Mantovan / Secchi Complex Data Modeling and Computationally Intensive Statistical Methods

1. Auflage 2011
ISBN: 978-88-470-1386-5
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 164 Seiten

Autoren/Hrsg.

Mantovan, Pietro

Secchi, Piercesare

Inhaltsverzeichnis

1;Title Page ;1
2;Copyright Page ;4
3;Preface;5
4;Table of Contents;7
5;List of Contributors;9
6;Space-time texture analysis in thermal infraredimaging for classification of Raynaud’s Phenomenon;11
6.1;1 Introduction;11
6.2;2 TheData;12
6.3;3 Processing thermal high resolution infrared images;13
6.3.1;3.1 Segmentation;13
6.3.2;3.2 Registration;13
6.4;4 Feature extraction;15
6.4.1;4.1 ST-GMRFs;16
6.4.2;4.2 Texture statistics through co-occurrence matrices;18
6.5;5 Classification results;19
6.6;6 Conclusions;20
6.7;References;21
7;Mixed-effects modelling of Kevlar fibre failure timesthrough Bayesian non-parametrics;23
7.1;1 Introduction;23
7.2;2 Accelerated life models for Kevlar fibre life data;25
7.3;3 The Bayesian semiparametric AFT model;26
7.4;4 Data analysis;28
7.5;5 Conclusions;34
7.6;Appendix;34
7.7;References;36
8;Space filling and locally optimal designs for Gaussian Universal Kriging;37
8.1;1 Introduction;37
8.2;2 Kriging methodology;39
8.3;3 Optimality of space filling designs;40
8.4;4 Locally optimal designs for Universal Kriging;41
8.4.1;4.1 Optimal designs for estimation;41
8.4.2;4.2 Optimal designs for prediction;46
8.5;5 Conclusions;48
8.6;References;48
9;Exploitation, integration and statistical analysis of thePublic Health Database and STEMI Archive in theLombardia region;50
9.1;1 Introduction;50
9.2;2 The MOMI2 study;52
9.3;3 The STEMI Archive;55
9.4;4 The Public Health Database;56
9.4.1;4.1 Healthcare databases;57
9.4.2;4.2 Health information systems in Lombardia;58
9.5;5 The statistical perspective;58
9.5.1;5.1 Frailty models;59
9.5.2;5.2 Generalised linear mixed models;60
9.5.3;5.3 Bayesian hierarchical models;61
9.6;6 Conclusions;62
9.7;References;62
10;Bootstrap algorithms for variance estimation in PS sampling;65
10.1;1 Introduction;65
10.2;2 The naïve boostrap;66
10.3;3 Holmberg’s PS bootstrap;67
10.4;4 The 0.5 PS-bootstrap;70
10.5;5 The x-balanced PS-bootstrap;70
10.6;6 Simulation study;71
10.7;7 Conclusions;76
10.8;References;76
11;Fast Bayesian functional data analysis of basal body temperature;78
11.1;1 Introduction;78
11.2;2 Methods;80
11.2.1;2.1 RVM in linear models;80
11.2.2;2.2 Extension to linear mixed model;81
11.3;3 Results: application to bbt data;84
11.3.1;3.1 Subject-specific profiles;85
11.3.2;3.2 Subject-specific and population average profiles;86
11.3.3;3.3 Prediction;88
11.4;4 Conclusions;88
11.5;References;89
12;A parametric Markov chain to model age- and state-dependent wear processes ;91
12.1;1 Introduction;91
12.2;2 System description and preliminary technological considerations;93
12.3;3 Data description and preliminary statistical considerations;94
12.4;4 Model description;97
12.5;5 Parameter estimation;99
12.6;6 Testing dependence on time and/or state;101
12.7;7 Conclusions;102
12.8;References;103
13;Case studies in Bayesian computation using INLA;104
13.1;1 Introduction;104
13.2;2 Latent Gaussian models;105
13.3;3 Integrated Nested Laplace Approximation;107
13.4;4 The INLA package for R;108
13.5;5 Case studies;108
13.5.1;5.1 A GLMM with over-dispersion;108
13.5.2;5.2 Childhood under nutrition in Zambia: spatial analysis;110
13.5.3;5.3 A simple example of survival data analysis;115
13.6;6 Conclusions;117
13.7;References;118
14;A graphical models approach for comparing gene sets;120
14.1;1 Introduction;104
14.2;2 Latent Gaussian models;105
14.3;3 Integrated Nested Laplace Approximation;107
14.4;4 The INLA package for R;108
14.5;5 Case studies;108
14.5.1;5.1 A GLMM with over-dispersion;108
14.5.2;5.2 Childhood undernutrition in Zambia: spatial analysis;110
14.5.3;5.3 A simple example of survival data analysis;115
14.6;6 Conclusions;117
14.7;References;118
15;A graphical models approach for comparing gene sets;120
15.1;1 Introduction;120
15.2;2 A brief introduction to pathways;121
15.3;3 Data and graphical models setup;123
15.4;4 Test of equality of two concentration matrices;125
15.5;5 Conclusions;126
15.6;References;126
16;Predictive densities and prediction limits based onpredictive likelihoods;128
16.1;1 Introduction;128
16.2;2 Review on predictive methods;129
16.3;2.1 Plug-in predictive procedures and improvements;130
16.4;2.2 Profile predictive likelihood and modifications;131
16.5;3 Likelihood-based predictive distributions and prediction limits;132
16.5.1;3.1 Probability distributions from predictive likelihoods;133
16.5.2;3.2 Prediction limits and coverage probabilities;135
16.5.3;4 Examples;135
16.5.3.1;4.1 Prediction limits for the sum of future Gaussian observations;136
16.5.3.2;4.2 Prediction limits for the maximum of future Gaussian observations;138
16.5.4;Appendix;139
16.5.5;References;141
17;Computer-intensive conditional inference;142
17.1;1 Introduction;142
17.2;2 An inference problem;144
17.3;3 Exponential family and ancillary statistic models;145
17.4;4 Analytic approximations;146
17.5;5 Bootstrap approximations;147
17.6;6 Examples;149
17.6.1;6.1 Inverse Gaussian distribution;149
17.6.2;6.2 Log-normal mean;150
17.6.3;6.3 Weibull distribution;151
17.6.4;6.4 Exponential regression;152
17.6.5;7 Conclusions;153
17.6.6;References;154
18;Monte Carlo simulation methods for reliability estimation and failure prognostics ;156
18.1;1 Introduction;157
18.2;2 The subset and line sampling methods for realiability estimation;158
18.3;3 Particle filtering for failure prognosis;161
18.4;4 Conclusions;166
18.5;References;167