polar stratospheric variability with statistical models." Atmospheric Chemistry and Physics 12, no. 13 (2012): 5691–701. http://dx.doi.org/10.5194/acp-12-5691-2012.

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Abstract. The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA); a cluster method based on finite elements (FEM-VARX); a neural network, namely the multi-layer perceptron (MLP); and support vector regression (SVR). These methods model time series by incorporating all

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Alamer, Ahmad A., Abdulaziz S. Almulhim, Ahmed A. Alrashed, and Ivo Abraham. "Mortality, Severity, and Hospital Admission among COVID-19 Patients with ACEI/ARB Use: A Meta-Analysis Stratifying Countries Based on Response to the First Wave of the Pandemic." Healthcare 9, no. 2 (2021): 127. http://dx.doi.org/10.3390/healthcare9020127.

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Background: The use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) is controversial for treating COVID-19 patients. We aimed to estimate pooled risks of mortality, disease severity, and hospitalization associated with ACEI/ARB use and stratify them by country and country clusters. Methods: We conducted a search in various databases through 4 July 2020 and then applied random-effects models to estimate pooled risks (ORp) across stratifications by country cluster. Clusters were chosen to reflect outbreak times (China followed by Korea/Italy, others su

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Sahour, Hossein, Mohamed Sultan, Mehdi Vazifedan, et al. "Statistical Applications to Downscale GRACE-Derived Terrestrial Water Storage Data and to Fill Temporal Gaps." Remote Sensing 12, no. 3 (2020): 533. http://dx.doi.org/10.3390/rs12030533.

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The Gravity Recovery and Climate Experiment (GRACE) has been successfully used to monitor variations in terrestrial water storage (GRACETWS) and groundwater storage (GRACEGWS) across the globe, yet such applications are hindered on local scales by the limited spatial resolution of GRACE data. Using the Lower Peninsula of Michigan as a test site, we developed optimum procedures to downscale GRACE Release-06 monthly mascon solutions. A four-fold exercise was conducted. Cluster analysis was performed to identify the optimum number and distribution of clusters (areas) of contiguous pixels of simil

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Zalisko, Olga. "MODERN MNEs FINANCIAL LEVERAGE AND CAPITAL STRUCTURE: THE COMPARATIVE ANALYSIS OF CLUSTER MODELS." ACTUAL PROBLEMS OF INTERNATIONAL RELATIONS 1, no. 127 (2016): 149–62. http://dx.doi.org/10.17721/apmv.2016.127.1.149-162.

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The cluster analysis applying the k-means method has been carried out. The analysis allowed discovering statistical features on financial leverage and capital structure of explored MNEs considering their industrial specificity as well. Clusterization was performed by thee criteria: the level of debt ratio; the level of direct financial leverage by means of the pair linear regression factor reflecting the influence of debt ratio on ROI; the level of indirect financial leverage by means of the pair linear regression factor reflecting the influence of short-term debt ratio on current market stock

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Jakub Horák, Petr Šuleř, and Jaromír Vrbka. "Analysis of Transportation Companies in the Czech Republic by the Kohonen Networks – Identification of Industry Leaders." Communications - Scientific letters of the University of Zilina 23, no. 1 (2020): A32—A43. http://dx.doi.org/10.26552/com.c.2021.1.a32-a43.

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Computational models of artificial neural networks are currently used in different areas. Accuracy of results exceeds the performance of traditional statistical techniques. Artificial neural networks as the Kohonen map may be used e.g. to identify industry leaders, thus replacing the traditional cluster analysis and other methods. The aim of this contribution is to analyse the transportation industry in the Czech Republic by the Kohonen networks and identify industry leaders. The data file contains results - division of companies into a total of 100 clusters. Each cluster is subjected to analy

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Petelski, Natalia, Darío Milesi, and Vladimiro Verre. "Strategies of innovation and appropriation. Sectoral analysis of Argentine manufacturing firms." Journal of Evolutionary Studies in Business 5, no. 1 (2020): 116–57. http://dx.doi.org/10.1344/jesb2020.1.j070.

