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Link de la búsquedaMathematical and Statistical Models and Methods in Reliability / SpringerLink (Online service) ; Rykov, V.V ; Nagraj Balakrishnan ; Nikulin, M.S (2010)
Título : Mathematical and Statistical Models and Methods in Reliability : Applications to Medicine, Finance, and Quality Control Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Rykov, V.V ; Nagraj Balakrishnan ; Nikulin, M.S Editorial: Boston, MA : Birkhäuser Boston Fecha de publicación: 2010 Otro editor: Imprint: Birkhäuser Colección: Statistics for Industry and Technology, ISSN 2364-6241 Número de páginas: XXVI, 457 p. 74 illus Il.: online resource ISBN/ISSN/DL: 978-0-8176-4971-5 Idioma : Inglés (eng) Palabras clave: Engineering Applied mathematics Mathematical models Probabilities Statistics Quality control Reliability Industrial safety Control, Reliability, Safety and Risk Statistical Theory Methods Applications of Mathematics for Life Sciences, Medicine, Health Sciences Probability Stochastic Processes Modeling Clasificación: 51 Matemáticas Resumen: An outgrowth of the sixth conference on “Mathematical Methods in Reliability: Theory, Methods, and Applications,” this book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by recognized experts in the field of reliability, the contributions cover a wide range of models, methods, and applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. The volume is thematically organized into four major sections: * Reliability Models, Methods, and Optimization; * Statistical Methods in Reliability; * Applications; * Computer Tools for Reliability. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence Nota de contenido: Reliability Models, Methods, and Optimization -- Reliability of Semi-Markov Systems with Asymptotic Merging Phase Space -- Nonlinearly Perturbed Stochastic Processes and Systems -- On a Copula for Failure Times of System Elements -- On One Method of Reliability Coefficients Calculation for Objects in Non-Homogeneous Event Flows -- A New Approach to Maintenance Optimization by Modeling Intensity Control -- Longitudinal Latent Markov Processes Observable Through an Invariant Rasch Model -- Dynamics of Dependence Properties for Lifetimes Influenced by Unobservable Environmental Factors -- On Alternative of Choice for a Prophylaxis Problem -- Optimal Incomplete Maintenance for Systems with Discrete Time-to-Failure Distribution -- A Gini-Type Index for Aging/Rejuvenating Objects -- Redundancy Analysis for Multi-state System: Reliability and Financial Assessment -- On the Reliability Modeling of Hierarchical Systems -- Statistical Methods in Reliability -- Parametric Estimation of Redundant System Reliability From Censored Data -- Assessing Accuracy of Statistical Inferences by Resamplings -- Change Point Estimation in Regression Models with Fixed Design -- A Model for Field Failure Prediction Using Dynamic Environmental Data -- Efficient Regression Estimation Under General Censoring and Truncation -- On Generalized Tests of Fit for Multinomial Populations -- Modeling and Scaling of Categorical Data -- Nonparametric Estimation and Testing the Effect of Covariates in Accelerated Life Time Models Under Censoring -- Nonparametric Estimation of Time Trend for Repairable Systems Data -- Confidence Region for Distribution Function from Censored Data -- Empirical Estimate with Uniformly Minimal d-Risk for Bernoulli Trials Success Probability -- Estimation of Archival Lifetime Distribution for Writable Optical Disks from Accelerated Testings -- Applications -- Ages in Reliability and Bio Systems, Interpretations, Control, and Applications -- Shocks in Mixed Populations -- Bayesian Estimation of Degradation Model Defined by a Wiener Process -- Benefits of Threshold Regression: A Case-Study Comparison with Cox Proportional Hazards Regression -- Optimal Stopping and Reselling of European Options -- Bayesian Modeling of Health State Preferences -- Information Measures in Biostatistics and Reliability Engineering -- Reliability Computer Tools -- Software System for Simulation and Research of Probabilistic Regularities and Statistical Data Analysis in Reliability and Quality Control -- Inverse Gaussian Model and Its Applications in Reliability and Survival Analysis En línea: http://dx.doi.org/10.1007/978-0-8176-4971-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33562 Mathematical and Statistical Models and Methods in Reliability : Applications to Medicine, Finance, and Quality Control [documento electrónico] / SpringerLink (Online service) ; Rykov, V.V ; Nagraj Balakrishnan ; Nikulin, M.S . - Boston, MA : Birkhäuser Boston : Imprint: Birkhäuser, 2010 . - XXVI, 457 p. 74 illus : online resource. - (Statistics for Industry and Technology, ISSN 2364-6241) .
