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Advances in reliability, failure and risk analysis / / edited by Harish Garg
| Advances in reliability, failure and risk analysis / / edited by Harish Garg |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] |
| Descrizione fisica | 1 online resource (XII, 409 p. 160 illus., 119 illus. in color.) |
| Disciplina | 620.00452 |
| Collana | Industrial and Applied Mathematics |
| Soggetto topico |
Reliability (Engineering) - Statistical methods
Risk assessment - Statistical methods Avaluació del risc Fiabilitat (Enginyeria) Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-9909-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Degradation and Failure Mechanisms of Complex Systems: Principles -- Simplified Approach to Analyse Fuzzy Reliability of a Repairable System -- Fault-Tolerant and Resilient Neural Control for Discrete-Time Nonlinear Systems -- Bayesian Reliability Analysis of the Topp–Leone Model under Different Loss Functions -- Availability Analysis of Non-Markovian Models with Rejuvenation and Check Pointing -- Reliability Metrics of Textile Confection Plant Using Copula Linguistic -- An Application of Soft Computing in Oil Condition Monitoring -- A Multi-Parameter Occupational Safety Risk Assessment Model for Chemicals in the University Laboratories by an MCDM-Sorting Method -- Failure Mode and Effect Analysis (FMEA) for Safety-Critical Systems in the Context of Industry 4.0 -- Optimization of Redundancy Allocation Problem Using QPSO Algorithm under Uncertain Environment -- Resilience: Enterprise Sustainability Based to Risk Management -- Reliability Analysis of Process Systems Using Intuitionistic Fuzzy Set Theory -- Smart Systems Risk Management in IoT-Based Supply Chain -- Risk and Reliability Analysis in the Era of Digital Transformation -- Distributed System Reliability Analysis with Two Coverage Factors: A Copula Approach -- Qualitative Analysis Method for Evaluation of Risk and Failures in Wind Power Plants: A Case Study of Turkey -- Some Discrete Parametric Markov-Chain System Models to Analyze Reliability -- Repair and Maintenance Management System of Food Processing Equipment: A Systematic Literature Review -- Reliability, Availability, Maintainability and Dependability of a Serial Rice Mill Plant with the Incorporation of Coverage Factor. |
| Record Nr. | UNINA-9910686470203321 |
| Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Law, probability & risk
| Law, probability & risk |
| Pubbl/distr/stampa | [Oxford], : Oxford University Press |
| Soggetto topico |
Proximate cause (Law)
Risk Law - Mathematical models Law - Methodology Probabilities Risk assessment Dret - Models matemàtics Dret - Metodologia Avaluació del risc Analyse statistique Droit Évaluation des risques Logique juridique Méthodologie Modèle mathématique Raisonnement approximatif Théorie des probabilités |
| Soggetto genere / forma |
Periodicals.
Internet resources. Périodique électronique (Descripteur de forme) Ressource Internet (Descripteur de forme) |
| ISSN | 1470-840X |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Law, probability and risk
Law, prob. & risk |
| Record Nr. | UNINA-9910146954103321 |
| [Oxford], : Oxford University Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Law, probability & risk
| Law, probability & risk |
| Pubbl/distr/stampa | [Oxford], : Oxford University Press |
| Soggetto topico |
Proximate cause (Law)
Risk Law - Mathematical models Law - Methodology Probabilities Risk assessment Dret - Models matemàtics Dret - Metodologia Avaluació del risc Analyse statistique Droit Évaluation des risques Logique juridique Méthodologie Modèle mathématique Raisonnement approximatif Théorie des probabilités |
| Soggetto genere / forma |
Periodicals.