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Following the evolutionary and neoshumpeterian theoretical framework, this research studies how the appropriation strategy of firms is formed in different industrial sector and what factors explain the use of the mechanisms that firms use to protect their innovations. The analysis is based on evidence from Argentine manufacturing firms surveyed by the National Survey of Employment Dynamics and Innovation (ENDEI) for 2010-2012. The results of the statistical analysis allow to identify three clusters at the sectoral level with differentiated characteristics in terms of their innovation activitie

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Davies, R. B., R. Hales, J. C. Harman, and S. R. Holdsworth. "Statistical Modeling of Creep Rupture Data." Journal of Engineering Materials and Technology 121, no. 3 (1999): 264–71. http://dx.doi.org/10.1115/1.2812374.

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A flexible statistical modeling framework for the analysis of creep rupture data is proposed, which offers an improvement on traditional methods of deriving creep rupture strength values and confidence limits. The paper reviews a family of models that can be used to represent the trend relationship between failure times about the trend line, and examines the reliability of extrapolations. Areas of statistical research which would lead to model improvement are discussed, such as variance heterogeneity, left censoring and allowance for the cluster (cast) structure of the data.

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Christiansen, Bo. "Atmospheric Circulation Regimes: Can Cluster Analysis Provide the Number?" Journal of Climate 20, no. 10 (2007): 2229–50. http://dx.doi.org/10.1175/jcli4107.1.

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Abstract The existence of multiple regimes in the extratropical tropospheric circulation is a hypothesis of theoretical importance with potential practical consequences. It is also a controversial hypothesis, and an abundance of conflicting results regarding both the existence and the number of regimes can be found in the literature. Studies of atmospheric regime behavior are often based on clustering methods such as k-means and mixture models. In the basic implementation of these methods the number of clusters has to be specified a priori and “How many clusters?” is a highly nontrivial questi

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Rodrigues, Paulo Jorge Canas, Rafael Almeida, and Kézia Mustafa. "The usefulness of robust multivariate methods: A case study with the menu items of a fast food restaurant chain." Ciência e Natura 42 (September 3, 2020): e17. http://dx.doi.org/10.5902/2179460x39892.

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Multivariate statistical methods have been playing an important role in statistics and data analysis for a very long time. Nowadays, with the increase in the amounts of data collected every day in many disciplines, and with the raise of data science, machine learning and applied statistics, that role is even more important. Two of the most widely used multivariate statistical methods are cluster analysis and principal component analysis. These, similarly to many other models and algorithms, are adequate when the data satisfies certain assumptions. However, when the distribution of the data is

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Pronyaeva, L. I., A. V. Pavlova, and O. A. Fedotenkova. "Inter-regional cluster formation based on the concept of spatial development." Regional Economics: Theory and Practice 18, no. 4 (2020): 688–708. http://dx.doi.org/10.24891/re.18.4.688.

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Subject. This article discusses the organizational and economic processes, and conditions for the formation and development of inter-regional clusters. Objectives. The article aims to clarify ideas and develop recommendations to assess the conditions and possibilities of inter-regional cluster forming. Methods. For the study, we used statistical analysis, graphical, and expert assessment methods, and a literature review. Results. The article proposes certain phases and an algorithm to form inter-regional clusters as potential economic growth zones of the regions. Conclusions and Relevance. The

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Feigelson, E. D., and G. J. Babu. "Statistical Methodology for Large Astronomical Surveys." Symposium - International Astronomical Union 179 (1998): 363–70. http://dx.doi.org/10.1017/s0074180900129043.

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Multiwavelength surveys present a variety of challenging statistical problems: raw data processing, source identification, source characterization and classification, and interrelations between multiwavelength properties. For these last two issues, we discuss the applicability of standard and new multivariate statistical techniques. Traditional methods such as ANOVA, principal components analysis, cluster analysis, and tests for multivariate linear hypotheses are underutilized in astronomy and can be very helpful. Newer statistical methods such as projection pursuit, multivariate splines, and

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Heo, Moonseong, Namhee Kim, Michael L. Rinke, and Judith Wylie-Rosett. "Sample size determinations for stepped-wedge clinical trials from a three-level data hierarchy perspective." Statistical Methods in Medical Research 27, no. 2 (2016): 480–89. http://dx.doi.org/10.1177/0962280216632564.