ISBN : 978-0-8176-4971-5
Idioma : Inglés (eng)
Palabras clave: Engineering Applied mathematics Mathematical models Probabilities Statistics Quality control Reliability Industrial safety Control, Reliability, Safety and Risk Statistical Theory Methods Applications of Mathematics for Life Sciences, Medicine, Health Sciences Probability Stochastic Processes Modeling Clasificación: 51 Matemáticas Resumen: An outgrowth of the sixth conference on “Mathematical Methods in Reliability: Theory, Methods, and Applications,” this book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by recognized experts in the field of reliability, the contributions cover a wide range of models, methods, and applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. The volume is thematically organized into four major sections: * Reliability Models, Methods, and Optimization; * Statistical Methods in Reliability; * Applications; * Computer Tools for Reliability. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence Nota de contenido: Reliability Models, Methods, and Optimization -- Reliability of Semi-Markov Systems with Asymptotic Merging Phase Space -- Nonlinearly Perturbed Stochastic Processes and Systems -- On a Copula for Failure Times of System Elements -- On One Method of Reliability Coefficients Calculation for Objects in Non-Homogeneous Event Flows -- A New Approach to Maintenance Optimization by Modeling Intensity Control -- Longitudinal Latent Markov Processes Observable Through an Invariant Rasch Model -- Dynamics of Dependence Properties for Lifetimes Influenced by Unobservable Environmental Factors -- On Alternative of Choice for a Prophylaxis Problem -- Optimal Incomplete Maintenance for Systems with Discrete Time-to-Failure Distribution -- A Gini-Type Index for Aging/Rejuvenating Objects -- Redundancy Analysis for Multi-state System: Reliability and Financial Assessment -- On the Reliability Modeling of Hierarchical Systems -- Statistical Methods in Reliability -- Parametric Estimation of Redundant System Reliability From Censored Data -- Assessing Accuracy of Statistical Inferences by Resamplings -- Change Point Estimation in Regression Models with Fixed Design -- A Model for Field Failure Prediction Using Dynamic Environmental Data -- Efficient Regression Estimation Under General Censoring and Truncation -- On Generalized Tests of Fit for Multinomial Populations -- Modeling and Scaling of Categorical Data -- Nonparametric Estimation and Testing the Effect of Covariates in Accelerated Life Time Models Under Censoring -- Nonparametric Estimation of Time Trend for Repairable Systems Data -- Confidence Region for Distribution Function from Censored Data -- Empirical Estimate with Uniformly Minimal d-Risk for Bernoulli Trials Success Probability -- Estimation of Archival Lifetime Distribution for Writable Optical Disks from Accelerated Testings -- Applications -- Ages in Reliability and Bio Systems, Interpretations, Control, and Applications -- Shocks in Mixed Populations -- Bayesian Estimation of Degradation Model Defined by a Wiener Process -- Benefits of Threshold Regression: A Case-Study Comparison with Cox Proportional Hazards Regression -- Optimal Stopping and Reselling of European Options -- Bayesian Modeling of Health State Preferences -- Information Measures in Biostatistics and Reliability Engineering -- Reliability Computer Tools -- Software System for Simulation and Research of Probabilistic Regularities and Statistical Data Analysis in Reliability and Quality Control -- Inverse Gaussian Model and Its Applications in Reliability and Survival Analysis En línea: http://dx.doi.org/10.1007/978-0-8176-4971-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33562 Ejemplares
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Título : Parametric Statistical Change Point Analysis : With Applications to Genetics, Medicine, and Finance Tipo de documento: documento electrónico Autores: Chen, Jie ; SpringerLink (Online service) ; Arjun K. Gupta Editorial: Boston : Birkhäuser Boston Fecha de publicación: 2012 Número de páginas: XIII, 273 p. 24 illus., 23 illus. in color Il.: online resource ISBN/ISSN/DL: 978-0-8176-4801-5 Idioma : Inglés (eng) Palabras clave: Statistics Applied mathematics Engineering Probabilities Statistical Theory and Methods for Life Sciences, Medicine, Health Sciences Engineering, Physics, Computer Science, Chemistry Earth Business/Economics/Mathematical Finance/Insurance Probability Stochastic Processes Applications of Mathematics Clasificación: 51 Matemáticas Resumen: Overall, the book gives a clear and systematic presentation of models and methods. It will be an excellent source for theoretical and applied statisticians who are interested in research on change-point analysis and its applications to many areas. —Mathematical Reviews (Review of the First Edition) Revised and expanded, Parametric