Kneib / Tutz Statistical Modelling and Regression Structures

Festschrift in Honour of Ludwig Fahrmeir
1. Auflage 2010
ISBN: 978-3-7908-2413-1
Verlag: Physica-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 472 Seiten

Autoren/Hrsg.

Kneib, Thomas

Tutz, Gerhard

Inhaltsverzeichnis

1;Foreword;5
2;Acknowledgements;7
3;Contents;8
4;List of Contributors;17
5;The Smooth Complex Logarithm and Quasi- Periodic Models;23
5.1;1 Foreword;23
5.2;2 Introduction;23
5.3;3 Data and Models;24
5.4;4 More to Explore;34
5.5;5 Discussion;37
5.6;References;39
6;P-spline Varying Coefficient Models for Complex Data;40
6.1;1 Introduction;40
6.2;2 ÏLarge Scale" VCM, without Backfitting;43
6.3;3 Notation and Snapshot of a Smoothing Tool: B-splines;45
6.4;4 Using B-splines for Varying Coefficient Models;47
6.5;5 P-spline Snapshot: Equally-Spaced Knots & Penalization;49
6.6;6 Optimally Tuning P-splines;52
6.7;7 MoreKTBResults;54
6.8;8 Extending P-VCM into the Generalized Linear Model;54
6.9;9 Two-dimensional Varying Coefficient Models;57
6.10;10 Discussion Toward More Complex VCMs;62
6.11;References;63
7;Penalized Splines, Mixed Models and Bayesian Ideas;65
7.1;1 Introduction;65
7.2;2 Notation and Penalized Splines as Linear Mixed Models;66
7.3;3 Classification with Mixed Models;68
7.4;4 Variable Selection with Simple Priors;70
7.5;5 Discussion and Extensions;76
7.6;References;77
8;Bayesian Linear RegressionÛ Different Conjugate Models and Their ( In) Sensitivity to Prior- Data Conflict;79
8.1;1 Introduction;79
8.2;2 Prior-data Conflict in the i.i.d. Case;82
8.3;3 The Standard Approach for Bayesian Linear Regression (SCP);84
8.4;ß;85
8.5;s;85
8.6;s;86
8.7;ß;87
8.8;4 An Alternative Approach for Conjugate Priors in Bayesian Linear Regression ( CCCP);88
8.9;ß;91
8.10;s;91
8.11;s;91
8.12;ß;95
8.13;5 Discussion and Outlook;96
8.14;References;97
9;An Efficient Model Averaging Procedure for Logistic Regression Models Using a Bayesian Estimator with Laplace Prior;99
9.1;1 Introduction;99
9.2;2 Model Averaging;100
9.3;3 Simulation Study;106
9.4;4 Conclusion and Outlook;108
9.5;References;109
10;Posterior and Cross-validatory Predictive Checks: A Comparison of MCMC and INLA;111
10.1;1 Introduction;111
10.2;2 The INLA Approach;112
10.3;3 Predictive Model Checks with MCMC;116
10.4;4 Application;119
10.5;5 Discussion;127
10.6;References;129
11;Data Augmentation and MCMC for Binary and Multinomial Logit Models;131
11.1;1 Introduction;131
11.2;2 MCMC Estimation Based on Data Augmentation for Binary Logit Regression Models;133
11.3;3 MCMC Estimation Based on Data Augmentation for the Multinomial Logit Regression Model;140
11.4;4 MCMC Sampling without Data Augmentation;143
11.5;5 Comparison of the Various MCMC Algorithms;145
11.6;6 Concluding Remarks;150
11.7;References;151
12;Generalized Semiparametric Regression with Covariates Measured with Error;153
12.1;1 Introduction;153
12.2;2 Semiparametric Regression Models with Measurement Error;155
12.3;3 Bayesian Inference;159
12.4;4 Simulations;163
12.5;5 Incident Heart Failure in the ARIC Study;170
12.6;6 Summary;173
12.7;References;173
13;Determinants of the Socioeconomic and Spatial Pattern of Undernutrition by Sex in India: A Geoadditive Semi- parametric Regression Approach;175
13.1;1 Introduction;175
13.2;2 TheData;178
13.3;3 Measurement and Determinants of Undernutrition;180
13.4;4 Variables Included in the Regression Model;182
13.5;5 Statistical Methodology - Semiparametric Regression Analysis;187
13.6;6 Results;190
13.7;7 Conclusion;197
13.8;References;198
14;Boosting for Estimating Spatially Structured Additive Models;200
14.1;1 Introduction;200
14.2;2 Methods;202
14.3;3 Results;208
14.4;4 Discussion;213
14.5;References;214