Internet resources. Périodique électronique (Descripteur de forme) Ressource Internet (Descripteur de forme) |
| ISSN | 1470-840X |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Law, probability and risk
Law, prob. & risk |
| Record Nr. | UNISA-996221337803316 |
| [Oxford], : Oxford University Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos
| Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (166 pages) |
| Disciplina | 519.2 |
| Soggetto topico |
Statistics
Experimental design Financial risk management Statistical Theory and Methods Design of Experiments Risk Management Avaluació del risc Estadística matemàtica Disseny d'experiments |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-06685-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996479369303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos
| Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (166 pages) |
| Disciplina | 519.2 |
| Soggetto topico |
Statistics
Experimental design Financial risk management Statistical Theory and Methods Design of Experiments Risk Management Avaluació del risc Estadística matemàtica Disseny d'experiments |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-06685-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910574043603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer
| Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer |
| Autore | van Oijen Marcel |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (118 pages) |
| Disciplina | 810 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Probabilities
Estadística bayesiana Probabilitats Avaluació del risc |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-16333-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Why This Book? -- Who Is this Book for? -- Notation -- Outline of Chapters -- Acknowledgements -- Contents -- 1 Introduction to Probabilistic Risk Analysis (PRA) -- 1.1 From Risk Matrices to PRA -- 1.2 Basic Equations for PRA -- 1.3 Decomposition of Risk: 2 or 3 Components -- 1.4 Resolution of PRA: Single-Threshold, Multi-Threshold, Categorical, Continuous -- 1.4.1 Single-Threshold PRA -- 1.4.2 Multi-Threshold PRA -- 1.4.3 Categorical PRA -- 1.4.4 Continuous PRA -- 1.5 Implementation of PRA: Distribution-Based, Sampling-Based, Model-Based -- 2 Distribution-Based Single-Threshold PRA -- 2.1 Conditional Distributions for z -- 2.1.1 Conditions for V Being Constant -- 2.2 Example of Distribution-Based PRA: Gaussian p[x,z] -- 2.2.1 Hazard Probability and Conditional Distributions -- 2.2.2 Conditional Expectations and PRA -- 2.3 Approximation Formulas for the Conditional Bivariate Gaussian Expectations -- 3 Sampling-Based Single-Threshold PRA -- 3.1 Example of Sampling-Based PRA: Linear Relationship -- 3.1.1 Varying the Threshold -- 3.2 Example of Sampling-Based PRA: Nonlinear Relationship -- 4 Sampling-Based Single-Threshold PRA: Uncertainty Quantification (UQ) -- 4.1 Uncertainty in p[H] -- 4.2 Uncertainty in V -- 4.3 Uncertainty in R -- 4.4 Extension of R-Code for PRA: Adding the UQ -- 4.5 PRA with UQ on the Nonlinear Data Set -- 4.6 Verification of the UQ by Simulating Multiple Data Sets -- 4.6.1 UQ-Verification: Nonlinear Relationship -- 4.6.2 UQ-Verification: Linear Relationship -- 4.7 Approximation Formulas for the Conditional Bivariate Gaussian Variances -- 5 Density Estimation to Move from Sampling- to Distribution-Based PRA -- 6 Copulas for Distribution-Based PRA -- 6.1 Sampling from Copulas and Carrying out PRA -- 6.2 Copula Selection -- 6.3 Using Copulas in PRA -- 7 Bayesian Model-Based PRA.