Statistical Change Point Analysis, Second Edition is an in-depth study of the change point problem from a general point of view, and a deeper look at change point analysis of the most commonly used statistical models. For some time, change point problems have appeared throughout the sciences in such disciplines as economics, medicine, psychology, signal processing, and geology; more recently, they have also been found extensively in applications related to biomedical imaging data, array Comparative Genomic Hybridization (aCGH) data, and gene expression data. These areas of interest—new and old—have motivated substantial research on change point problems and led to a significant body of literature in the field. The present monograph stands as a valuable contribution to this literature. Key features and topics: * Clear and systematic exposition with a great deal of introductory material included; * Different models in each chapter, including gamma and exponential models, rarely examined thus far in the literature; * Extensive examples to emphasize key concepts and different methodologies used, namely the likelihood ratio criterion as well as the Bayesian and information criterion approaches; * An up-to-date comprehensive bibliography and two indices. New to the Second Edition: * New examples of change point analysis in modern molecular biology and other fields such as finance and air traffic control; * Two new sections of applications of the underlying change point models in analyzing the array Comparative Genomic Hybridization (aCGH) data for DNA copy number changes; * A new chapter on change points in the hazard function; * A new chapter on other practical change point models, such as the epidemic change point model and a smooth-and-abrupt change point model. This monograph will be a highly useful resource for an impressively broad range of researchers in statistics, as well as a useful supplement for graduate courses in the field Nota de contenido: Preface -- Preliminaries -- Introduction -- Univariate Normal Model -- Multivariate Normal Model -- Regression Model -- Gamma Model -- Exponential Model -- Change Point Model for the Hazard Function -- Discrete Models -- Other Models -- Bibliography -- Author Index -- Subject Index En línea: http://dx.doi.org/10.1007/978-0-8176-4801-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32668 Parametric Statistical Change Point Analysis : With Applications to Genetics, Medicine, and Finance [documento electrónico] / Chen, Jie ; SpringerLink (Online service) ; Arjun K. Gupta . - Boston : Birkhäuser Boston, 2012 . - XIII, 273 p. 24 illus., 23 illus. in color : online resource.
ISBN : 978-0-8176-4801-5
Idioma : Inglés (eng)
Palabras clave: Statistics Applied mathematics Engineering Probabilities Statistical Theory and Methods for Life Sciences, Medicine, Health Sciences Engineering, Physics, Computer Science, Chemistry Earth Business/Economics/Mathematical Finance/Insurance Probability Stochastic Processes Applications of Mathematics Clasificación: 51 Matemáticas Resumen: Overall, the book gives a clear and systematic presentation of models and methods. It will be an excellent source for theoretical and applied statisticians who are interested in research on change-point analysis and its applications to many areas. —Mathematical Reviews (Review of the First Edition) Revised and expanded, Parametric Statistical Change Point Analysis, Second Edition is an in-depth study of the change point problem from a general point of view, and a deeper look at change point analysis of the most commonly used statistical models. For some time, change point problems have appeared throughout the sciences in such disciplines as economics, medicine, psychology, signal processing, and geology; more recently, they have also been found extensively in applications related to biomedical imaging data, array Comparative Genomic Hybridization (aCGH) data, and gene expression data. These areas of interest—new and old—have motivated substantial research on change point problems and led to a significant body of literature in the field. The present monograph stands as a valuable contribution to this literature. Key features and topics: * Clear and systematic exposition with a great deal of introductory material included; * Different models in each chapter, including gamma and exponential models, rarely examined thus far in the literature; * Extensive examples to emphasize key concepts and different methodologies used, namely the likelihood ratio criterion as well as the Bayesian and information criterion approaches; * An up-to-date comprehensive bibliography and two indices. New to the Second Edition: * New examples of change point analysis in modern molecular biology and other fields such as finance and air traffic control; * Two new sections of applications of the underlying change point models