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: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 472 Seiten

The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir`s far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.

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