7.1 Linear Example: Full Bayesian PRA with Uncertainty -- 7.1.1 Checking the MCMC -- 7.1.2 PRA -- 7.2 Nonlinear Example: Full Bayesian PRA with Uncertainty -- 7.3 Advantages of the Bayesian Modelling Approach -- 8 Sampling-Based Multi-Threshold PRA:Gaussian Linear Example -- 9 Distribution-Based Continuous PRA: Gaussian Linear Example -- 10 Categorical PRA with Other Splits than for Threshold-Levels: Spatio-Temporal Example -- 10.1 Spatio-Temporal Environmental Data: x(s,t) -- 10.2 Spatio-Temporal System Data: z(s,t) -- 10.3 Single-Category Single-Threshold PRA for the Spatio-Temporal Data -- 10.4 Two-Category Single-Threshold PRA for Spatio-Temporal Data -- 11 Three-Component PRA -- 11.1 Three-Component PRA for Spatio-Temporal Data -- 11.2 Country-Wide Application of Three-Component PRA -- 11.3 UQ for Three-Component PRA -- 12 Introduction to Bayesian Decision Theory (BDT) -- 12.1 Example of BDT in Action -- 13 Implementation of BDT Using Bayesian Networks -- 13.1 Three Ways to Specify a Multivariate Gaussian -- 13.1.1 Switching Between the Three Different Specifications of the Multivariate Gaussian -- 13.2 Sampling from a GBN and Bayesian Updating -- 13.2.1 Updating a GBN When Information About Nodes Becomes Available -- 13.3 A Linear BDT Example Implemented as a GBN -- 13.4 A Linear BDT Example Implemented Using \texttt{Nimble} -- 13.4.1 Varying IRRIG to Identify the Value for Which E[U] Is Maximized -- 13.5 A Nonlinear BDT Example Implemented Using \texttt{Nimble} -- 14 A Spatial Example: Forestry in Scotland -- 14.1 A Decision Problem: Forest Irrigation in Scotland -- 14.2 Computational Demand of BDT and Emulation -- 14.3 Data -- 14.4 A Simple Model for Forest Yield Class (YC) -- 14.5 Emulation -- 14.6 Application of the Emulator -- 15 Spatial BDT Using Model and Emulator -- 15.1 Multiple Action Levels -- 16 Linkages Between PRA and BDT. 16.1 Risk Management -- 16.2 The Relationship Between Utility Maximisation in BDT and Risk Assessment in PRA: R_c -- 16.3 Simplified Accounting for Both Benefits and Costs of the Action: R_b -- 16.4 Only Correcting for Costs: R_a -- 17 PRA vs. BDT in the Spatial Example -- 18 Three-Component PRA in the Spatial Example -- 19 Discussion -- 19.1 PRA and Its Application -- 19.2 Data and Computational Demand of PRA -- 19.3 BDT -- 19.4 Computational Demand of BDT -- 19.5 PRA as a Tool for Simplifying and Elucidating BDT -- 19.6 Parameter and Model Uncertainties -- 19.7 Modelling and Decision-Support for Forest Response to Hazards -- 19.8 Spatial Statistics -- References -- Index. |
| Record Nr. | UNISA-996499869203316 |
van Oijen Marcel
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer
| Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer |
| Autore | van Oijen Marcel |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (118 pages) |
| Disciplina | 810 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Probabilities
Estadística bayesiana Probabilitats Avaluació del risc |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-16333-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Why This Book? -- Who Is this Book for? -- Notation -- Outline of Chapters -- Acknowledgements -- Contents -- 1 Introduction to Probabilistic Risk Analysis (PRA) -- 1.1 From Risk Matrices to PRA -- 1.2 Basic Equations for PRA -- 1.3 Decomposition of Risk: 2 or 3 Components -- 1.4 Resolution of PRA: Single-Threshold, Multi-Threshold, Categorical, Continuous -- 1.4.1 Single-Threshold PRA -- 1.4.2 Multi-Threshold PRA -- 1.4.3 Categorical PRA -- 1.4.4 Continuous PRA -- 1.5 Implementation of PRA: Distribution-Based, Sampling-Based, Model-Based -- 2 Distribution-Based Single-Threshold PRA -- 2.1 Conditional Distributions for z -- 2.1.1 Conditions for V Being Constant -- 2.2 Example of Distribution-Based PRA: Gaussian p[x,z] -- 2.2.1 Hazard Probability and Conditional Distributions -- 2.2.2 Conditional Expectations and PRA -- 2.3 Approximation Formulas for the Conditional Bivariate Gaussian Expectations -- 3 Sampling-Based Single-Threshold PRA -- 3.1 Example of Sampling-Based PRA: Linear Relationship -- 3.1.1 Varying the Threshold -- 3.2 Example of Sampling-Based PRA: Nonlinear Relationship -- 4 Sampling-Based Single-Threshold PRA: Uncertainty Quantification (UQ) -- 4.1 Uncertainty in p[H] -- 4.2 Uncertainty in V -- 4.3 Uncertainty in R -- 4.4 Extension of R-Code for PRA: Adding the UQ -- 4.5 PRA with UQ on the Nonlinear Data Set -- 4.6 Verification of the UQ by Simulating Multiple Data Sets -- 4.6.1 UQ-Verification: Nonlinear Relationship -- 4.6.2 UQ-Verification: Linear Relationship -- 4.7 Approximation Formulas for the Conditional Bivariate Gaussian Variances -- 5 Density Estimation to Move from Sampling- to Distribution-Based PRA -- 6 Copulas for Distribution-Based PRA -- 6.1 Sampling from Copulas and Carrying out PRA -- 6.2 Copula Selection -- 6.3 Using Copulas in PRA -- 7 Bayesian Model-Based PRA.
7.1 Linear Example: Full Bayesian PRA with Uncertainty -- 7.1.1 Checking the MCMC -- 7.1.2 PRA -- 7.2 Nonlinear Example: Full Bayesian PRA with Uncertainty -- 7.3 Advantages of the Bayesian Modelling Approach -- 8 Sampling-Based Multi-Threshold PRA:Gaussian Linear Example -- 9 Distribution-Based Continuous PRA: Gaussian Linear Example -- 10 Categorical PRA with Other Splits than for Threshold-Levels: Spatio-Temporal Example -- 10.1 Spatio-Temporal Environmental Data: x(s,t) -- 10.2 Spatio-Temporal System Data: z(s,t) -- 10.3 Single-Category Single-Threshold PRA for the Spatio-Temporal Data -- 10.4 Two-Category Single-Threshold PRA for Spatio-Temporal Data -- 11 Three-Component PRA -- 11.1 Three-Component PRA for Spatio-Temporal Data -- 11.2 Country-Wide Application of Three-Component PRA -- 11.3 UQ for Three-Component PRA -- 12 Introduction to Bayesian Decision Theory (BDT) -- 12.1 Example of BDT in Action -- 13 Implementation of BDT Using Bayesian Networks -- 13.1 Three Ways to Specify a Multivariate Gaussian -- 13.1.1 Switching Between the Three Different Specifications of the Multivariate Gaussian -- 13.2 Sampling from a GBN and Bayesian Updating -- 13.2.1 Updating a GBN When Information About Nodes Becomes Available -- 13.3 A Linear BDT Example Implemented as a GBN -- 13.4 A Linear BDT Example Implemented Using \texttt{Nimble} -- 13.4.1 Varying IRRIG to Identify the Value for Which E[U] Is Maximized -- 13.5 A Nonlinear BDT Example Implemented Using \texttt{Nimble} -- 14 A Spatial Example: Forestry in Scotland -- 14.1 A Decision Problem: Forest Irrigation in Scotland -- 14.2 Computational Demand of BDT and Emulation -- 14.3 Data -- 14.4 A Simple Model for Forest Yield Class (YC) -- 14.5 Emulation -- 14.6 Application of the Emulator -- 15 Spatial BDT Using Model and Emulator -- 15.1 Multiple Action Levels -- 16 Linkages Between PRA and BDT. 16.1 Risk Management -- 16.2 The Relationship Between Utility Maximisation in BDT and Risk Assessment in PRA: R_c -- 16.3 Simplified Accounting for Both Benefits and Costs of the Action: R_b -- 16.4 Only Correcting for Costs: R_a -- 17 PRA vs. BDT in the Spatial Example -- 18 Three-Component PRA in the Spatial Example -- 19 Discussion -- 19.1 PRA and Its Application -- 19.2 Data and Computational Demand of PRA -- 19.3 BDT -- 19.4 Computational Demand of BDT -- 19.5 PRA as a Tool for Simplifying and Elucidating BDT -- 19.6 Parameter and Model Uncertainties -- 19.7 Modelling and Decision-Support for Forest Response to Hazards -- 19.8 Spatial Statistics -- References -- Index. |
| Record Nr. | UNINA-9910632480803321 |
|
van Oijen Marcel
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Survival analysis proportional and non-proportional hazards regression / / John O'Quigley
| Survival analysis proportional and non-proportional hazards regression / / John O'Quigley |
| Autore | O'Quigley John |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (476 pages) |
| Disciplina | 363.17 |
| Soggetto topico |
Hazardous substances - Risk assessment
Regression analysis Substàncies perilloses Avaluació del risc Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-33439-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Summary of main notation -- 1 Introduction -- 1.1 Chapter summary -- 1.2 Context and motivation -- 1.3 Some examples -- 1.4 Main objectives -- 1.5 Neglected and underdeveloped topics -- 1.6 Model-based prediction -- 1.7 Data sets -- 1.8 Use as a graduate text -- 1.9 Classwork and homework -- 2 Survival analysis methodology -- 2.1 Chapter summary -- 2.2 Context and motivation -- 2.3 Basic tools -- 2.4 Some potential models -- 2.5 Censoring -- 2.6 Competing risks -- 2.7 Classwork and homework -- 3 Survival without covariates -- 3.1 Chapter summary -- 3.2 Context and motivation -- 3.3 Parametric models for survival functions -- 3.4 Empirical estimate (no censoring) -- 3.5 Kaplan-Meier (empirical estimate with censoring) -- 3.6 Nelson-Aalen estimate of survival -- 3.7 Model verification using empirical estimate -- 3.8 Classwork and homework -- 3.9 Outline of proofs -- 4 Proportional hazards models -- 4.1 Chapter summary -- 4.2 Context and motivation -- 4.3 General or non-proportional hazards model -- 4.4 Proportional hazards model -- 4.5 Cox regression model -- 4.6 Modeling multivariate problems -- 4.7 Classwork and homework -- 5 Proportional hazards models in epidemiology -- 5.1 Chapter summary -- 5.2 Context and motivation -- 5.3 Odds ratio, relative risk, and 2times2 tables -- 5.4 Logistic regression and proportional hazards -- 5.5 Survival in specific groups -- 5.6 Genetic epidemiology -- 5.7 Classwork and homework -- 6 Non-proportional hazards models -- 6.1 Chapter summary -- 6.2 Context and motivation -- 6.3 Partially proportional hazards models -- 6.4 Partitioning of the time axis -- 6.5 Time-dependent covariates -- 6.6 Linear and alternative model formulations -- 6.7 Classwork and homework -- 7 Model-based estimating equations -- 7.1 Chapter summary -- 7.2 Context and motivation.
7.3 Likelihood solution for parametric models -- 7.4 Semi-parametric estimating equations -- 7.5 Estimating equations using moments -- 7.6 Incorrectly specified models -- 7.7 Estimating equations in small samples -- 7.8 Classwork and homework -- 7.9 Outline of proofs -- 8 Survival given covariate information -- 8.1 Chapter summary -- 8.2 Context and motivation -- 8.3 Probability that Ti is greater than Tj -- 8.4 Conditional survival given ZinH -- 8.5 Other relative risk forms -- 8.6 Informative censoring -- 8.7 Classwork and homework -- 8.8 Outline of proofs -- 9 Regression effect process -- 9.1 Chapter summary -- 9.2 Context and motivation -- 9.3 Elements of the regression effect process -- 9.4 Univariate regression effect process -- 9.5 Regression effect processes for several covariates -- 9.6 Iterated logarithm for effective sample size -- 9.7 Classwork and homework -- 9.8 Outline of proofs -- 10 Model construction guided by regression effect process -- 10.1 Chapter summary -- 10.2 Context and motivation -- 10.3 Classical graphical methods -- 10.4 Confidence bands for regression effect process -- 10.5 Structured tests for time dependency -- 10.6 Predictive ability of a regression model -- 10.7 The R2 estimate of Ω2 -- 10.8 Using R2 and fit to build models -- 10.9 Some simulated situations -- 10.10 Illustrations from clinical studies -- 10.11 Classwork and homework -- 10.12 Outline of proofs -- 11 Hypothesis tests based on regression effect process -- 11.1 Chapter summary -- 11.2 Context and motivation -- 11.3 Some commonly employed tests -- 11.4 Tests based on the regression effect process -- 11.5 Choosing the best test statistic -- 11.6 Relative efficiency of competing tests -- 11.7 Supremum tests over cutpoints -- 11.8 Some simulated comparisons -- 11.9 Illustrations -- 11.10 Some further thoughts -- 11.11 Classwork and homework. 11.12 Outline of proofs -- A Probability -- A.1 Essential tools for survival problems -- A.2 Integration and measure -- A.3 Random variables and probability measure -- A.4 Convergence for random variables -- A.5 Topology and distance measures -- A.6 Distributions and densities -- A.7 Multivariate and copula models -- A.8 Expectation -- A.9 Order statistics and their expectations -- A.10 Approximations -- B Stochastic processes -- B.1 Broad overview -- B.2 Brownian motion -- B.3 Counting processes and martingales -- B.4 Inference for martingales and stochastic integrals -- C Limit theorems -- C.1 Empirical processes and central limit theorems -- C.2 Limit theorems for sums of random variables -- C.3 Functional central limit theorem -- C.4 Brownian motion as limit process -- C.5 Empirical distribution function -- D Inferential tools -- D.1 Theory of estimating equations -- D.2 Efficiency in estimation and in tests -- D.3 Inference using resampling techniques -- D.4 Conditional, marginal, and partial likelihood -- E Simulating data under the non-proportional hazards model -- E.1 Method 1-Change-point models -- E.2 Method 2-Non-proportional hazards models -- Further exercises and proofs -- Bibliography -- Index. |
| Record Nr. | UNINA-9910484649503321 |
|
O'Quigley John
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Survival analysis proportional and non-proportional hazards regression / / John O'Quigley
| Survival analysis proportional and non-proportional hazards regression / / John O'Quigley |
| Autore | O'Quigley John |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (476 pages) |
| Disciplina | 363.17 |
| Soggetto topico |
Hazardous substances - Risk assessment
Regression analysis Substàncies perilloses Avaluació del risc Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-33439-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Summary of main notation -- 1 Introduction -- 1.1 Chapter summary -- 1.2 Context and motivation -- 1.3 Some examples -- 1.4 Main objectives -- 1.5 Neglected and underdeveloped topics -- 1.6 Model-based prediction -- 1.7 Data sets -- 1.8 Use as a graduate text -- 1.9 Classwork and homework -- 2 Survival analysis methodology -- 2.1 Chapter summary -- 2.2 Context and motivation -- 2.3 Basic tools -- 2.4 Some potential models -- 2.5 Censoring -- 2.6 Competing risks -- 2.7 Classwork and homework -- 3 Survival without covariates -- 3.1 Chapter summary -- 3.2 Context and motivation -- 3.3 Parametric models for survival functions -- 3.4 Empirical estimate (no censoring) -- 3.5 Kaplan-Meier (empirical estimate with censoring) -- 3.6 Nelson-Aalen estimate of survival -- 3.7 Model verification using empirical estimate -- 3.8 Classwork and homework -- 3.9 Outline of proofs -- 4 Proportional hazards models -- 4.1 Chapter summary -- 4.2 Context and motivation -- 4.3 General or non-proportional hazards model -- 4.4 Proportional hazards model -- 4.5 Cox regression model -- 4.6 Modeling multivariate problems -- 4.7 Classwork and homework -- 5 Proportional hazards models in epidemiology -- 5.1 Chapter summary -- 5.2 Context and motivation -- 5.3 Odds ratio, relative risk, and 2times2 tables -- 5.4 Logistic regression and proportional hazards -- 5.5 Survival in specific groups -- 5.6 Genetic epidemiology -- 5.7 Classwork and homework -- 6 Non-proportional hazards models -- 6.1 Chapter summary -- 6.2 Context and motivation -- 6.3 Partially proportional hazards models -- 6.4 Partitioning of the time axis -- 6.5 Time-dependent covariates -- 6.6 Linear and alternative model formulations -- 6.7 Classwork and homework -- 7 Model-based estimating equations -- 7.1 Chapter summary -- 7.2 Context and motivation.