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Stepped-wedge (SW) designs have been steadily implemented in a variety of trials. A SW design typically assumes a three-level hierarchical data structure where participants are nested within times or periods which are in turn nested within clusters. Therefore, statistical models for analysis of SW trial data need to consider two correlations, the first and second level correlations. Existing power functions and sample size determination formulas had been derived based on statistical models for two-level data structures. Consequently, the second-level correlation has not been incorporated in co

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Price, Bertram, Elichia A. Venso, Mark F. Frana, Joshua Greenberg, Adam Ware, and Lee Currey. "Classification Tree Method for Bacterial Source Tracking with Antibiotic Resistance Analysis Data." Applied and Environmental Microbiology 72, no. 5 (2006): 3468–75. http://dx.doi.org/10.1128/aem.72.5.3468-3475.2006.

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ABSTRACT Various statistical classification methods, including discriminant analysis, logistic regression, and cluster analysis, have been used with antibiotic resistance analysis (ARA) data to construct models for bacterial source tracking (BST). We applied the statistical method known as classification trees to build a model for BST for the Anacostia Watershed in Maryland. Classification trees have more flexibility than other statistical classification approaches based on standard statistical methods to accommodate complex interactions among ARA variables. This article describes the use of c

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Bobkov, Aleksandr L., Igor V. Denisov, and Oksana V. Kuchmaeva. "Research of organizational structure of retail business and educational institution by methods of statistical analysis." Statistics and Economics 16, no. 1 (2019): 11–21. http://dx.doi.org/10.21686/2500-3925-2019-1-11-21.

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The purpose of the study.The purpose of the research is to define a previously developed theoretical evolution model of an organizational structure, that is based on the hypothesis that the two types of organizational structures, consecutive and parallel, succeed each other, undergoing quality changes with the growth and development of organisations, though pertaining properties which determine the structure characteristics and provide a sufficient growth of organizations’ efficiency. Following the results of the authors’ previous research, it was hypothesized that the growth of an organizatio

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Svobodová, J., and H. Urbancová. "Statistical Results of Activities Categorization in Czech Agricultural Companies." Scientia Agriculturae Bohemica 47, no. 3 (2016): 135–45. http://dx.doi.org/10.1515/sab-2016-0020.

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Abstract In today’s competitive environment, to identify and correctly adjust the individual components of the business model is an important strategic device for every entrepreneur. This paper (preliminary study) deals with different types of business models applied to the sector of small and medium-size farms in the Czech Republic. The main objective was to identify and categorize activities undertaken by Czech farmers into homogeneous clusters and offer recommendations on possible business model modification. The research was based on data from the Farm Accountancy Data Network (hereafter F

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Ivanova, N. S. "Forecasting Models for the Economic Security of Regional Clusters of the National Economy." Statistics of Ukraine 81, no. 2 (2018): 72–81. http://dx.doi.org/10.31767/su.2(81)2018.02.09.

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Introduction. The principal internal factors of the economic security of regions are defined by principal component analysis and their correlation with respective indicators is determined. Four principal factors (components) of the economic security of regions are identified. Forecasting models for the economic security of regions of the national economy are constructed by regional cluster using the results of the study. The study is performed by data on main socio-economic indicators of 24 Ukrainian regions over 2008–2015 and by economic security clusters (classified as “good”, “sufficient” a

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Abdulnassar, A. A., and Latha R. Nair. "A Comprehensive Study on the Importance of the Elbow and the Silhouette Metrics in Cluster Count Prediction for Partition Cluster Models." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 3792–806. http://dx.doi.org/10.47059/revistageintec.v11i4.2408.

