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Format: X, 270 p. 94 illus., 78 illus. in color. , online resource.

Edition: 1st ed. 2024.

ISBN: 9783031657238

Series Statement: Contributions to Statistics,

Content: This volume on the latest developments in statistical modelling is a collection of refereed papers presented at the 38th International Workshop on Statistical Modelling, IWSM 2024, held from 14 to 19 July 2024 in Durham, UK. The contributions cover a wide range of topics in statistical modelling, including generalized linear models, mixture models, regularization techniques, hidden Markov models, smoothing methods, censoring and imputation techniques, Gaussian processes, spatial statistics, shape modelling, goodness-of-fit problems, and network analysis. Various highly topical applications are presented as well, especially from biostatistics. The approaches are equally frequentist and Bayesian, a categorization the statistical modelling community has synergetically overcome. The book also features the workshop's keynote contribution on statistical modelling for big and little data, highlighting that both small and large data sets come with their own challenges. The International Workshop on Statistical Modelling (IWSM) is the annual workshop of the Statistical Modelling Society, with the purpose of promoting important developments, extensions, and applications in statistical modelling, and bringing together statisticians working on related problems from various disciplines. This volume reflects this spirit and contributes to initiating and sustaining discussions about problems in statistical modelling and triggers new developments and ideas in the field.

Note: REML for two dimensional P splines -- Learning Bayesian networks from ordinal data The Bayesian way -- Latent Dirichlet allocation and hidden Markov models to identify public perception of sustainability in social media data -- Bayesian approaches to model overdispersion in Spatio temporal binomial data -- Elicitation of priors for intervention effects in educational trial data -- Elicitation of priors for intervention effects in educational trial data -- Optimism correction of the AUC with complex survey data -- Statistical models for patient centered outcomes in clinical studies -- Bayesian hidden Markov models for early warning -- A Bayesian Markov-switching for smooth modelling of extreme value distributions.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031657221

Additional Edition: Printed edition: ISBN 9783031657245

Additional Edition: Printed edition: ISBN 9783031657252

Language: English

DOI: 10.1007/978-3-031-65723-8

URL: https://doi.org/10.1007/978-3-031-65723-8

URL: Volltext  (URL des Erstveröffentlichers)

URL: lizenzpflichtig

UID:

Format: 1 online resource (281 pages)

Edition: 1st ed. 2024.

ISBN: 3-031-65723-3

Series Statement: Contributions to Statistics,

Content: This volume on the latest developments in statistical modelling is a collection of refereed papers presented at the 38th International Workshop on Statistical Modelling, IWSM 2024, held from 14 to 19 July 2024 in Durham, UK. The contributions cover a wide range of topics in statistical modelling, including generalized linear models, mixture models, regularization techniques, hidden Markov models, smoothing methods, censoring and imputation techniques, Gaussian processes, spatial statistics, shape modelling, goodness-of-fit problems, and network analysis. Various highly topical applications are presented as well, especially from biostatistics. The approaches are equally frequentist and Bayesian, a categorization the statistical modelling community has synergetically overcome. The book also features the workshop’s keynote contribution on statistical modelling for big and little data, highlighting that both small and large data sets come with their own challenges. The International Workshop on Statistical Modelling (IWSM) is the annual workshop of the Statistical Modelling Society, with the purpose of promoting important developments, extensions, and applications in statistical modelling, and bringing together statisticians working on related problems from various disciplines. This volume reflects this spirit and contributes to initiating and sustaining discussions about problems in statistical modelling and triggers new developments and ideas in the field.

Note: REML for two dimensional P splines -- Learning Bayesian networks from ordinal data The Bayesian way -- Latent Dirichlet allocation and hidden Markov models to identify public perception of sustainability in social media data -- Bayesian approaches to model overdispersion in Spatio temporal binomial data -- Elicitation of priors for intervention effects in educational trial data -- Elicitation of priors for intervention effects in educational trial data -- Optimism correction of the AUC with complex survey data -- Statistical models for patient centered outcomes in clinical studies -- Bayesian hidden Markov models for early warning -- A Bayesian Markov-switching for smooth modelling of extreme value distributions.

Additional Edition: ISBN 3-031-65722-5

Language: English

DOI: 10.1007/978-3-031-65723-8

UID:

Format: 1 online resource (281 pages)

Edition: 1st ed.

ISBN: 3-031-65723-3

Series Statement: Contributions to Statistics Series

Note: Intro -- Preface -- Contents -- REML for Two-Dimensional P-Splines -- 1 Introduction -- 2 Sparse Mixed Models for P-Splines -- 3 Back Transformation from Mixed Models to P-Splines -- 4 An Application and Comparison of Computation Times -- 5 Discussion -- References -- Learning Bayesian Networks from Ordinal Data - The Bayesian Way -- 1 Introduction -- 2 Mathematical Details -- 2.1 Bayesian Networks -- 2.2 Gaussian Bayesian Networks (BGe Score) -- 2.3 New Bayesian Network Model (`BoB Method') -- 2.4 MCMC Inference -- 2.5 Sampling the Latent (Unobserved) Gaussian Values -- 2.6 Simulation Details -- 3 Empirical Results -- 4 Conclusion -- References -- Latent Dirichlet Allocation and Hidden Markov Models to Identify Public Perception of Sustainability in Social Media Data -- 1 Introduction -- 2 Methods -- 3 Data and Analysis Framework -- 4 Preliminary Results and Discussions -- References -- Bayesian Approaches to Model Overdispersion in Spatio-Temporal Binomial Data -- 1 Introduction -- 2 Methods -- 3 Application -- 4 Conclusions -- References -- Elicitation of Priors for Intervention Effects in Educational Trial Data -- 1 Introduction -- 2 Methodology -- 3 Manual Elicitation of Priors from Evaluation Reports -- 4 Results -- References -- Addressing Covariate Lack in Unit-Level Small Area Models Using GAMLSS -- 1 The Core of the Problem -- 2 The Proposal -- 2.1 Non-parametric Bootstrap -- 3 Simulation Results -- 4 Application and Concluding Remarks -- References -- Optimism Correction of the AUC with Complex Survey Data -- 1 Introduction -- 2 Methods -- 3 Simulation Study -- 3.1 Results -- 4 Conclusions -- References -- Statistical Models for Patient-Centered Outcomes in Clinical Studies -- 1 Introduction -- 2 A Statistical Model for DAOH -- 3 Analysis -- 3.1 Results -- 3.2 Model Interpretation -- 4 Discussion -- References. , Bayesian Hidden Markov Models for Early Warning -- 1 Introduction -- 2 Model -- 3 Markov Chain Monte Carlo Algorithm -- 4 Real Data Application -- References -- A Bayesian Markov-Switching for Smooth Modelling of Extreme Value Distributions -- 1 Introduction -- 2 Markov-Switching Model Settings -- 3 Application to Energy Prices in Spain -- 4 Conclusions -- References -- Derivatives of the Log of a Determinant -- 1 Introduction -- 2 Theory -- 3 Applications -- References -- Monitoring Viral Infections in Severe Acute Respiratory Syndrome Patients in Brazil -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Discussion -- References -- A Computationally Efficient Spatio-Temporal Fusion Model for Reflectance Data -- 1 Introduction -- 2 Methodology -- 3 Application to Lake Garda Dataset -- 4 Simulation Study -- 5 Conclusion -- References -- Spatial Confounding in Gradient Boosting -- 1 Introduction -- 2 Gradient Boosting with Spatial+ -- 3 Simulation Study -- 4 Data Application -- 5 Conclusion -- References -- Adaptive Generalized Logistic Lasso and Its Application to Rankings in Sports -- 1 Introduction -- 2 Adaptive Generalized Logistic Lasso via Conic Programming -- 3 Simulation -- 4 Ranking in Sports -- 5 Summary -- References -- A Biclustering Approach via Mixture of Latent Trait Analyzers for the Analysis of Digital Divide in Italy -- 1 Introduction -- 2 Data -- 3 The MLTA Model for Biclustering -- 4 Results -- 5 Conclusions -- References -- Shrinkage in a Bayesian Panel Data Model with Time-Varying Coefficients -- 1 Introduction -- 2 Model Specification -- 2.1 Panel Data Model with Time-Varying Regression Effects -- 2.2 Latent Variable Model -- 2.3 Posterior Inference -- 3 Simulation Study -- 4 Case Study: Mother's Yearly Income -- 5 Conclusion -- References -- Integrating Single Index Effects in Generalized Additive Models -- 1 Introduction. , 2 Incorporating Single Index Effects into Generalised Additive Models -- 3 GefCom2014 Data Application -- 4 Conclusions -- References -- An Underrated Prior Distribution for Proportions. The Logistic-Normal for Dynamical Football Predictions -- 1 Introduction -- 2 Data -- 2.1 Methodology -- 2.2 Model Specification -- 2.3 Teams Merits -- 3 Results -- 4 Discussion -- References -- A Comparison of Extreme Gradient and Gaussian Process Boosting for a Spatial Logistic Regression on Satellite Data -- 1 Extreme Gradient Boosting -- 2 Gaussian Process Boosting -- 3 Model Specification and Experimental Design -- 4 Results -- 5 Discussion -- References -- Gene Coexpression Analysis with Dirichlet Mixture Model: Accelerating Model Evaluation Through Closed-Form KL Divergence Approximation Using Variational Techniques -- 1 Introduction -- 2 Methods -- 2.1 KL Divergence: Monte Carlo Approach -- 2.2 KL Divergence: Variational Approach -- 3 Results -- 4 Conclusion -- References -- Optimizing Variable Selection in Multi-Omics Datasets: A Focus on Exclusive Lasso -- 1 Introduction -- 2 Methods -- 3 Results -- 3.1 Simulation Studies -- 3.2 Down Syndrome Analysis -- 4 Conclusion -- References -- Non-parametric Frailty Model for the Natural History of Prostate Cancer -- Using Data from a Screening Trial -- 1 Introduction -- 2 Methods -- 2.1 Longitudinal Submodel -- 2.2 Multi-state Model -- 2.3 Likelihood Function -- 3 Application -- References -- Parametric and Non-parametric Bayesian Imputation for Right Censored Survival Data -- 1 Introduction -- 2 Methods -- 2.1 Parametric Bayesian Approach -- 2.2 Non-parametric Bayesian Approach -- 3 Simulation -- 4 Application -- 5 Conclusion -- References -- An Updated Wilcoxon-Mann-Whitney Test -- 1 Introduction -- 2 Mid-quantiles and Mid p-Values -- 3 Application to the Wilcoxon-Mann-Whitney Test -- 3.1 Attainment Rates. , 4 Power -- 5 Conclusion -- References -- Estimating a Lower Bound of the Population Size in Capture-Recapture Experiments with Right Censored Data -- 1 Introduction -- 2 The Model -- 3 Examples of Application -- References -- Inference for Quasi-reaction Models with Covariate-Dependent Rates -- 1 Introduction -- 2 Latent Event History Model -- 3 Simulation Study: SIR System Under Lockdown -- 4 Epidemic Modelling of COVID-19 -- 5 Conclusion -- References -- Modelling of Overdispersed Count Rates -- 1 Introduction -- 2 Motivating Example -- 3 Negative Binomial Rate Models -- 4 Extensions -- 5 Discussion -- References -- Sparse Intrinsic Gaussian Processes for Prediction on Manifolds: Extending Applications to Environmental Contexts -- 1 Introduction -- 2 Construction of Sparse Intrinsic GP -- 2.1 Numerical Approximation of the Heat Kernel -- 2.2 Sparse Method DIC Used for Intrinsic GP -- 3 Implementation of Sparse Intrinsic GP -- 3.1 The Sparse Intrinsic GP on the U-Shape Domain -- 3.2 Impacted Predictive Distribution -- 4 Discussion -- References -- Functional Copula Graphical Regression Model for Analysing Brain-Body Rhythm -- 1 Introduction -- 2 Metodology -- 3 Analysis -- 3.1 Measurement Protocol -- 3.2 Graph Estimation -- 3.3 Model Selection and Results -- 4 Conclusion -- References -- State-Space Models for Clustering of Compositional Trajectories -- 1 Introduction -- 2 State Space Representations for Compositions -- 3 ECM Algorithm for Model-Based Clustering -- 4 Application -- 5 Ongoing Research -- References -- Approximated Gaussian Random Field Under Different Parameterizations for MCMC -- 1 Introduction -- 2 Spatial Modelling -- 2.1 The SPDE Method -- 2.2 Spatial Random Field Parameterization -- 3 Simulation -- 3.1 Simulation Results -- 4 Real Application: Index of Relative Abundance for Pollock -- 4.1 Real Application Results. , 5 Conclusion -- References -- Shape Analysis of AF Segments for Rapid Assessment of Mohs Layers for BCC Presence by AF-Raman Microscopy -- 1 Motivation and Objectives -- 2 Data -- 3 Methods, Related Work -- 4 Results and Discussion -- References -- Additive Mixed Models for Location, Scale and Shape via Gradient Boosting Techniques -- 1 Overview -- 2 Methods -- 2.1 Model Formulation -- 2.2 Boosting Algorithm -- 2.3 Computational Details -- 3 Cystis Fibrosis Application -- 4 Outlook -- References -- Regression Analysis with Missing Data Using Interval Imputation -- 1 Introduction -- 2 Interval Imputation -- 3 Bayesian Analysis -- 3.1 Point Estimation from Intervals -- 4 Illustration -- 4.1 Synthetic Dataset -- 4.2 Naval Propulsion Plants Data Set -- 5 Conclusion -- References -- Models of Network Delay -- 1 Introduction -- 2 Queuing Theory and Related Work -- 3 Notation and Convolution -- 4 Combination of Gamma Random Variables -- 5 Fits to Real Data -- 5.1 Data Set -- 5.2 Fits to Data -- 5.3 Estimating the Latency Shift -- 6 Conclusion -- References -- Estimating Dose and Time of Exposure from a Protein-Based Radiation Biomarker -- 1 Introduction -- 2 A Model Linking Calibration Curves with Foci Decay -- 3 Parameter Elicitation -- 4 Simulation Test Results -- 5 Real Data Bootstrap Test Results -- 6 Conclusion -- References -- Statistical Modelling for Big and Little Data -- 1 Introduction -- 2 Big Data -- 3 Little Data -- 4 Big and Little Data -- References -- Semi-Markov Multistate Model with Interval-Censored Transition Times -- 1 Introduction -- 2 Methodology -- 2.1 General Framework -- 2.2 Semi-Markov Multistate Modeling Approach -- 2.3 Interval-Censored Transition Times from Intermediate States -- 3 Three-Wave COVID-19 Data -- 4 Conclusions -- References -- A Distance-Based Statistic for Goodness-of-Fit Assessment -- 1 Introduction. , 2 Material and Methods.

Additional Edition: ISBN 3-031-65722-5

Language: English