7.3 Likelihood solution for parametric models -- 7.4 Semi-parametric estimating equations -- 7.5 Estimating equations using moments -- 7.6 Incorrectly specified models -- 7.7 Estimating equations in small samples -- 7.8 Classwork and homework -- 7.9 Outline of proofs -- 8 Survival given covariate information -- 8.1 Chapter summary -- 8.2 Context and motivation -- 8.3 Probability that Ti is greater than Tj -- 8.4 Conditional survival given ZinH -- 8.5 Other relative risk forms -- 8.6 Informative censoring -- 8.7 Classwork and homework -- 8.8 Outline of proofs -- 9 Regression effect process -- 9.1 Chapter summary -- 9.2 Context and motivation -- 9.3 Elements of the regression effect process -- 9.4 Univariate regression effect process -- 9.5 Regression effect processes for several covariates -- 9.6 Iterated logarithm for effective sample size -- 9.7 Classwork and homework -- 9.8 Outline of proofs -- 10 Model construction guided by regression effect process -- 10.1 Chapter summary -- 10.2 Context and motivation -- 10.3 Classical graphical methods -- 10.4 Confidence bands for regression effect process -- 10.5 Structured tests for time dependency -- 10.6 Predictive ability of a regression model -- 10.7 The R2 estimate of Ω2 -- 10.8 Using R2 and fit to build models -- 10.9 Some simulated situations -- 10.10 Illustrations from clinical studies -- 10.11 Classwork and homework -- 10.12 Outline of proofs -- 11 Hypothesis tests based on regression effect process -- 11.1 Chapter summary -- 11.2 Context and motivation -- 11.3 Some commonly employed tests -- 11.4 Tests based on the regression effect process -- 11.5 Choosing the best test statistic -- 11.6 Relative efficiency of competing tests -- 11.7 Supremum tests over cutpoints -- 11.8 Some simulated comparisons -- 11.9 Illustrations -- 11.10 Some further thoughts -- 11.11 Classwork and homework. 11.12 Outline of proofs -- A Probability -- A.1 Essential tools for survival problems -- A.2 Integration and measure -- A.3 Random variables and probability measure -- A.4 Convergence for random variables -- A.5 Topology and distance measures -- A.6 Distributions and densities -- A.7 Multivariate and copula models -- A.8 Expectation -- A.9 Order statistics and their expectations -- A.10 Approximations -- B Stochastic processes -- B.1 Broad overview -- B.2 Brownian motion -- B.3 Counting processes and martingales -- B.4 Inference for martingales and stochastic integrals -- C Limit theorems -- C.1 Empirical processes and central limit theorems -- C.2 Limit theorems for sums of random variables -- C.3 Functional central limit theorem -- C.4 Brownian motion as limit process -- C.5 Empirical distribution function -- D Inferential tools -- D.1 Theory of estimating equations -- D.2 Efficiency in estimation and in tests -- D.3 Inference using resampling techniques -- D.4 Conditional, marginal, and partial likelihood -- E Simulating data under the non-proportional hazards model -- E.1 Method 1-Change-point models -- E.2 Method 2-Non-proportional hazards models -- Further exercises and proofs -- Bibliography -- Index. |
| Record Nr. | UNISA-996466390803316 |
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O'Quigley John
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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