Davis, Larry E., Engel, Rafael J., Gurin, Patricia., and Davis, Larry E.
Fremdbild, Selbstbild, Rassische Identität, Forschungsmethode, Völkerpsychologie, Race -- Research -- United States -- Statistical methods., Ethnicity -- Research -- United States -- Statistical methods., Race awareness -- Research -- United States -- Statistical methods., Racism -- Research -- United States -- Statistical methods., Demography -- United States -- Statistical methods., Ethnopsychology -- United States -- Statistical methods., Culturele antropologie., Onderzoek., Methodologie., and Statistische methoden.
Models, Statistical., Prognosis., Regression Analysis., Medical statistics., Medicine -- Research -- Statistical methods., Evidence-based medicine -- Statistical methods., Clinical trials -- Statistical methods., Regression analysis., Statistics., and Statistics for Life Sciences, Medicine, Health Sciences.
This book aims to provide insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or only in a simplistic way, and updating of already available models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. The text is primarily intended for epidemiologists and applied biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linea.
Population -- Statistical methods. and Population -- Mathematical models.
This book gives a unifying framework for estimating the abundance of open populations: populations subject to births, deaths and movement, given imperfect measurements or samples of the populations. The focus is primarily on populations of vertebrates for which dynamics are typically modelled within the framework of an annual cycle, and for which stochastic variability in the demographic processes is usually modest. Discrete-time models are developed in which animals can be assigned to discrete states such as age class, gender, maturity, population (within a metapopulation), or species (for multi-species models). The book goes well beyond estimation of abundance, allowing inference on underlying population processes such as birth or recruitment, survival and movement. This requires the formulation and fitting of population dynamics models. The resulting fitted models yield both estimates of abundance and estimates of parameters characterizing the underlying processes.
The aim of this book is to cover a wide scope of recent statistical methods used by scientists in biostatistics as well as in other related fields such as chemometrics, environmetrics and geophysics. Each method is accompanied with interactive and automatic Xplore routines, available on-line, allowing people to reproduce the proposed examples or to apply the methods to their own real datasets.
MEDICAL / Health Risk Assessment., MEDICAL / Epidemiology., Epidemiologic Methods., Clinical epidemiology -- Methodology., Medicine., Epidemiology., Bioinformatics., Statistical methods., Biology -- Data processing., Medicine & Public Health., Computer Appl. in Life Sciences., and Biostatistics.
Considers uncertainty and error analysis as an integral part of scaling. This book draws together a series of important case studies to provide a comprehensive review and synthesis of the concepts, theories and methods in scaling and uncertainty analysis. It is of interest to both researchers and practitioners working on landscape issues.
Data Interpretation, Statistical., Biostatistics -- methods., Metagenomics -- methods., Metagenomics -- Laboratory manuals., Laboratory Manuals., Laboratory manuals., Medicine., Human genetics., Nucleic acids., Statistical methods., Biomedicine., Nucleic Acid Chemistry., and Biostatistics.
Population genomics is a recently emerged discipline, which aims at understanding how evolutionary processes influence genetic variation across genomes. Today, in the era of cheaper next-generation sequencing, it is no longer as daunting to obtain whole genome data for any species of interest and population genomics is now conceivable in a wide range of fields, from medicine and pharmacology to ecology and evolutionary biology. However, because of the lack of reference genome and of enough a priori data on the polymorphism, population genomics analyses will still involve higher constraints for researchers working on non-model organisms, as regards the choice of the genotyping/sequencing technique or that of the analysis methods. Therefore, Data Production and Analysis in Population Genomics purposely puts emphasis on protocols and methods that are applicable to species where genomic resources are still scarce. It is divided into three convenient sections, each one tackling one of the main challenges facing scientists setting up a population genomics study. The first section helps devising a sampling and/or experimental design suitable to address the biological question of interest. The second section addresses how to implement the best genotyping or sequencing method to obtain the required data given the time and cost constraints as well as the other genetic resources already available. Finally, the last section is about making the most of the (generally huge) dataset produced by using appropriate analysis methods in order to reach a biologically relevant conclusion. Written in the successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, advice on methodology and implementation, and notes on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, Data Production and Analysis in Population Genomics serves a wide readership by providing guidelines to help choose and implement the best experimental or analytical strategy for a given purpose.
Evolution (Biology), Statistics as Topic -- methods., Programming Languages., Biological Evolution., Phylogeny., Cladistic analysis -- Statistical methods., Phylogeny -- Data processing., Evolution (Biology) -- Data processing., R (Computer program language), Bioinformatics., Life sciences., Statistics., Life Sciences., Evolutionary Biology., and Statistics for Life Sciences, Medicine, Health Sciences.
