Semiparametric regression
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| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2003
|
| Ausgabe: | 1. publ. |
| Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
[12] |
| Schlagworte: | |
| Online-Zugang: | Sample text Table of contents Publisher description Inhaltsverzeichnis Klappentext |
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| 245 | 1 | 0 | |a Semiparametric regression |c David Ruppert ; M. P. Wand ; R. J. Carroll |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2003 | |
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| 490 | 1 | |a Cambridge series in statistical and probabilistic mathematics |v [12] | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 4 | |a Analyse de régression | |
| 650 | 7 | |a Non-parametrische statistiek |2 gtt | |
| 650 | 7 | |a Regressieanalyse |2 gtt | |
| 650 | 4 | |a Statistique non paramétrique | |
| 650 | 4 | |a Nonparametric statistics | |
| 650 | 4 | |a Regression analysis | |
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| 700 | 1 | |a Carroll, Raymond J. |d 1949- |e Verfasser |0 (DE-588)111805708 |4 aut | |
| 830 | 0 | |a Cambridge series in statistical and probabilistic mathematics |v [12] |w (DE-604)BV011442366 |9 12 | |
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Datensatz im Suchindex
| _version_ | 1819669283694706688 |
|---|---|
| adam_text | Contents
Preface
page
xiii
Guide to Notation
xv
1
Introduction
1
1.1
Assessing the Carcinogenicity of Phenolphthalein
3
1.2
Salinity and Fishing in North Carolina
4
1.3
Management of a Retirement Fund
5
1.4
Biomonitoring
of Airborne Mercury
7
1.5
Term Structure of Interest Rates
7
1.6
Air Pollution and Mortality in Milan: The Harvesting Effect
11
2
Parametric Regression
15
2.1
Introduction
15
2.2
Linear Regression Models
15
2.3
Regression Diagnostics
20
2.4
Inference
28
2.5
Parametric Additive Models
36
2.6
Model Selection
44
2.7
Polynomial Regression Models
46
2.8
Nonlinear Regression
48
2.9
Transformations in Regression
51
2.10
Bibliographic Notes
55
2.11
Summary of Formulas
55
3
Scatterplot Smoothing
57
3.1
Introduction
57
3.2
Preliminary Ideas
58
3.3
Practical Implementation
62
3.4
Automatic Knot Selection
64
3.5
Penalized Spline Regression
65
3.6
Quadratic Spline Bases
67
3.7
Other Spline Models and Bases
69
3.8
Other Penalties
74
3.9
General Definition of a Penalized Spline
75
3.10
Linear Smoothers
76
3.11
Error of a Smoother
76
3.12
Rank of a Smoother
78
3.13
Degrees of Freedom of a Smoother
80
3.14
Residual Degrees of Freedom
82
3.15
Other Approaches to Scatterplot Smoothing
84
3.16
Choosing a Scatterplot Smoother
87
3.17
Bibliographical Notes
88
3.18
Summary of Formulas
89
4
Mixed Models
91
4.1
Introduction
91
4.2
Mixed Models
91
4.3
Prediction
95
4.4
The Linear Mixed Model (LMM)
98
4.5
Estimation and Prediction in LMM
98
4.6
Estimated BLUP (EBLUP)
101
4.7
Standard Error Estimation
102
4.8
Hypothesis Testing
104
4.9
Penalized Splines as BLUPs
108
4.10
Bibliographical Notes
110
4.11
Summary of Formulas
110
5
Automatic Scatterplot Smoothing
112
5.1
Introduction
112
5.2
The Likelihood Approach
113
5.3
The Model Selection Approach
114
5.4
Caveats of Automatic Parameter Selection
120
5.5
Choosing the Knots and Basis Functions
123
5.6
Automatic Selection of the Number of Knots
127
5.7
Bibliographical Notes
131
5.8
Summary of Formulas
131
6
Inference
133
6.1
Introduction
133
6.2
Variability Bands
133
6.3
Confidence and Prediction Intervals
135
6.4
Inference for Penalized Splines
137
6.5
Simultaneous Confidence Bands