in analyzing the array Comparative Genomic Hybridization (aCGH) data for DNA copy number changes; * A new chapter on change points in the hazard function; * A new chapter on other practical change point models, such as the epidemic change point model and a smooth-and-abrupt change point model. This monograph will be a highly useful resource for an impressively broad range of researchers in statistics, as well as a useful supplement for graduate courses in the field Nota de contenido: Preface -- Preliminaries -- Introduction -- Univariate Normal Model -- Multivariate Normal Model -- Regression Model -- Gamma Model -- Exponential Model -- Change Point Model for the Hazard Function -- Discrete Models -- Other Models -- Bibliography -- Author Index -- Subject Index En línea: http://dx.doi.org/10.1007/978-0-8176-4801-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32668 Ejemplares
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Título : Statistical Methods for Dynamic Treatment Regimes : Reinforcement Learning, Causal Inference, and Personalized Medicine Tipo de documento: documento electrónico Autores: Chakraborty, Bibhas ; SpringerLink (Online service) ; Moodie, Erica E.M Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XVI, 204 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-7428-9 Idioma : Inglés (eng) Palabras clave: Statistics Health informatics for Life Sciences, Medicine, Sciences Statistics, general Informatics Clasificación: 51 Matemáticas Resumen: Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies Nota de contenido: Introduction -- The Data: Observational Studies and Sequentially Randomized Trials -- Statistical Reinforcement Learning -- Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes -- Estimation of Optimal DTRs by Directly Modeling Regimes -- G-computation: Parametric Estimation of Optimal DTRs -- Estimation DTRs for Alternative Outcome Types -- Inference and Non-regularity -- Additional Considerations and Final Thoughts -- Glossary -- Index -- References En línea: http://dx.doi.org/10.1007/978-1-4614-7428-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32355 Statistical Methods for Dynamic Treatment Regimes : Reinforcement Learning, Causal Inference, and Personalized Medicine [documento electrónico] / Chakraborty, Bibhas ; SpringerLink (Online service) ; Moodie, Erica E.M . - New York, NY : Springer New York : Imprint: Springer, 2013 . - XVI, 204 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-1-4614-7428-9
Idioma : Inglés (eng)
Palabras clave: Statistics Health informatics for Life Sciences, Medicine, Sciences Statistics, general Informatics Clasificación: 51 Matemáticas Resumen: Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies Nota de contenido: Introduction -- The Data: Observational Studies and Sequentially Randomized Trials -- Statistical Reinforcement Learning -- Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes -- Estimation of Optimal DTRs by Directly Modeling Regimes -- G-computation: Parametric Estimation of Optimal DTRs -- Estimation DTRs for Alternative Outcome Types -- Inference and Non-regularity -- Additional Considerations and Final Thoughts -- Glossary -- Index -- References En línea: http://dx.doi.org/10.1007/978-1-4614-7428-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32355 Ejemplares
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Título : Statistical Reasoning in Medicine : The Intuitive P-Value Primer Tipo de documento: documento electrónico Autores: Moyé, Lemuel A ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Número de páginas: XX, 302 p. 46 illus Il.: online resource ISBN/ISSN/DL: 978-0-387-46212-7 Idioma : Inglés (eng) Palabras clave: Mathematics Epidemiology Probabilities Statistics Probability Theory and Stochastic Processes for Life Sciences, Medicine, Health Sciences Clasificación: 51 Matemáticas Resumen: Lowers the Learning Curve for Physicians and Researchers! The successful Statistical Reasoning in Medicine: The Intuitive P-value Primer, with its novel emphasis on patient and community protection, illustrated the correct use of statistics in health care research for healthcare workers. Through clear explanations and examples, this book provided the non-mathematician with a foundation for understanding the underlying statistical reasoning process in clinical research, the core principles of research design, and the correct use of statistical inference and p-values. The P-Value Primer 2nd Edition levels the learning curve of statistics for health care researchers by further de-emphasizing mathematical and computational devices, bringing the principles of statistical reasoning closer to the uninitiated. Adding to the updated discussions of research design, hypothesis testing, regression analysis, and Bayes procedures, are new discussions of absolute and relative risk, as well as a lucid description