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Proper selection of cluster count gives better clustering results in partition models. Partition clustering methods are very simple as well as efficient. Kmeans and its modified versions are very efficient cluster models and the results are very sensitive to the chosen K value. The partition clustering algorithms are more suitable in applications where the data are arranged in a uniform manner. This work aims to evaluate the importance of assigning cluster count value in order to improve the efficiency of partition clustering algorithms using two well known statistical methods, the Elbow metho

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Do, K.-A., G. J. McLachlan, R. Bean, and S. Wen. "Application of Gene Shaving and Mixture Models to Cluster Microarray Gene Expression Data." Cancer Informatics 5 (January 2007): 117693510700500. http://dx.doi.org/10.1177/117693510700500002.

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Researchers are frequently faced with the analysis of microarray data of a relatively large number of genes using a small number of tissue samples. We examine the application of two statistical methods for clustering such microarray expression data: EMMIX-GENE and GeneClust. EMMIX-GENE is a mixture-model based clustering approach, designed primarily to cluster tissue samples on the basis of the genes. GeneClust is an implementation of the gene shaving methodology, motivated by research to identify distinct sets of genes for which variation in expression could be related to a biological propert

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Rose, Rodrigo L., Tejas G. Puranik, and Dimitri N. Mavris. "Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives." Aerospace 7, no. 10 (2020): 143. http://dx.doi.org/10.3390/aerospace7100143.

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The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for th

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Poletti, Michele, Elisa Carretta, Laura Bonvicini, and Paolo Giorgi-Rossi. "Cognitive Clusters in Specific Learning Disorder." Journal of Learning Disabilities 51, no. 1 (2016): 32–42. http://dx.doi.org/10.1177/0022219416678407.

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The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The introduction of the single overarching diagnostic category of specific learning disorder (SLD) could underemphasize interindividual clinical differences regarding intracategory cognitive functioning and learning proficiency, according to current models of multip

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Puig, Xavier, Marti Font, and Josep Ginebra. "Bayesian Analysis of the Heterogeneity of Literary Style." Revista Colombiana de Estadística 39, no. 2 (2016): 205. http://dx.doi.org/10.15446/rce.v39n2.50151.

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<p>We proposed statistical analysis of the heterogeneity of literary style in a set of texts that simultaneously use different stylometric characteristics, like word length and the frequency of function words. The data set consists of several tables with the same number of rows, with the i-th row of all tables corresponding to the i-th text. The analysis proposed clusters the rows of all these tables simultaneously into groups with homogeneous style, based on a finite mixture of sets of multinomial models, one set for each table. Different from the usual heuristic cluster analysis approa

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Li, Zhaonan, Xinyi Xu, and Junshan Shen. "Semiparametric Bayesian analysis of accelerated failure time models with cluster structures." Statistics in Medicine 36, no. 25 (2017): 3976–89. http://dx.doi.org/10.1002/sim.7406.

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Rasheduzzaman, Md, Md Amirul Islam, and Rashedur M. Rahman. "Workload Prediction on Google Cluster Trace." International Journal of Grid and High Performance Computing 6, no. 3 (2014): 34–52. http://dx.doi.org/10.4018/ijghpc.2014070103.

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Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neur

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Zhang, M., M. Mahsin, L. Huang, et al. "LO05: A statistical analysis to estimate the spatial dynamics of opioid-related emergency medical services responses in the city of Calgary 2017." CJEM 20, S1 (2018): S8. http://dx.doi.org/10.1017/cem.2018.67.

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Introduction: Understanding the spatial distribution of opioid abuse at the local level may facilitate community intervention strategies. The purpose of this analysis was to apply spatial analytical methods to determine clustering of opioid-related emergency medical services (EMS) responses in the City of Calgary. Methods: Using opioid-related EMS responses in the City of Calgary between January 1st through October 31st, 2017, we estimated the dissemination area (DA) specific spatial randomness effects by incorporating the spatial autocorrelation using intrinsic Gaussian conditional autoregres

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Bell, Samuel S., Savin S. Chand, Kevin J. Tory, and Christopher Turville. "Statistical Assessment of the OWZ Tropical Cyclone Tracking Scheme in ERA-Interim." Journal of Climate 31, no. 6 (2018): 2217–32. http://dx.doi.org/10.1175/jcli-d-17-0548.1.

