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Книги з теми "Statistical graph analysis"

Автор: Grafiati

Опубліковано: 13 лютого 2024

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1

Kalyagin, V. A., A. P. Koldanov, P. A. Koldanov, and P. M. Pardalos. Statistical Analysis of Graph Structures in Random Variable Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60293-2.

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2

Langtangen, Hans Petter. Solving PDEs in Python: The FEniCS Tutorial I. Cham: Springer Nature, 2017.

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3

Structure in complex networks. Berlin: Springer, 2009.

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4

Basford, Kaye E. Graphical analysis of multiresponse data: Illustrated with a plant breeding trial : interdisciplinary statistics. Boca Raton, Fla: Chapman & Hall/CRC, 1999.

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5

Basford, Kaye E. Graphical analysis of multiresponse data: Illustrated with a plant breeding trial. Boca Raton, Fla: Chapman & Hall/CRC, 1999.

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6

Whittaker, J. Graphical models in applied multivariate statistics. Chichester [England]: Wiley, 1990.

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7

Philippe, Mathis, ed. Graphs and networks. London: ISTE, 2007.

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8

Barthélemy, Jean-Pierre. Trees and proximity representations. Chichester: Wiley, 1991.

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9

Philippe, Mathis, ed. Graphs and networks: Multilevel modeling. 2nd ed. London: J. Wiley & Sons, 2010.

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10

Philippe, Mathis, ed. Graphs and networks: Multilevel modeling. 2nd ed. London: J. Wiley & Sons, 2010.

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11

Mathis, Philippe. Graphs and networks: Multilevel modeling. 2nd ed. London: J. Wiley & Sons, 2010.

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12

Classification and regression trees. New York, N.Y: Chapman & Hall, 1993.

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13

H, Andersen H., ed. Linear and graphical models: For the multivariate complex normal distribution. New York: Springer-Verlag, 1995.

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14

Horton, Gary A. Humboldt County graph and data book: A graphical presentation of waterbasin, hydrologic, and county socioeconomic databases and analysis. Carson City, Nev: Nevada Division of Water Planning, 1996.

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15

Litvinov, G. L. (Grigoriĭ Lazarevich), 1944- editor of compilation and Sergeev, S. N., 1981- editor of compilation, eds. Tropical and idempotent mathematics and applications: International Workshop on Tropical and Idempotent Mathematics, August 26-31, 2012, Independent University, Moscow, Russia. Providence, Rhode Island: American Mathematical Society, 2014.

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16

Bretschneider, Sarah. Mathematical Models for Evacuation Planning in Urban Areas. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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17

Sebastian, Rudolph, Wilson Nic, Howse John, Corby Olivier, and SpringerLink (Online service), eds. Graph Structures for Knowledge Representation and Reasoning: Second International Workshop, GKR 2011, Barcelona, Spain, July 16, 2011. Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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18

1973-, Warzel Simone, ed. Random operators: Disorder effects on quantum spectra and dynamics. Providence, Rhode Island: American Mathematical Society, 2015.

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19

Pardalos, P. M. Statistical Analysis of Graph Structures in Random Variable Networks. Springer International Publishing AG, 2020.

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20

Statistical And Evolutionary Analysis Of Biological Networks. Imperial College Press, 2010.

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21

OGUNBIYI, Taiwo. LEFT-SIDE GRAPH and RIGHT-SIDE LINED COMPOSITION BOOK: 120 Pages Graph Paper for Engineering and Statistical Analysis 8 By 10. Independently Published, 2020.

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22

Yohannes, Yisehac, and Patrick Webb. Classification and Regression Trees, Cart: A User Manual for Identifying Indicators of Vulnerability to Famine and Chronic Food Insecurity (Microcomputers in Policy Research, 3). Intl Food Policy Research Inst, 1999.

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23

Desmarais, Bruce A., and Skyler J. Cranmer. Statistical Inference in Political Networks Research. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.8.