of the number needed to treat (NNT). The multiple analysis issue is clearly defined, and a new description of the correct use and interpretation of combined endpoints in health care research is offered in an easily digestible format. The P-value Primer 2nd Edition demolishes other obstacles that have impeded a clear understanding of the application of statistics in medicine. The intertwined roles of epidemiology and biostatistics are depicted. In addition to a description of the non-technical history of statistics, a new discussion describes the active cultural forces that have historically argued against the use of probability and statistics, placing the current applications and controversies involving p-values in context. New illustrations of the difficulties physicians and health care providers face in research are offered, and the differences between research skills and statistical skills are distinguished. New discussion describing the process of scientific reasoning, p-values, and the law is included. All of this nonstandard content, so essential for a well rounded perspective on the modern use of statistics in medicine, makes this volume unique among introductory statistics books. New figures, conversation, and illustrations fortify each chapter. In addition, three new appendices have been added on the normal distribution, sample size computations, and new requirements for the use of statistics in the courtroom. Dr. Lemuel A. Moyé, M.D., Ph.D., is Professor of Biostatistics and the University of Texas School of Public Health, and is a licensed physician for twenty-five years in Indiana and Texas. Dr. Moyé has served as a clinical trial consultant for many pharmaceutical companies, and, in addition, has worked for the Federal Food and Drug Administration, serving for six years on an advisory committee and providing guidance to the FDA on the approval of drugs and devices. He has written six textbooks in the application of statistics to public health research Nota de contenido: Prologue -- The Basis of Statistical Reasoning in Medicine -- Search Versus Research -- A Hypothesis-Testing Primer -- Mistaken Identity: P-values in Epidemiology -- Shrine Worship -- P-values, Power, and Efficacy -- Scientific Reasoning, P-values, and the Court -- One-Sided Versus Two-Sided Testing -- Multiple Testing and Combined Endpoints -- Subgroup Analyses -- P-values and Regression Analyses -- Bayesian Analysis: Posterior P-values En línea: http://dx.doi.org/10.1007/978-0-387-46212-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34847 Statistical Reasoning in Medicine : The Intuitive P-Value Primer [documento electrónico] / Moyé, Lemuel A ; SpringerLink (Online service) . - New York, NY : Springer New York, 2006 . - XX, 302 p. 46 illus : online resource.
ISBN : 978-0-387-46212-7
Idioma : Inglés (eng)
Palabras clave: Mathematics Epidemiology Probabilities Statistics Probability Theory and Stochastic Processes for Life Sciences, Medicine, Health Sciences Clasificación: 51 Matemáticas Resumen: Lowers the Learning Curve for Physicians and Researchers! The successful Statistical Reasoning in Medicine: The Intuitive P-value Primer, with its novel emphasis on patient and community protection, illustrated the correct use of statistics in health care research for healthcare workers. Through clear explanations and examples, this book provided the non-mathematician with a foundation for understanding the underlying statistical reasoning process in clinical research, the core principles of research design, and the correct use of statistical inference and p-values. The P-Value Primer 2nd Edition levels the learning curve of statistics for health care researchers by further de-emphasizing mathematical and computational devices, bringing the principles of statistical reasoning closer to the uninitiated. Adding to the updated discussions of research design, hypothesis testing, regression analysis, and Bayes procedures, are new discussions of absolute and relative risk, as well as a lucid description of the number needed to treat (NNT). The multiple analysis issue is clearly defined, and a new description of the correct use and interpretation of combined endpoints in health care research is offered in an easily digestible format. The P-value Primer 2nd Edition demolishes other obstacles that have impeded a clear understanding of the application of statistics in medicine. The intertwined roles of epidemiology and biostatistics are depicted. In addition to a description of the non-technical history of statistics, a new discussion describes the active cultural forces that have historically argued against the use of probability and statistics, placing the current applications and controversies involving p-values in context. New illustrations of the difficulties physicians and health care providers face in research are offered, and