Complex Data Modeling And Computationally Intensive Statistical Methods
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Complex Data Modeling and Computationally Intensive Statistical Methods by Pietro Mantovan,Piercesare Secchi Pdf
Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.
Advances in Complex Data Modeling and Computational Methods in Statistics by Anna Maria Paganoni,Piercesare Secchi Pdf
The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.
Complex Data Modeling and Computationally Intensive Statistical Methods by Pietro Mantovan,Piercesare Secchi Pdf
Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.
Complex Models and Computational Methods in Statistics by Matteo Grigoletto,Francesco Lisi,Sonia Petrone Pdf
The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
Statistical Modeling and Analysis for Complex Data Problems by Pierre Duchesne,Bruno Rémillard Pdf
STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.
Statistical Methods and Modeling of Seismogenesis by Nikolaos Limnios,Eleftheria Papadimitriou,George Tsaklidis Pdf
The study of earthquakes is a multidisciplinary field, an amalgam of geodynamics, mathematics, engineering and more. The overriding commonality between them all is the presence of natural randomness. Stochastic studies (probability, stochastic processes and statistics) can be of different types, for example, the black box approach (one state), the white box approach (multi-state), the simulation of different aspects, and so on. This book has the advantage of bringing together a group of international authors, known for their earthquake-specific approaches, to cover a wide array of these myriad aspects. A variety of topics are presented, including statistical nonparametric and parametric methods, a multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties and multifractal approaches, selfcorrecting models, the linked stress release model, Markovian arrival models, Poisson-based detection techniques, change point detection techniques on seismicity models, and, finally, semi-Markov models for earthquake forecasting.
Statistical Models for Data Analysis by Paolo Giudici,Salvatore Ingrassia,Maurizio Vichi Pdf
The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They offer solutions for relevant problems in statistical data analysis and contain the explicit derivation of the proposed models as well as their implementation. The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society.
Frederick W. Faltin,Ron S. Kenett,Fabrizio Ruggeri
Author : Frederick W. Faltin,Ron S. Kenett,Fabrizio Ruggeri Publisher : John Wiley & Sons Page : 533 pages File Size : 44,7 Mb Release : 2012-07-24 Category : Medical ISBN : 9781119942047
Statistical Methods in Healthcare by Frederick W. Faltin,Ron S. Kenett,Fabrizio Ruggeri Pdf
Statistical Methods in Healthcare In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success. Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed. With a focus on finding solutions to these challenges, this book: Provides a comprehensive, in-depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development. Offers a broad coverage of standards and established methods through leading edge techniques. Uses an integrated case study based approach, with focus on applications. Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance. Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes. Addresses the use of modern statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision. Practitioners in healthcare-related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.
Spatial and Spatio-temporal Bayesian Models with R - INLA by Marta Blangiardo,Michela Cameletti Pdf
Spatial and Spatio-Temporal Bayesian Models withR-INLA provides a much needed, practically oriented& innovative presentation of the combination of Bayesianmethodology and spatial statistics. The authors combine anintroduction to Bayesian theory and methodology with a focus on thespatial and spatio-temporal models used within the Bayesianframework and a series of practical examples which allow the readerto link the statistical theory presented to real data problems. Thenumerous examples from the fields of epidemiology, biostatisticsand social science all are coded in the R package R-INLA, which hasproven to be a valid alternative to the commonly used Markov ChainMonte Carlo simulations
New Diagnostic, Therapeutic and Organizational Strategies for Acute Coronary Syndromes Patients by Niccolò Grieco,Maurizio Marzegalli,Anna Maria Paganoni Pdf
This book collects several contributions, written both by statisticians and medical doctors, which focus on the identification of new diagnostic, therapeutic and organizational strategies in order to improve the occurrence of clinical outcomes for Acute Coronary Syndromes (ACS) patients. The work is structured in two different parts: the first one is focused on cooperative project mainly on statistical analysis of large clinical and administrative databases; the second one faces the development of innovative diagnostic techniques, with specific reference to genetic and proteomic, and the evolution of new imaging techniques for the early identification of patients at major risk of thrombotic, arrhythmic complications and at risk of poor revascularization.
Author : Ton J. Cleophas,Aeilko H. Zwinderman Publisher : Springer Science & Business Media Page : 739 pages File Size : 46,8 Mb Release : 2012-02-07 Category : Medical ISBN : 9789400728639
Statistics Applied to Clinical Studies by Ton J. Cleophas,Aeilko H. Zwinderman Pdf
Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds. This development, however fascinating, generally does not lead to simple results. We should not forget that clinical studies are, mostly, for confirming prior hypotheses based on sound arguments, and the simplest tests provide the best power and are adequate for such studies. In the past few years the authors of this 5th edition, as teachers and research supervisors in academic and top-clinical facilities, have been able to closely observe the latest developments in the field of clinical data analysis, and they have been able to assess their performance. In this 5th edition the 47 chapters of the previous edition have been maintained and upgraded according to the current state of the art, and 20 novel chapters have been added after strict selection of the most valuable and promising novel methods. The novel methods are explained using practical examples and step-by-step analyses readily accessible for non-mathematicians. All of the novel chapters have been internationally published by the authors in peer-reviewed journal, including the American Journal of Therapeutics, the European Journal of Clinical Investigation, The International journal of Clinical Pharmacology and therapeutics, and other journals, and permission is granted by all of them to use this material in the current book. We should add that the authors are well-qualified in their fields of knowledge. Professor Zwinderman is president-elect of the International Society of Biostatistics, and Professor Cleophas is past-president of the American College of Angiology. From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors, although from a different discipline, one clinician and one statistician, have been working and publishing together for over 10 years, and their research of statistical methodology can be characterized as a continued effort to demonstrate that statistics is not mathematics but rather a discipline at the interface of biology and mathematics. They firmly believe that any reader can benefit from this clinical approach to statistical data analysis.
Advances in Theoretical and Applied Statistics by Nicola Torelli,Fortunato Pesarin,Avner Bar-Hen Pdf
This volume includes contributions selected after a double blind review process and presented as a preliminary version at the 45th Meeting of the Italian Statistical Society. The papers provide significant and innovative original contributions and cover a broad range of topics including: statistical theory; methods for time series and spatial data; statistical modeling and data analysis; survey methodology and official statistics; analysis of social, demographic and health data; and economic statistics and econometrics.
Statistical Data Science by Adams Niall M,Cohen Ed Pdf
As an emerging discipline, data science broadly means different things across different areas. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis. Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers. remove
Sampling Designs Dependent on Sample Parameters of Auxiliary Variables by Janusz L. Wywiał Pdf
This short monograph provides a synthesis of new research on sampling designs that are dependent on sample moments or the order statistics of auxiliary variables. The range of survey sampling methods and their applications has gradually increased over time, and these applications have led to new theoretical solutions that provide better sampling designs or estimators. Recently, several important properties of sampling designs have been discovered, and many new methods have been published. Offering an overview of these developments, this book describes sampling designs dependent on the sample generalized variance of auxiliary variables, examines properties of sampling designs proportional to functions of sample order statistics of the auxiliary variable, and takes into account continuous sampling designs. The text will be useful for students and statisticians whose work involves survey sampling, and it will inspire those looking for new sampling designs dependent on auxiliary variables.