ebook ebooks e-book e-books downloaden bei MyEbooks.ch downloaden

Statistical Modelling and Regression Structures Festschrift in Honour of Ludwig Fahrmeir

:	Thomas Kneib, Gerhard Tutz
:	Thomas Kneib, Gerhard Tutz
:	Statistical Modelling and Regression Structures Festschrift in Honour of Ludwig Fahrmeir
:	Physica-Verlag
:	9783790824131
:	1
:	CHF 85.30
:

:	Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
:	English

:	472
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

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.

	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	40
	43	40
	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	47
	49	47
	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
	s	85
	s	86
	ß	87
	4 An Alternative Approach for Conjugate Priors in Bayesian Linear Regression ( CCCP)	88
	ß	91
	s	91
	s	91
	ß	95
	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