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    Growth Curve Models and Applications [Electronic resource] : indian Statistical Institute, Giridih, India, March 28-29, 2016 / / ed. Dasgupta, Ratan. - 1st ed. 2017. - [S. l. : s. n.]. - XVII, 253 p. 196 illus., 154 illus. in color. - ISBN 9783319638867
Рубрики: Statistics .
   Biostatistics.
   Mathematical statistics.
   Statistical Theory and Methods.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
   Statistics and Computing/Statistics Programs.
   Probability and Statistics in Computer Science.
   Statistics for Business, Management, Economics, Finance, Insurance.
Анотація: Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally refereed to the high quality standards of leading journals in the field. Theoretical findings and case studies are reported from a wide range of fundamental and applied work across the broad range of natural sciences that comprise Growth Curve Modeling Methodology is particularly relevant to health care, prediction of crop yield, child nutrition, poverty measurements, and estimation of growth rate in any given scenario All papers feature original, peer-reviewed content Ratan Dasgupta, Ph.D., is Professor at the Indian Statistical Institute, Kolkata. Apart from his Ph.D topic on rates of convergence in CLT, his areas of research interest include applications of Statistics in Quality Control, fluid mechanics, environment, physics, and other areas of applied statistics. He has published roughly 70 research papers. .
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    Next Generation Sequencing Based Clinical Molecular Diagnosis of Human Genetic Disorders [Electronic resource] / ed. Wong, Lee-Jun C. - 1st ed. 2017. - [S. l. : s. n.]. - VIII, 364 p. 23 illus., 17 illus. in color. - ISBN 9783319564180
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Рубрики: Microbial genetics.
   Microbial genomics.
   Human genetics.
   Biostatistics.
   Microbial Genetics and Genomics.
   Human Genetics.
   Biostatistics.
Анотація: Next Generation Sequencing technology has been applied to clinical diagnoses in the past three to five years using various approaches, including target gene panels and whole exomes. The purpose of this book is to summarize the experiences, the results, advantages and disadvantages, along with future development in the area of NGS-based molecular diagnosis. This up-to-date volume will not only provide the readers working with Next Generation Sequencing the basics on how to apply the technology to molecular diagnosis, but will present the results and experience of practical application.
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    Blasco, Agustín.
    Bayesian Data Analysis for Animal Scientists [Electronic resource] : the Basics / / Agustín. Blasco ; . - 1st ed. 2017. - [S. l. : s. n.]. - XVIII, 275 p. 160 illus., 151 illus. in color. - ISBN 9783319542744
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Рубрики: Agriculture.
   Veterinary medicine.
   Biomathematics.
   Animal genetics.
   Biostatistics.
   Agriculture.
   Veterinary Medicine/Veterinary Science.
   Mathematical and Computational Biology.
   Animal Genetics and Genomics.
   Biostatistics.
Анотація: In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.
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    Rauch, Geraldine.
    Planning and Analyzing Clinical Trials with Composite Endpoints [Electronic resource] / Geraldine. Rauch, Schüler, Svenja., Kieser, Meinhard. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XVI, 255 p. 9 illus., 2 illus. in color. - ISBN 9783319737706
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Рубрики: Statistics .
   Biostatistics.
   Pharmaceutical technology.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
   Pharmaceutical Sciences/Technology.
Анотація: This book addresses the most important aspects of how to plan and evaluate clinical trials with a composite primary endpoint to guarantee a clinically meaningful and valid interpretation of the results. Composite endpoints are often used as primary efficacy variables for clinical trials, particularly in the fields of oncology and cardiology. These endpoints combine several variables of interest within a single composite measure, and as a result, all variables that are of major clinical relevance can be considered in the primary analysis without the need to adjust for multiplicity. Moreover, composite endpoints are intended to increase the size of the expected effects thus making clinical trials more powerful. The book offers practical advice for statisticians and medical experts involved in the planning and analysis of clinical trials. For readers who are mainly interested in the application of the methods, all the approaches are illustrated with real-world clinical trial examples, and the software codes required for fast and easy implementation are provided. The book also discusses all the methods in the context of relevant guidelines related to the topic. To benefit most from the book, readers should be familiar with the principles of clinical trials and basic statistical methods.
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Schüler, Svenja.; Kieser, Meinhard.; Rauch, Geraldine. \.\
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    Härdle, Wolfgang Karl.
    Basic Elements of Computational Statistics [Electronic resource] / Wolfgang Karl. Härdle, Okhrin, Ostap., Okhrin, Yarema. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XXI, 305 p. 97 illus., 66 illus. in color. - ISBN 9783319553368