Demography -- Statistical methods., Demography -- Mathematical models., Population -- Statistical methods., Population -- Mathematical models., Population forecasting -- Statistical methods., Population forecasting -- Mathematical models., Demography., Epidemiology., Mathematical statistics., Population., and Statistics.
Sustainability of pension systems, intergeneration fiscal equity under population aging, and accounting for health care benefits for future retirees are examples of problems that cannot be solved without understanding the nature of population forecasts and their uncertainty. Similarly, the accuracy of population estimates directly affects both the distributions of formula-based government allocations to sub-national units and the apportionment of political representation. The book develops the statistical foundation for addressing such issues. Areas covered include classical mathematical demography, event history methods, multi-state methods, stochastic population forecasting, sampling and census coverage, and decision theory. The methods are illustrated with empirical applications from Europe and the U.S. For statisticians the book provides a unique introduction to demographic problems in a familiar language. For demographers, actuaries, epidemiologists, and professionals in related fields, the book presents a unified statistical outlook on both classical methods of demography and recent developments. To facilitate its classroom use, exercises are included. Over half of the book is readily accessible to undergraduates, but more maturity may be required to benefit fully from the complete text. Knowledge of differential and integral calculus, matrix algebra, basic probability theory, and regression analysis is assumed. Juha M. Alho is Professor of Statistics, University of Joensuu, Finland, and Bruce D. Spencer is Professor of Statistics and Faculty Fellow at the Institute for Policy Research, Northwestern University. Both have contributed extensively to statistical demography and served in advisory roles and as statistical consultants in the field.
In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders in the field and they will take the reader from basic introductory material to the state-of the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book.
Finance -- Statistical methods., Capital market -- Statistical methods., Statistical physics., Financial engineering., Physics, Mathematics, Economics -- Statistics, Economics, Game Theory, Economics, Social and Behav. Sciences, Statistical Physics, Dynamical Systems and Complexity, Statistics for Business/Economics/Mathematical Finance/Insurance, and Economic Theory
This third edition includes two new chapters containing a practitioner's treatment of two important current topics in banking: the basic notions and tools of risk management and capital requirements for financial institutions, including an overview of the new Basel II capital framework.
Provides a concise introduction to the basic methods of evaluating option contracts, analysing financial time series, selecting portfolios and managing risks making realistic assumptions of the market behviour. Focuses on both the fundamentals of mathematical finance and financial time series analysis and on applicaiton to given problems of financial markets -- Back cover.
ISBMDA (International Symposium) (2004 : Barcelona, Spain), Barreiro, José M., and ISBMDA (International Symposium) (2004 : Barcelona, Spain)
Genomics -- statistics & numerical data., Data Interpretation, Statistical., Computational Biology -- statistics & numerical data., Medicine -- Research -- Statistical methods -- Congresses., Genomics -- Statistical methods -- Congresses., Bioinformatics -- Congresses., Computer science., Medical records -- Data processing., Database management., Information storage and retrieval systems., Artificial intelligence., Bioinformatics., Congresses., Kongress., Conference proceedings., and Conference papers and proceedings.
Thisyear,the5thInternationalSymposiumonMedicalDataAnalysishasexperimented an apparently slight modi?cation. The word “biological” has been added to the title of the conferences. The motivation for this shift goes beyond the wish to attract a diff- ent kind of professional. It is linked to recent trends to produce a shift within various biomedical areas towards genomics-based research and practice. For instance, medical informaticsandbioinformaticsarebeinglinkedina synergicareadenominatedbiom- ical informatics.Similarly,patient careis beingimproved,leadingto conceptsandareas such as molecular medicine, genomic medicine or personalized healthcare. The resultsfromdifferentgenomeprojects,the advancesin systemsbiologyand the integrative approaches to physiology would not be possible without new approaches in data and information processing. Within this scenario, novel methodologies and tools will beneededtolinkclinicalandgenomicinformation,forinstance,forgeneticclinical trials, integrated data mining of genetic clinical records and clinical databases, or gene expression studies, among others. Genomic medicine presents a series of challenges that need to be addressed by researchers and practitioners. In this sense, this ISBMDA conference aimed to become a place where researchers involved in biomedical research could meet and discuss. For this conference, the classical contents of former ISMDA conferences were updated to incorporate various issues from the biological ?elds. Similarly to the incorporation of these new topics of the conference, data analysts will face, in this world of genomic medicine and related areas, signi?cant challenges in research, education and practice.