15;Generalized Linear Mixed Models Based on Boosting;216
15.1;1 Introduction;216
15.2;2 Generalized Linear Mixed Models - GLMM;217
15.3;3 Boosted Generalized Linear Mixed Models - bGLMM;219
15.4;4 Application to CD4 Data;231
15.5;5 Concluding Remarks;233
15.6;References;233
16;Measurement and Predictors of a Negative Attitude towards Statistics among LMU Students;235
16.1;1 Introduction;235
16.2;2 Method;237
16.3;3 Results;239
16.4;4 Discussion and Conclusion;245
16.5;References;247
17;Graphical Chain Models and their Application;249
17.1;1 Introduction;249
17.2;2 Graphical Chain Models;251
17.3;3 Model Selection;253
17.4;4 Data Set;254
17.5;5 Results;258
17.6;6 Discussion;261
17.7;References;262
17.8;Appendix;264
18;Indirect Comparison of Interaction Graphs;266
18.1;1 Introduction;267
18.2;2 Methods;268
18.3;3 Example;272
18.4;4 Discussion;274
18.5;References;276
18.6;Appendix;277
18.7;.;278
19;Modelling, Estimation and Visualization of Multivariate Dependence for High- frequency Data;283
19.1;1 Multivariate Risk Assessment for Extreme Risk;283
19.2;2 Measuring Extreme Dependence;286
19.3;3 Extreme Dependence Estimation;296
19.4;4 High-frequency Financial Data;301
19.5;5 Conclusion;314
19.6;References;315
20;Ordinal- and Continuous-Response Stochastic Volatility Models for Price Changes: An Empirical Comparison;317
20.1;1 Introduction;317
20.2;2 Ordinal- and Continuous-Response Stochastic Volatility Models;319
20.3;3 Application;324
20.4;4 Summary and Discussion;335
20.5;References;336
21;Copula Choice with Factor Credit Portfolio Models;337
21.1;1 Introduction;337
21.2;2 Factor Models;339
21.3;3 The Berkowitz Test;341
21.4;4 Simulation Study and Analyses;344
21.5;5 Conclusion;351
21.6;References;351
22;Penalized Estimation for Integer Autoregressive Models;353
22.1;1 Introduction;353
22.2;2 Integer Autoregressive Processes and Inference;355
22.3;3 Penalized Conditional Least Squares Inference;357
22.4;4 Examples;359
22.5;5 Discussion;365
22.6;References;366
22.7;Appendix;367
23;Bayesian Inference for a Periodic Stochastic Volatility Model of Intraday Electricity Prices;369
23.1;1 Introduction;369
23.2;2 Periodic Autoregressions;371
23.3;3 Periodic Stochastic VolatilityModel;372
23.4;4 Bayesian Posterior Inference;375
23.5;5 Intraday Electricity Prices;377
23.6;6 Discussion;384
23.7;References;386
23.8;Appendix;388
23.9;S;391
24;Online Change-Point Detection in Categorical Time Series;393
24.1;1 Introduction;393
24.2;2 Modeling Categorical Time Series;394
24.3;3 Prospective CUSUM Changepoint Detection;398
24.4;4 Applications;404
24.5;5 Discussion;410
24.6;References;411
25;Multiple Linear Panel Regression with Multiplicative Random Noise;414
25.1;1 Introduction;414
25.2;2 The Model;416
25.3;3 The Naive Estimator and its Bias;417
25.4;4 Corrected Estimator;420
25.5;5 Residual Variance and Intercept;422
25.6;6 Asymptotic Covariance Matrix;423
25.7;7 Simulation;424
25.8;8 Conclusion;427
25.9;References;428
26;A Note on Using Multiple Singular Value Decompositions to Cluster Complex Intracellular Calcium Ion Signals;433
26.1;1 Introduction;433
26.2;2 Experiment;435
26.3;3 Methods;436
26.4;Ca2+;437
26.5;Ca2+;438
26.6;4 Clustering;441
26.7;5 Conclusion;441
26.8;References;442
27;On the self-regularization property of the EM algorithm for Poisson inverse problems;445
27.1;1 Introduction;445
27.2;2 Scaling properties of the EM algorithm;453
27.3;3 The effect of the initial guess;457
27.4;References;460
28;Sequential Design of Computer Experiments for Constrained Optimization;463
28.1;1 Introduction;464
28.2;2 Modeling;465
28.3;3 A Minimization Algorithm;467
28.4;4 An Autoregressive Model and Example;472
28.5;5 Discussion;480
28.6;References;485