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| Author | : Fengqing (Zoe) Zhang |
| Publisher | : |
| Total Pages | : 142 |
| Release | : 2010 |
| Genre | : Biochemical markers |
| ISBN | : |
| Author | : Ram N. Mohapatra |
| Publisher | : Springer |
| Total Pages | : 500 |
| Release | : 2015-06-25 |
| Genre | : Mathematics |
| ISBN | : 8132224523 |
This book discusses recent developments and contemporary research in mathematics, statistics and their applications in computing. All contributing authors are eminent academicians, scientists, researchers and scholars in their respective fields, hailing from around the world. This is the second conference on mathematics and computing organized at Haldia Institute of Technology, India. The conference has emerged as a powerful forum, offering researchers a venue to discuss, interact and collaborate, and stimulating the advancement of mathematics and its applications in computer science. The book will allow aspiring researchers to update their knowledge of cryptography, algebra, frame theory, optimizations, stochastic processes, compressive sensing, functional analysis, complex variables, etc. Educating future consumers, users, producers, developers and researchers in mathematics and computing is a challenging task and essential to the development of modern society. Hence, mathematics and its applications in computing are of vital importance to a broad range of communities, including mathematicians and computing professionals across different educational levels and disciplines. In current research, modeling and simulation, making decisions under uncertainty and pattern recognition have become very common. Professionals across different educational levels and disciplines need exposure to advances in mathematics and computing. In this context, this book presents research papers on applicable areas of current interest. It also includes papers in which experts summarize research findings, such as signal processing and analysis and low-rank-matrix approximation for solving large systems, which will emerge as powerful tools for further research. These new advances and cutting-edge research in the fields of mathematics and their applications to computing are of paramount importance for young researchers.
| Author | : Darius M. Dziuda |
| Publisher | : Cambridge University Press |
| Total Pages | : 295 |
| Release | : 2024-04-30 |
| Genre | : Science |
| ISBN | : 100900770X |
Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient. This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine. It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them. This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas.
| Author | : Sadanori Konishi |
| Publisher | : CRC Press |
| Total Pages | : 340 |
| Release | : 2014-06-06 |
| Genre | : Mathematics |
| ISBN | : 1466567287 |
Select the Optimal Model for Interpreting Multivariate Data Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering. The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.
| Author | : Stephen W. Looney |
| Publisher | : John Wiley & Sons |
| Total Pages | : 426 |
| Release | : 2015-01-23 |
| Genre | : Social Science |
| ISBN | : 1118552466 |
A “how to” guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation. The authors provide descriptions of select elementary statistical methods that are traditionally used to analyze biomarker data with a focus on the proper application of each method, including necessary assumptions, software recommendations, and proper interpretation of computer output. In addition, the book discusses frequently encountered challenges in analyzing biomarker data and how to deal with them, methods for the quality assessment of biomarkers, and biomarker study designs. Covering a broad range of statistical methods that have been used to analyze biomarker data in published research studies, Analysis of Biomarker Data: A Practical Guide also features: A greater emphasis on the application of methods as opposed to the underlying statistical and mathematical theory The use of SAS®, R, and other software throughout to illustrate the presented calculations for each example Numerous exercises based on real-world data as well as solutions to the problems to aid in reader comprehension The principles of good research study design and the methods for assessing the quality of a newly proposed biomarker A companion website that includes a software appendix with multiple types of software and complete data sets from the book’s examples Analysis of Biomarker Data: A Practical Guide is an ideal upper-undergraduate and graduate-level textbook for courses in the biological or environmental sciences. An excellent reference for statisticians who routinely analyze and interpret biomarker data, the book is also useful for researchers who wish to perform their own analyses of biomarker data, such as toxicologists, pharmacologists, epidemiologists, environmental and clinical laboratory scientists, and other professionals in the health and environmental sciences.
| Author | : Alvin C. Rencher |
| Publisher | : John Wiley & Sons |
| Total Pages | : 805 |
| Release | : 2012-07-10 |
| Genre | : Mathematics |
| ISBN | : 0470178965 |
Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere." —IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space. In addition, the authors explore a wealth of newly added topics, including: Confirmatory Factor Analysis Classification Trees Dynamic Graphics Transformations to Normality Prediction for Multivariate Multiple Regression Kronecker Products and Vec Notation New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Detailed appendices provide partial solutions as well as supplemental tables, and an accompanying FTP site features the book's data sets and related SAS® code. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.
| Author | : Abdelmonem Afifi |
| Publisher | : CRC Press |
| Total Pages | : 530 |
| Release | : 2011-07-05 |
| Genre | : Mathematics |
| ISBN | : 1466503246 |
This new version of the bestselling Computer-Aided Multivariate Analysis has been appropriately renamed to better characterize the nature of the book. Taking into account novel multivariate analyses as well as new options for many standard methods, Practical Multivariate Analysis, Fifth Edition shows readers how to perform multivariate statistical
| Author | : Kai Yang |
| Publisher | : McGraw Hill Professional |
| Total Pages | : 318 |
| Release | : 2004-03-17 |
| Genre | : Technology & Engineering |
| ISBN | : 0071501371 |
Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. * Graphical multivariate data display * Multivariate regression and path analysis * Multivariate process control charts * Six sigma and multivariate statistical methods
| Author | : |
| Publisher | : |
| Total Pages | : 8 |
| Release | : 2005 |
| Genre | : |
| ISBN | : |
Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality, and substantial noise. These characteristics generate challenges in discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. We present data processing methods and computational intelligence that combines support vector machines (SVM) with particle swarm optimization (PSO) for biomarker selection from MALDI-TOF spectra of enriched serum. SVM classifiers were built for various combinations of m/z windows guided by the PSO algorithm. The method identified mass points that achieved high classification accuracy in distinguishing cancer patients from non-cancer controls. Based on their frequency of occurrence in multiple runs, six m/z windows were selected as candidate biomarkers. These biomarkers yielded 100% sensitivity and 91% specificity in distinguishing liver cancer patients from healthy individuals in an independent dataset.
| Author | : G. Peck |
| Publisher | : |
| Total Pages | : 400 |
| Release | : 2017-11-07 |
| Genre | : |
| ISBN | : 9781979505949 |
This book develops Advanced Multivariate Methods for Prediction and Clasification: Nearest Neighbors, KNN Classifier, Ensemble Learning, Classification Ensemble, Regression Ensemble, Boosting, Bagging, Bagging of Regression Trees, Bagging of Classification Trees, Quantile Regression, Random Forest, Support Vector Machines for Binary Classification, Clasification Leaner Techniques and Regression Learner Techniques. This techniques are very important for work in Data Science. In addition, the book also develops examples and applications relating to such methods.Classification Learner Automatically train a selection of models and help you choose the best model. Model types include decision trees, discriminant analysis, support vector machines, logisticregression, nearest neighbors, and ensemble classification.Regression Learner train regression models to predict data. Using thisapp, you can explore your data, select features, specify validation schemes, train models,and assess results. You can perform automated training to search for the best regressionmodel type, including linear regression models, regression trees, Gaussian processregression models, support vector machines, and ensembles of regression trees.