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Рубрики: Statistics .
   Mathematical statistics.
   Biostatistics.
   Statistics and Computing/Statistics Programs.
   Statistical Theory and Methods.
   Probability and Statistics in Computer Science.
   Biostatistics.
   Statistics for Business, Management, Economics, Finance, Insurance.
   Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Анотація: This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R. It covers mathematical, statistical as well as programming problems in computational statistics and contains a wide variety of practical examples. In addition to the numerous R sniplets presented in the text, all computer programs (quantlets) and data sets to the book are available on GitHub and referred to in the book. This enables the reader to fully reproduce as well as modify and adjust all examples to their needs. The book is intended for advanced undergraduate and first-year graduate students as well as for data analysts new to the job who would like a tour of the various statistical tools in a data analysis workshop. The experienced reader with a good knowledge of statistics and programming might skip some sections on univariate models and enjoy the various mathematical roots of multivariate techniques. The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allows readers to reproduce the tables, pictures and calculations inside this Springer book.
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Okhrin, Ostap.; Okhrin, Yarema.; Härdle, Wolfgang Karl. \.\
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    Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry [Electronic resource] / ed.: Datta, Susmita., Mertens, Bart J. A. - 1st ed. 2017. - [S. l. : s. n.]. - VIII, 295 p. 106 illus., 83 illus. in color. - ISBN 9783319458090
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Рубрики: Statistics .
   Biostatistics.
   Metabolism.
   Bioinformatics.
   Analytical chemistry.
   Mathematical statistics.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
   Metabolomics.
   Computational Biology/Bioinformatics.
   Analytical Chemistry.
   Probability and Statistics in Computer Science.
Анотація: This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results. Susmita Datta received her PhD in statistics from the University of Georgia. She is a tenured professor in the Department of Biostatistics at the University of Florida. Before joining the University of Florida she was a professor and a distinguished university scholar at the University of Louisville. She is a Fellow of the American Association for the Advancement of Science, American Statistical Association, and an elected member of the International Statistical Institute. She is past president of the Caucus for Women in Statistics, and she actively supports research and education for women in STEM fields. Bart Mertens received his PhD in statistical sciences from University College London, Department of Statistical Sciences, on statistical analysis methods for spectrometry data. He is currently Associate Professor at the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Centre, where he has been working in both research and consulting for statistical analysis methodology with mass spectrometry proteomic data for more than 10 years.
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    Yi, Grace Y.
    Statistical Analysis with Measurement Error or Misclassification [Electronic resource] : strategy, Method and Application / / Grace Y. Yi ; . - 1st ed. 2017. - [S. l. : s. n.]. - XXVII, 479 p. 16 illus., 1 illus. in color. - ISBN 9781493966400
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Рубрики: Statistics .
   Biostatistics.
   Epidemiology.
   Statistical Theory and Methods.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
   Epidemiology.
Анотація: This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. .
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    New Advances in Statistics and Data Science [Electronic resource] / ed. Chen, Ding-Geng. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XXIII, 348 p. 74 illus., 41 illus. in color. - ISBN 9783319694160
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Рубрики: Statistics .
   Big data.
   Biostatistics.
   Statistical Theory and Methods.
   Big Data/Analytics.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
Анотація: This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency, Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.
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Chen, Ding-Geng. \ed.\; Jin, Zhezhen. \ed.\; Li, Gang. \ed.\; Li, Yi. \ed.\; Liu, Aiyi. \ed.\; Zhao, Yichuan. \ed.\
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    Isik, Fikret.
    Genetic Data Analysis for Plant and Animal Breeding [Electronic resource] / Fikret. Isik, Holland, James., Maltecca, Christian. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XVII, 400 p. 360 illus., 241 illus. in color. - ISBN 9783319551777
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Рубрики: Biostatistics.
   Plant breeding.
   Plant genetics.
   Agriculture.
   Animal genetics.
   Biostatistics.
   Plant Breeding/Biotechnology.
   Plant Genetics and Genomics.
   Agriculture.
   Animal Genetics and Genomics.
Анотація: This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
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Holland, James.; Maltecca, Christian.; Isik, Fikret. \.\
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10.