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The Okubo–Weiss–Zeta (OWZ) tropical cyclone (TC) detection scheme, which has been used to detect TCs in climate, seasonal prediction, and weather forecast models, is assessed on its ability to produce a realistic TC track climatology in the ERA-Interim product over the 25-yr period 1989 to 2013. The analysis focuses on TCs that achieve gale-force (17 m s−1) sustained winds. Objective criteria were established to define TC tracks once they reach gale force for both observed and detected TCs. A lack of consistency between storm tracks preceding this level of intensity led these track segments to

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Iliev, Iliycho, Snezhana Gocheva-Ilieva, and Chavdar Kulin. "Prediction of strontium bromide laser efficiency using cluster and decision tree analysis." EPJ Web of Conferences 167 (2018): 04002. http://dx.doi.org/10.1051/epjconf/201816704002.

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Subject of investigation is a new high-powered strontium bromide (SrBr2) vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relat

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Wartalska, Katarzyna, Bartosz Kaźmierczak, Monika Nowakowska, and Andrzej Kotowski. "Analysis of Hyetographs for Drainage System Modeling." Water 12, no. 1 (2020): 149. http://dx.doi.org/10.3390/w12010149.

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Modeling the reliability of storm water drainage systems encounters a number of methodological difficulties, especially in the selection of a reliable rainfall scenario. Many methods for creating reference hyetographs are described in the literature. The aim of the work was the analysis of the shapes of local precipitation hyetographs and the verification of the reference shapes of rainfall hyetographs used for the drainage systems designing and modeling its operation in Poland (Euler type II and DVWK models). The research material was represented by historical records of rainfall data from th

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Mátrai, Tamás, and János Tóth. "Cluster Analysis of Public Bike Sharing Systems for Categorization." Sustainability 12, no. 14 (2020): 5501. http://dx.doi.org/10.3390/su12145501.

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The world population will reach 9.8 billion by 2050, with increased urbanization. Cycling is one of the fastest developing sustainable transport solutions. With the spread of public bike sharing (PBS) systems, it is very important to understand the differences between systems. This article focuses on the clustering of different bike sharing systems around the world. The lack of a comprehensive database about PBS systems in the world does not allow comparing or evaluating them. Therefore, the first step was to gather data about existing systems. The existing systems could be categorized by grou

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Mazwi, Freedom, Rangarirai G. Muchetu, and George T. Mudimu. "Revisiting the Trimodal Agrarian Structure as a Social Differentiation Analysis Framework in Zimbabwe: A Study." Agrarian South: Journal of Political Economy: A triannual Journal of Agrarian South Network and CARES 10, no. 2 (2021): 318–43. http://dx.doi.org/10.1177/2277976020973837.

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The study of social differentiation in the countryside is often dominated by the deployment of classical analytical frameworks. This article quantitatively explores social differentiation at the sub-national level (Chiredzi and Zvimba districts in Zimbabwe), through the use of the trimodal agrarian structure (TMAS) framework. It addresses the question of whether variables outlined in TMAS (land sizes, labor, and credit) stimulate social differentiation patterns across various settlement models, which emerged after Zimbabwe’s land reform program. If so, what groups or clusters emerge and what a

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Erickson, R. P., J. L. Rodgers, and W. S. Sarle. "Statistical analysis of neural organization." Journal of Neurophysiology 70, no. 6 (1993): 2289–300. http://dx.doi.org/10.1152/jn.1993.70.6.2289.

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1. The responses of 32 taste neurons in the solitary nucleus of the rat to 12 stimuli were analyzed with multidimensional scaling (MDS) and cluster analysis (CA) procedures. These analyses of empirical taste data were compared with similar analyses of two model data sets of known configuration to help clarify the implications of these methods commonly used in forming conclusions about the organization of the taste system. 2. To relate to possible conclusions about groupings in taste, both model data sets were chosen as the best possible examples of ungrouped data, the first being completely re

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