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Author: Thorsten Dickhaus Publisher: Springer Science & Business Media ISBN: 3642451829 Category : Science Languages : en Pages : 182
Book Description
This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students.
Author: Thorsten Dickhaus Publisher: Springer Science & Business Media ISBN: 3642451829 Category : Science Languages : en Pages : 182
Book Description
This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students.
Author: David Ruppert Publisher: Cambridge University Press ISBN: 9780521785167 Category : Mathematics Languages : en Pages : 410
Book Description
Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The 2003 book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here.
Author: Jianqing Fan Publisher: Springer Science & Business Media ISBN: 0387693955 Category : Mathematics Languages : en Pages : 565
Book Description
This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
Author: Jianqing Fan Publisher: CRC Press ISBN: 0429527616 Category : Mathematics Languages : en Pages : 942
Book Description
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Author: Trevor Hastie Publisher: CRC Press ISBN: 1498712177 Category : Business & Economics Languages : en Pages : 354
Book Description
Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl
Author: Werner Hildenbrand Publisher: Cambridge University Press ISBN: 9780521312677 Category : Business & Economics Languages : en Pages : 316
Book Description
This volume includes papers delivered at the Fourth World Congress of the Econometric Society. It will interest economic theorists and econometricians working in universities, government, and business and financial institutions.
Author: Adem Karahoca Publisher: BoD – Books on Demand ISBN: 9535107208 Category : Computers Languages : en Pages : 340
Book Description
Data Mining Applications in Engineering and Medicine targets to help data miners who wish to apply different data mining techniques. Data mining generally covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, etc. In this book, most of the areas are covered by describing different applications. This is why you will find here why and how Data Mining can also be applied to the improvement of project management. Since Data Mining has been widely used in a medical field, this book contains different chapters reffering to some aspects and importance of its use in the mentioned field: Incorporating Domain Knowledge into Medical Image Mining, Data Mining Techniques in Pharmacovigilance, Electronic Documentation of Clinical Pharmacy Interventions in Hospitals etc. We hope that this book will inspire readers to pursue education and research in this emerging field.
Author: Thorsten Dickhaus
Author: Thorsten Dickhaus
Author: 
Author: David Ruppert
Author: Simo Särkkä
Author: Jianqing Fan
Author: Jianqing Fan
Author: Trevor Hastie
Author: Werner Hildenbrand
Author: Adem Karahoca
Author: