Features 21 case studies that illustrate commonly used approaches to answer scientific questions in such areas as biology, toxicology, clinical medicine, environmental hazards, agriculture, forestry and wildlife. Examples of statistical methods used in these case studies include linear regression, survival analysis, principle components, design of experiments, resampling and bootstrap. A disk containing the collective data sets will accompany the book.
Features 21 case studies that illustrate commonly used approaches to answer scientific questions in such areas as biology, toxicology, clinical medicine, environmental hazards, agriculture, forestry and wildlife. Examples of statistical methods used in these case studies include linear regression, survival analysis, principle components, design of experiments, resampling and bootstrap. A disk containing the collective data sets will accompany the book.
This book brings together aspects of statistics and machine learning to provide a comprehensive guide to evaluating, interpreting and understanding biometric data. It naturally leads to topics including data mining and prediction to be examined in detail. The book places an emphasis on the various performance measures available for biometric systems, what they mean, and when they should and should not be applied. The evaluation techniques are presented rigorously, however they are always accompanied by intuitive explanations. This is important for the increased acceptance of biometrics among non-technical decision makers, and ultimately the general public.
Biometrics is a component of Encyclopedia of Mathematical Sciences in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. Biometry is a broad discipline covering all applications of statistics and mathematics to biology. The Theme Biometrics is divided into areas of expertise essential for a proper application of statistical and mathematical methods to contemporary biological problems. These volumes cover four main topics: Data Collection and Analysis, Statistical Methodology, Computation, Biostatistical Methods and Research Design and Selected Topics. These volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.
Point process statistics is successfully used in fields such as material science, human epidemiology, social sciences, animal epidemiology, biology, and seismology. Its further application depends greatly on good software and instructive case studies that show the way to successful work. This book satisfies this need by a presentation of the spatstat package and many statistical examples. Researchers, spatial statisticians and scientists from biology, geosciences, materials sciences and other fields will use this book as a helpful guide to the application of point process statistics. No other book presents so many well-founded point process case studies. From the reviews: "For those interested in analyzing their spatial data, the wide variatey of examples and approaches here give a good idea of the possibilities and suggest reasonable paths to explore." Michael Sherman for the Journal of the American Statistical Association, December 2006
Offers students with little background in statistical analysis an introduction to a variety of statistical concepts and methods. In addition to the incorporation of computer calculation, this new edition expands on a number of important topics, including the revised Kolmogrov-Smirnov test.
Biometrics provide quantitative representations of human features, physiological and behavioral. This book is a compilation of biometric technologies developed by various research groups in Tecnologico de Monterrey, Mexico. It provides a summary of biometric systems as a whole, explaining the principles behind physiological and behavioral biometrics and exploring different types of commercial and experimental technologies and current and future applications in the fields of security, military, criminology, healthcare education, business, and marketing. Examples of biometric systems using brain signals or electroencephalography (EEG) are given. Mobile and home EEG use in children’s natural environments is covered. At the same time, some examples focus on the relevance of such technology in monitoring epileptic encephalopathies in children. Using reliable physiological signal acquisition techniques, functional Human Machine Interfaces (HMI) and Brain-Computer Interfaces (BCI) become possible. This is the case of an HMI used for assistive navigation systems, controlled via voice commands, head, and eye movements. A detailed description of the BCI framework is presented, and applications of user-centered BCIs, oriented towards rehabilitation, human performance, and treatment monitoring are explored. Massive data acquisition also plays an essential role in the evolution of biometric systems. Machine learning, deep learning, and Artificial Intelligence (AI) are crucial allies here. They allow the construction of models that can aid in early diagnosis, seizure detection, and data-centered medical decisions. Such techniques will eventually lead to a more concise understanding of humans.
Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,