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Анотація:

Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. This chapter reviews the most prominent methods of inferential network analysis for both cross-sectionally and longitudinally observed networks, including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, the chapter summarizes its analytic form, identifies prominent published applications in political science, and discusses computational considerations. It concludes with a set of guidelines for selecting a method for a given application.

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24

Tukey, John Wilder, and Kaye E. Basford. Graphical Analysis of Multi-Response Data (Interdisciplinary Statistics). Chapman & Hall/CRC, 1998.

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25

Coolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.

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Анотація:

This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of random graph generation. This book is aimed at the graduate student or advanced undergraduate. It includes many worked examples, numerical simulations and exercises making it suitable for use in teaching. Explicit pseudocode algorithms are included to make the ideas easy to apply. Datasets are becoming increasingly large and network applications wider and more sophisticated. Testing hypotheses against properly specified control cases (null models) is at the heart of the ‘scientific method’. Knowledge on how to generate controlled and unbiased random graph ensembles is vital for anybody wishing to apply network science in their research.

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26

Tukey, John Wilder, and Kaye E. Basford. Graphical Analysis of Multi-Response Data. Taylor & Francis Group, 2020.

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27

Tukey, John Wilder, and Kaye E. Basford. Graphical Analysis of Multi-Response Data. Taylor & Francis Group, 2020.

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28

Tukey, John Wilder, and Kaye E. Basford. Graphical Analysis of Multi-Response Data. Taylor & Francis Group, 2020.

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29

Tukey, John Wilder, and Kaye E. Basford. Graphical Analysis of Multi-Response Data. Taylor & Francis Group, 2020.

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30

Logg, Anders, and Hans Petter Langtangen. Solving PDEs in Python: The FEniCS Tutorial I. Springer, 2017.

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31

Peacock, Janet L., and Sally M. Kerry. Introduction to presenting statistical analyses. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780198599661.003.0005.

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Анотація:

Chapter 5 discusses presenting statistical analyses, and covers numerical data, results section, describing the results, assessing non-response bias, presenting results for different media, drawing up subject profiles, graphs and tables, and categorical and continuous data.

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32

Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Introduction to presenting statistical analyses. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0005.

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Анотація:

Chapter 5 discusses presenting statistical analyses, and covers numerical data, results section, describing the results, assessing non-response bias, presenting results for different media, and drawing up subject profiles, graphs and tables, and categorical and continuous data. It shows how to present data that have been transformed for analysis. It contains helpful tips and information about reporting descriptive data clearly. The chapter includes analyses using Stata, SAS, SPSS, and R.

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33

Auerbach, Charles, and Wendy Zeitlin. SSD for R. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197582756.001.0001.

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Анотація:

Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines, including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. This book serves as a guide for those desiring to conduct single-subject data analysis. The aim of this text is to introduce readers to the various functions available in SSD for R, a new, free, and innovative software package written in R, the robust open-source statistical programming language written by the book’s authors. SSD for R has the most comprehensive functionality specifically designed for the analysis of single-subject research data currently available. SSD for R has numerous graphing and charting functions to conduct robust visual analysis. Besides the ability to create simple line graphs, features are available to add mean, median, and standard deviation lines across phases to help better visualize change over time. Graphs can be annotated with text. SSD for R contains a wide variety of functions to conduct statistical analyses traditionally conducted with single-subject data. These include numerous descriptive statistics and effect size functions and tests of statistical significance, such as t tests, chi-squares, and the conservative dual criteria. Finally, SSD for R has the capability of analyzing group-level data. Readers are led step by step through the analytical process based on the characteristics of their data. Numerous examples and illustrations are provided to help readers understand the wide range of functions available in SSD for R and their application to data analysis and interpretation.

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34

Practical R for biologists: an introduction. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0000.

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Анотація:

Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.

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35

Ferreira, Eliel Alves, and João Vicente Zamperion. Excel: Uma ferramenta estatística. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-400-5.

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Анотація:

This study aims to present the concepts and methods of statistical analysis using the Excel software, in a simple way aiming at a greater ease of understanding of students, both undergraduate and graduate, from different areas of knowledge. In Excel, mainly Data Analysis Tools will be used. For a better understanding, there are, in this book, many practical examples applying these tools and their interpretations, which are of paramount importance. In the first chapter, it deals with introductory concepts, such as introduction to Excel, the importance of statistics, concepts and definitions. Being that in this will be addressed the subjects of population and sample, types of data and their levels of measurement. Then it brings a detailed study of Descriptive Statistics, where it will be studied percentage, construction of graphs, frequency distribution, measures of central tendency and measures of dispersion. In the third chapter, notions of probability, binomial and normal probability distribution will be studied. In the last chapter, Inferential Statistics will be approached, starting with the confidence interval, going through the hypothesis tests (F, Z and t tests), ending with the statistical study of the correlation between variables and simple linear regression. It is worth mentioning that the statistical knowledge covered in this book can be useful for, in addition to students, professionals who want to improve their knowledge in statistics using Excel.

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36

Shekhar, Shashi, and Venkata M. V. Gunturi. Spatio-Temporal Graph Data Analytics. Springer, 2019.

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37

Shekhar, Shashi, and Venkata M. V. Gunturi. Spatio-Temporal Graph Data Analytics. Springer, 2018.

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38

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2015.

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39

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2015.

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40

Bolla, Marianna. Spectral Clustering and Biclustering of Networks: Large Graphs and Contingency Tables. Wiley & Sons, Limited, John, 2013.

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41

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2016.

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42

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2015.

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43

STATISTICS WITH JMP: GRAPHS, DESCRIPTIVE STATISTICS AND PROBABILITY. JOHN WILEY, 2015.

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44

Klerman, Daniel. Quantitative Legal History. Edited by Markus D. Dubber and Christopher Tomlins. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198794356.013.19.

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Анотація:

Quantitative legal history is in a rather sorry state. Only about a quarter of recent works of legal history use even simple quantitative methods (such as tables or graphs), and articles or books with more sophisticated methods, such as regression analysis, are extremely rare. The infrequent use of quantitative techniques is also a missed opportunity. Scholars from other fields, including economics, sociology, and political science, are using statistics to analyse legal history. Such analysis is particularly helpful in understanding the effect of legal change and in analysing the influence of multiple factors on legislation, judicial decision-making, and citizen behaviour. This chapter first assesses quantitatively the use of quantitative methods in legal history. It then discusses a few examples of the successful use of numbers and statistics in recent books addressing legal historical topics. Finally, it looks to the future of quantitative legal history.

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45

Makela, Susanna, Yajuan Si, and Andrew Gelman. Graphical Visualization of Polling Results. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.12.

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Анотація:

This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.

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46

Buu, Anne, and Runze Li. New Statistical Methods Inspired by Data Collected from Alcohol and Substance Abuse Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676001.003.0021.

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Анотація:

This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity and predictive validity using daily process data. The excellent statistical properties of the methods introduced have been supported by simulation studies. The applications in alcohol and substance abuse research have also been demonstrated by graphs on real longitudinal data.

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47

Newman, Mark. Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.001.0001.

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Анотація:

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

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48

Verbaarschot, Jac. Quantum chaos and quantum graphs. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.33.

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Анотація:

This article examines the origins of the universality of the spectral statistics of quantum chaotic systems in the context of periodic orbit theory. It also considers interesting analogies between periodic orbit theory and the sigma model, along with related work on quantum graphs. The article first reviews some facts and definitions for classically chaotic systems in order to elucidate their quantum behaviour, focusing on systems with two degrees of freedom: one characterized by ergodicity and another by hyperbolicity. It then describes two semiclassical approximation techniques — Gutzwiller’s periodic orbit theory and a refined approach incorporating the unitarity of the quantum evolution — and highlights their importance in understanding universal spectral statistics, and how they are related to the sigma model. This is followed by an analysis of parallel developments for quantum graphs, which are relevant to quantum chaos.

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49

Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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50

Mathis, Philippe. Graphs and Networks: Multilevel Modeling. Wiley & Sons, Incorporated, John, 2013.

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