142
6.6
Testing the Adequacy of Parametric Models
145
6.7
Testing for No Effect
149
6.8
Inference Using First Derivatives
151
6.9
Testing for Existence of a Feature
156
6.10
Bibliographical Notes
158
6.11
Summary of Formulas
159
7
Simple Semiparametric Models
161
7.1
Introduction
161
7.2
Beyond Scatterplot Smoothing
161
7.3
Semiparametric Binary Offset Model
7.4
Additivity and Interactions
7.5
General Parametric Component
7.6
Inference
7.7
Bibliographical Notes
8
Additive Models
8.1
Introduction
8.2
Fitting an Additive Model
8.3
Degrees of Freedom
8.4
Smoothing Parameter Selection
8.5
Hypothesis Testing
8.6
Model Selection
8.7
Bibliographical Notes
9
Semiparametric Mixed Models
9.1
Introduction
9.2
Additive Mixed Models
9.3
Subject-Specific Curves
9.4
Bibliographical Notes
10
Generalized Parametric Regression
10.1
Introduction
10.2
Binary Response Data
10.3
Logistic Regression
10.4
Other Generalized Linear Models
10.5
Iteratively Reweighted Least Squares
10.6
Hat Matrix, Degrees of Freedom, and Standard Errors
10.7
Overdispersion and Variance Functions: Pseudolikelihood
10.8
Generalized Linear Mixed Models
10.9
Deviance
10.10
Technical Details
10.11
Bibliographical Notes
11
Generalized Additive Models
11.1
Introduction
11.2
Generalized Scatterplot Smoothing
11.3
Generalized Additive Mixed Models
11.4
Degrees-of-Freedom Approximations
11.5
Automatic Smoothing Parameter Selection
11.6
Hypothesis Testing
11.7
Model Selection
11.8
Density Estimation
11.9
Bibliographical Notes
12
Interaction Models
12.1
Introduction
12.2
Binary-by-Continuous Interaction Models
12.3
Factor
-by-Curve Interactions in Additive Models
226
12.4
Varying Coefficient Models
234
12.5
Continuous-by-Continuous Interactions
235
12.6
Bibliographical Notes
237
13
Bivariate Smoothing
238
13.1
Introduction
238
13.2
Choice of Bivariate Basis Functions
240
13.3
Kriging
242
13.4
General Radial Smoothing
248
13.5
Default Automatic Bivariate Smoother
256
13.6
Geoadditive Models
258
13.7
Additive Plus Interaction Models
259
13.8
Generalized Bivariate Smoothing
259
13.9
Appendix: Equivalence of BLUP using Z« and
Ър
259
13.10
Bibliographical Notes
260
14
Variance Function Estimation
261
14.1
Introduction
261
263
264
266
267
15
Measurement Error
268
268
269
270
273
273
275
16
Bayesian Semiparametric Regression
276
16.1
Introduction
276
16.2
General Framework
277
16.3
Scatterplot Smoothing
279
16.4
Linear Mixed Models
285
16.5
Generalized Linear Mixed Models
288
16.6
Rao-Blackwellization
291
16.7
Bibliographical Notes
292
17
Spatially Adaptive Smoothing
293
17.1
Introduction
293
17.2
A Local Penalty Method
294
17.3
Completely Automatic Algorithm
295
17.4
Bayesian Inference
296
14.2
Formulation
14.3
Application to the
LIDAR
Data
14.4
Quasilikelihood and Variance Functions
14.5
Bibliographical Notes
Measurement Error
15.1
Introduction
15.2
Formulation
15.3
The Expectation Maximization (EM) Algorithm
15.4
Simulated Example Revisited
15.5
Sensitivity Analysis Example
15.6
Bibliographical Notes
17.5
Simulations
298
17.6
LIDAR
Example
304
17.7
Additive Models
305
17.8
Bibliographical Notes
307
18
Analyses
308
18.1
Cancer Rates on Cape Cod
308
18.2
Assessing the Carcinogenicity of Phenolphthalein
308
18.3
Salinity and Fishing in North Carolina
308
18.4
Management of a Retirement Fund
ЗІЗ
18.5
Biomonitoring
of Airborne Mercury
314
18.6
Term Structure of Interest Rates
315
18.7
Air Pollution and Mortality in Milan: The Harvesting Effect
319
19
Epilogue
320
320
320
321
325
326
326
326
331
333
335
335
В
Computational Issues
336
B.
