Statistical Modelling and Regression Structures (eBook)

Festschrift in Honour of Ludwig Fahrmeir

Thomas Kneib, Gerhard Tutz (Herausgeber)

eBook Download: PDF

2010 | 2010
XXIV, 472 Seiten
Physica (Verlag)
978-3-7908-2413-1 (ISBN)

Infos

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.

Inhalt

Foreword 5
Acknowledgements 7
Contents 8
List of Contributors 17
The Smooth Complex Logarithm and Quasi- Periodic Models 23
1 Foreword 23
2 Introduction 23
3 Data and Models 24
4 More to Explore 34
5 Discussion 37
References 39
P-spline Varying Coefficient Models for Complex Data 40
1 Introduction 40
2 ÏLarge Scale" VCM, without Backfitting 43
3 Notation and Snapshot of a Smoothing Tool: B-splines 45
4 Using B-splines for Varying Coefficient Models 47
5 P-spline Snapshot: Equally-Spaced Knots & Penalization
6 Optimally Tuning P-splines 52
7 MoreKTBResults 54
8 Extending P-VCM into the Generalized Linear Model 54
9 Two-dimensional Varying Coefficient Models 57
10 Discussion Toward More Complex VCMs 62
References 63
Penalized Splines, Mixed Models and Bayesian Ideas 65
1 Introduction 65
2 Notation and Penalized Splines as Linear Mixed Models 66
3 Classification with Mixed Models 68
4 Variable Selection with Simple Priors 70
5 Discussion and Extensions 76
References 77
Bayesian Linear RegressionÛ Different Conjugate Models and Their ( In) Sensitivity to Prior- Data Conflict 79
1 Introduction 79
2 Prior-data Conflict in the i.i.d. Case 82
3 The Standard Approach for Bayesian Linear Regression (SCP) 84
ß 85
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4 An Alternative Approach for Conjugate Priors in Bayesian Linear Regression ( CCCP) 88
ß 91
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5 Discussion and Outlook 96
References 97
An Efficient Model Averaging Procedure for Logistic Regression Models Using a Bayesian Estimator with Laplace Prior 99
1 Introduction 99
2 Model Averaging 100
3 Simulation Study 106
4 Conclusion and Outlook 108
References 109
Posterior and Cross-validatory Predictive Checks: A Comparison of MCMC and INLA 111
1 Introduction 111
2 The INLA Approach 112
3 Predictive Model Checks with MCMC 116
4 Application 119
5 Discussion 127
References 129
Data Augmentation and MCMC for Binary and Multinomial Logit Models 131
1 Introduction 131
2 MCMC Estimation Based on Data Augmentation for Binary Logit Regression Models 133
3 MCMC Estimation Based on Data Augmentation for the Multinomial Logit Regression Model 140
4 MCMC Sampling without Data Augmentation 143
5 Comparison of the Various MCMC Algorithms 145
6 Concluding Remarks 150
References 151
Generalized Semiparametric Regression with Covariates Measured with Error 153
1 Introduction 153
2 Semiparametric Regression Models with Measurement Error 155
3 Bayesian Inference 159
4 Simulations 163
5 Incident Heart Failure in the ARIC Study 170
6 Summary 173
References 173
Determinants of the Socioeconomic and Spatial Pattern of Undernutrition by Sex in India: A Geoadditive Semi- parametric Regression Approach 175
1 Introduction 175
2 TheData 178
3 Measurement and Determinants of Undernutrition 180
4 Variables Included in the Regression Model 182
5 Statistical Methodology - Semiparametric Regression Analysis 187
6 Results 190
7 Conclusion 197
References 198
Boosting for Estimating Spatially Structured Additive Models 200
1 Introduction 200
2 Methods 202
3 Results 208
4 Discussion 213
References 214
Generalized Linear Mixed Models Based on Boosting 216
1 Introduction 216
2 Generalized Linear Mixed Models - GLMM 217