the differences between research skills and statistical skills are distinguished. New discussion describing the process of scientific reasoning, p-values, and the law is included. All of this nonstandard content, so essential for a well rounded perspective on the modern use of statistics in medicine, makes this volume unique among introductory statistics books. New figures, conversation, and illustrations fortify each chapter. In addition, three new appendices have been added on the normal distribution, sample size computations, and new requirements for the use of statistics in the courtroom. Dr. Lemuel A. Moyé, M.D., Ph.D., is Professor of Biostatistics and the University of Texas School of Public Health, and is a licensed physician for twenty-five years in Indiana and Texas. Dr. Moyé has served as a clinical trial consultant for many pharmaceutical companies, and, in addition, has worked for the Federal Food and Drug Administration, serving for six years on an advisory committee and providing guidance to the FDA on the approval of drugs and devices. He has written six textbooks in the application of statistics to public health research Nota de contenido: Prologue -- The Basis of Statistical Reasoning in Medicine -- Search Versus Research -- A Hypothesis-Testing Primer -- Mistaken Identity: P-values in Epidemiology -- Shrine Worship -- P-values, Power, and Efficacy -- Scientific Reasoning, P-values, and the Court -- One-Sided Versus Two-Sided Testing -- Multiple Testing and Combined Endpoints -- Subgroup Analyses -- P-values and Regression Analyses -- Bayesian Analysis: Posterior P-values En línea: http://dx.doi.org/10.1007/978-0-387-46212-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34847 Ejemplares
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Título : Medical Applications of Finite Mixture Models Tipo de documento: documento electrónico Autores: Schlattmann, Peter ; SpringerLink (Online service) Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2009 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: X, 246 p. 74 illus Il.: online resource ISBN/ISSN/DL: 978-3-540-68651-4 Idioma : Inglés (eng) Palabras clave: Statistics Public health Epidemiology Biostatistics for Life Sciences, Medicine, Health Sciences and Computing/Statistics Programs Clasificación: 51 Matemáticas Resumen: The book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models. The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology. After an informal introduction the book provides and summarizes the mathematical background necessary to understand the algorithms. The emphasis of the book is on a variety of medical applications such as gene expression data, meta-analysis and population pharmacokinetics. These applications are discussed in detail using real data from the medical literature. The book offers an R package which enables the reader to use the methods for his/her needs Nota de contenido: Overview over the Book -- - Heterogeneity in Medicine -- Modeling Count Data -- Theory and Algorithms -- Disease Mapping and Cluster Investigations -- Modeling Heterogeneity in Psychophysiology -- Investigating and Analyzing Heterogeneity in Meta-Analysis -- Analysis of Gene Expression Data En línea: http://dx.doi.org/10.1007/978-3-540-68651-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34011 Medical Applications of Finite Mixture Models [documento electrónico] / Schlattmann, Peter ; SpringerLink (Online service) . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2009 . - X, 246 p. 74 illus : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-3-540-68651-4
Idioma : Inglés (eng)
Palabras clave: Statistics Public health Epidemiology Biostatistics for Life Sciences, Medicine, Health Sciences and Computing/Statistics Programs Clasificación: 51 Matemáticas Resumen: The book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models. The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology. After an informal introduction the book provides and summarizes the mathematical background necessary to understand the algorithms. The emphasis of the book is on a variety of medical applications such as gene expression data, meta-analysis and population pharmacokinetics. These applications are discussed in detail using real data from the medical literature. The book offers an R package which enables the reader to use the methods for his/her needs Nota de contenido: Overview over the Book -- - Heterogeneity in Medicine -- Modeling Count Data -- Theory and Algorithms -- Disease Mapping and Cluster Investigations -- Modeling Heterogeneity in Psychophysiology -- Investigating and Analyzing Heterogeneity in Meta-Analysis -- Analysis of Gene Expression Data En línea: http://dx.doi.org/10.1007/978-3-540-68651-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34011 Ejemplares
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