    Translational Bioinformatics and Its Application [Electronic resource] / ed. Wei, Dong-Qing. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - VIII, 437 p. 85 illus., 59 illus. in color. - ISBN 9789402410457
Рубрики: Bioinformatics.
   Human genetics.
   Biostatistics.
   Health informatics.
   Bioinformatics.
   Human Genetics.
   Biostatistics.
   Health Informatics.
Анотація: This book offers a detailed overview of translational bioinformatics together with real-case applications. Translational bioinformatics integrates the areas of basic bioinformatics, clinical informatics, statistical genetics and informatics in order to further our understanding of the molecular basis of diseases. By analyzing voluminous amounts of molecular and clinical data, it also provides clinical information, which can then be applied. Filling the gap between clinic research and informatics, the book is a valuable resource for human geneticists, clinicians, health educators and policy makers, as well as graduate students majoring in biology, biostatistics, and bioinformatics.
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Wei, Dong-Qing. \ed.\; Ma, Yilong. \ed.\; Cho, William C.S. \ed.\; Xu, Qin. \ed.\; Zhou, Fengfeng. \ed.\
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11.

    Big and Complex Data Analysis [Electronic resource] : methodologies and Applications / / ed. Ahmed, S. Ejaz. - 1st ed. 2017. - [S. l. : s. n.]. - XIV, 386 p. 85 illus., 55 illus. in color. - ISBN 9783319415734
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Рубрики: Statistics .
   Big data.
   Biostatistics.
   Data mining.
   Statistical Theory and Methods.
   Statistics and Computing/Statistics Programs.
   Big Data/Analytics.
   Biostatistics.
   Data Mining and Knowledge Discovery.
Анотація: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
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12.

    Chuang-Stein, Christy.
    Quantitative Decisions in Drug Development [Electronic resource] / Christy. Chuang-Stein, Kirby, Simon. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XV, 248 p. 27 illus., 11 illus. in color. - ISBN 9783319460765
    Зміст:

Рубрики: Statistics .
   Biostatistics.
   Pharmacy.
   Pharmaceutical technology.
   Quality control.
   Reliability.
   Industrial safety.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
   Drug Safety and Pharmacovigilance.
   Pharmaceutical Sciences/Technology.
   Quality Control, Reliability, Safety and Risk.
Анотація: This book offers a high-level treatise of evidence-based decisions in drug development. Because of the inseparable relationship between designs and decisions, a good portion of this book is devoted to the design of clinical trials. The book begins with an overview of product development and regulatory approval pathways. It then discusses how to incorporate prior knowledge into study design and decision making at different stages of drug development. The latter include selecting appropriate metrics to formulate decisions criteria, determining go/no-go decisions for progressing a drug candidate to the next stage and predicting the effectiveness of a product. Lastly, it points out common mistakes made by drug developers under the current drug-development paradigm. The book offers useful insights to statisticians, clinicians, regulatory affairs managers and decision-makers in the pharmaceutical industry who have a basic understanding of the drug-development process and the clinical trials conducted to support drug-marketing authorization. The authors provide software codes for select analytical approaches discussed in the book. The book includes enough technical details to allow statisticians to replicate the quantitative illustrations so that they can generate information to facilitate decision-making themselves.
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13.

    Extended Abstracts Fall 2015 [Electronic resource] : biomedical Big Data; Statistics for Low Dose Radiation Research / / ed. Ainsbury, Elizabeth A. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - VII, 131 p. 24 illus., 17 illus. in color. - ISBN 9783319556390
    Зміст:

Рубрики: Statistics .
   Biomathematics.
   Biostatistics.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Mathematical and Computational Biology.
   Biostatistics.
Анотація: This two-part volume gathers extended conference abstracts corresponding to selected talks from the "Biostatnet workshop on Biomedical (Big) Data" and from the "DoReMi LD-RadStats: Workshop for statisticians interested in contributing to EU low dose radiation research", which were held at the Centre de Recerca Matemàtica (CRM) in Barcelona from November 26th to 27th, 2015, and at the Institut de Salut Global ISGlobal (former CREAL) from October 26th to 28th, 2015, respectively. Most of the contributions are brief articles, presenting preliminary new results not yet published in regular research journals. The first part is devoted to the challenges of analyzing so called "Biomedical Big Data", tremendous amounts of biomedical and health data that are generated every day due to the use of recent technological advances such as massive genomic sequencing, electronic health records or high-resolution medical imaging, among others. The analysis of this information poses significant challenges for researchers in the fields of biostatistics, bioinformatics, and signal processing. Furthermore, other relevant challenges in biostatistical research, not necessarily involving big data, are also discussed. In turn, the second part is dedicated to low dose radiation research, where there is a need to fully understand and characterize potential sources of uncertainty before they can be reduced. Further, the book demonstrates why formal uncertainty analysis has the potential to provide a common platform for multidisciplinary research in this field. This book is intended for established researchers, as well as for PhD and postdoctoral students who want to learn more about the latest advances in these highly active areas of research.
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Ainsbury, Elizabeth A. \ed.\; Calle, M.Luz. \ed.\; Cardis, Elisabeth. \ed.\; Einbeck, Jochen. \ed.\; Gómez, Guadalupe. \ed.\; Puig, Pere. \ed.\
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14.

    Durstewitz, Daniel.
    Advanced Data Analysis in Neuroscience [Electronic resource] : integrating Statistical and Computational Models / / Daniel. Durstewitz ; . - 1st ed. 2017. - [S. l. : s. n.]. - XXV, 292 p. 76 illus., 66 illus. in color. - ISBN 9783319599762
    Зміст:

Рубрики: Statistics .
   Neurosciences.
   Biomathematics.
   Biostatistics.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Statistical Theory and Methods.
   Neurosciences.
   Mathematical and Computational Biology.
   Biostatistics.
Анотація: This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. "Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function." Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego “This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “ Bruno B. Averbeck .
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Durstewitz, Daniel. \.\
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15.

    García Martínez, Martínez, Constantino Antonio.
    Heart Rate Variability Analysis with the R package RHRV [Electronic resource] / Martínez, Constantino Antonio. García Martínez, Otero Quintana, Abraham. [et al.] ; . - 1st ed. 2017. - [S. l. : s. n.]. - XVI, 157 p. 50 illus., 29 illus. in color. - ISBN 9783319653556
    Зміст:

Рубрики: Statistics .
   Signal processing.
   Image processing.
   Speech processing systems.
   Biostatistics.
   Biomedical engineering.
   Cardiology.
   Cardiac imaging.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Signal, Image and Speech Processing.
   Biostatistics.
   Biomedical Engineering and Bioengineering.
   Cardiology.
   Cardiac Imaging.
Анотація: This book introduces readers to the basic concepts of Heart Rate Variability (HRV) and its most important analysis algorithms using a hands-on approach based on the open-source RHRV software. HRV refers to the variation over time of the intervals between consecutive heartbeats. Despite its apparent simplicity, HRV is one of the most important markers of the autonomic nervous system activity and it has been recognized as a useful predictor of several pathologies. The book discusses all the basic HRV topics, including the physiological contributions to HRV, clinical applications, HRV data acquisition, HRV data manipulation and HRV analysis using time-domain, frequency-domain, time-frequency, nonlinear and fractal techniques. Detailed examples based on real data sets are provided throughout the book to illustrate the algorithms and discuss the physiological implications of the results. Offering a comprehensive guide to analyzing beat information with RHRV, the book is intended for masters and Ph.D. students in various disciplines such as biomedical engineering, human and veterinary medicine, biology, and pharmacy, as well as researchers conducting heart rate variability analyses on both human and animal data.
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Otero Quintana, Abraham.; Vila, Xosé A.; Lado Touriño, María José.; Rodríguez-Liñares, Leandro.; Rodríguez Presedo, Jesús María.; Méndez Penín, Arturo José.; García Martínez, Constantino Antonio. \.\
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16.

    Monte-Carlo Simulation-Based Statistical Modeling [Electronic resource] / ed.: Chen, Ding-Geng (Din)., Chen, John Dean. - 1st ed. 2017. - [S. l. : s. n.]. - XX, 430 p. 64 illus., 33 illus. in color. - ISBN 9789811033070
    Зміст:

Рубрики: Statistics .
   Biostatistics.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
Анотація: This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
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Chen, Ding-Geng (Din). \ed.\; Chen, John Dean. \ed.\
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Статистика за 13.07.2024
Кількість запитів	313
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