3 Boosted Generalized Linear Mixed Models - bGLMM 219
4 Application to CD4 Data 231
5 Concluding Remarks 233
References 233
Measurement and Predictors of a Negative Attitude towards Statistics among LMU Students 235
1 Introduction 235
2 Method 237
3 Results 239
4 Discussion and Conclusion 245
References 247
Graphical Chain Models and their Application 249
1 Introduction 249
2 Graphical Chain Models 251
3 Model Selection 253
4 Data Set 254
5 Results 258
6 Discussion 261
References 262
Appendix 264
Indirect Comparison of Interaction Graphs 266
1 Introduction 267
2 Methods 268
3 Example 272
4 Discussion 274
References 276
Appendix 277
. 278
Modelling, Estimation and Visualization of Multivariate Dependence for High- frequency Data 283
1 Multivariate Risk Assessment for Extreme Risk 283
2 Measuring Extreme Dependence 286
3 Extreme Dependence Estimation 296
4 High-frequency Financial Data 301
5 Conclusion 314
References 315
Ordinal- and Continuous-Response Stochastic Volatility Models for Price Changes: An Empirical Comparison 317
1 Introduction 317
2 Ordinal- and Continuous-Response Stochastic Volatility Models 319
3 Application 324
4 Summary and Discussion 335
References 336
Copula Choice with Factor Credit Portfolio Models 337
1 Introduction 337
2 Factor Models 339
3 The Berkowitz Test 341
4 Simulation Study and Analyses 344
5 Conclusion 351
References 351
Penalized Estimation for Integer Autoregressive Models 353
1 Introduction 353
2 Integer Autoregressive Processes and Inference 355
3 Penalized Conditional Least Squares Inference 357
4 Examples 359
5 Discussion 365
References 366
Appendix 367
Bayesian Inference for a Periodic Stochastic Volatility Model of Intraday Electricity Prices 369
1 Introduction 369
2 Periodic Autoregressions 371
3 Periodic Stochastic VolatilityModel 372
4 Bayesian Posterior Inference 375
5 Intraday Electricity Prices 377
6 Discussion 384
References 386
Appendix 388
S 391
Online Change-Point Detection in Categorical Time Series 393
1 Introduction 393
2 Modeling Categorical Time Series 394
3 Prospective CUSUM Changepoint Detection 398
4 Applications 404
5 Discussion 410
References 411
Multiple Linear Panel Regression with Multiplicative Random Noise 414
1 Introduction 414
2 The Model 416
3 The Naive Estimator and its Bias 417
4 Corrected Estimator 420
5 Residual Variance and Intercept 422
6 Asymptotic Covariance Matrix 423
7 Simulation 424
8 Conclusion 427
References 428
A Note on Using Multiple Singular Value Decompositions to Cluster Complex Intracellular Calcium Ion Signals 433
1 Introduction 433
2 Experiment 435
3 Methods 436
Ca2+ 437
Ca2+ 438
4 Clustering 441
5 Conclusion 441
References 442
On the self-regularization property of the EM algorithm for Poisson inverse problems 445
1 Introduction 445
2 Scaling properties of the EM algorithm 453
3 The effect of the initial guess 457
References 460
Sequential Design of Computer Experiments for Constrained Optimization 463
1 Introduction 464
2 Modeling 465
3 A Minimization Algorithm 467
4 An Autoregressive Model and Example 472
5 Discussion 480
References 485

Produkt-Details

• Erscheint lt. Verlag: 12.1.2010
• Zusatzinfo: XXIV, 472 p.
• Verlagsort: Heidelberg
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Schlagworte: Bayesian Statistics • Estimator • expectation–maximization algorithm • linear regression • Logistic Regression • regression models • Semiparametric Regression • statistical model • Statistical Modelling • Time Series
• ISBN-10: 3-7908-2413-5 / 3790824135
• ISBN-13: 978-3-7908-2413-1 / 9783790824131