1
Fast Computation of Penalized Spline Smooths
336
B.2 Computation of Covariance Matrix Estimators
351
B.3 Software
353
Bibliography
361
Author Index
375
Notation Index
380
Example Index
381
Subject Index
382
Epilogue
19.1
Introduction
19.2
Minimalist Statistics
19.3
Some Omitted Topics
19.4
Future Research
Technical Complements
A.I
Introduction
A.2
Matrix Definitions and Results
A.3
Linear Algebra
A
.4
Probability Definitions and Results
A.5
Maximum Likelihood Estimation
A.6
Bibliographical Notes
Semiparametric
regression is concerned with the flexible incorporation of nonlinear
functional relationships in regression analyses. Any application area that uses
regression analysis can benefit from semiparametric regression. Assuming only a
basic familiarity with ordinary parametric regression, this user-friendly book
explains the techniques and benefits of semiparametric regression in a concise and
modular fashion. The authors make liberal use of graphics and examples plus case
studies taken from environmental, financial, and other applications. They include
practical advice on implementation and pointers to relevant software.
This book is suitable as a textbook for students with little background in
regression as well as a reference book for statistically oriented scientists
-
such
as biostatisticians, econometricians, quantitative social scientists, and
epidemiologists
-
with a good working knowledge of regression and the desire to
begin using more flexible semiparametric models. Even experts on semiparametric
regression should find something new here.
|
| any_adam_object | 1 |
| author | Ruppert, David 1948- Wand, Matt P. 1963- Carroll, Raymond J. 1949- |
| author_GND | (DE-588)133112209 (DE-588)133057526 (DE-588)111805708 |
| author_facet | Ruppert, David 1948- Wand, Matt P. 1963- Carroll, Raymond J. 1949- |
| author_role | aut aut aut |
| author_sort | Ruppert, David 1948- |
| author_variant | d r dr m p w mp mpw r j c rj rjc |
| building | Verbundindex |
| bvnumber | BV019769434 |
| callnumber-first | Q - Science |
| callnumber-label | QA278 |
| callnumber-raw | QA278.2 |
| callnumber-search | QA278.2 |
| callnumber-sort | QA 3278.2 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | MR 2100 QB 910 QH 234 SK 840 |
| ctrlnum | (OCoLC)51222299 (DE-599)BVBBV019769434 |
| dewey-full | 519.5/36 519.536 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 519.536 |
| dewey-search | 519.5/36 519.536 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Soziologie Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV019769434 |
| illustrated | Illustrated |
| indexdate | 2024-12-23T18:12:17Z |
| institution | BVB |
| isbn | 0521780500 0521785162 9780521780506 9780521785167 |
| language | English |
| lccn | 2002041460 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-013095610 |
| oclc_num | 51222299 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-11 DE-355 DE-BY-UBR DE-29 DE-706 DE-188 DE-824 DE-739 DE-20 DE-521 |
| owner_facet | DE-19 DE-BY-UBM DE-11 DE-355 DE-BY-UBR DE-29 DE-706 DE-188 DE-824 DE-739 DE-20 DE-521 |
| physical | XVI, 386 S. zahlr. graph. Darst., Kt. |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| series | Cambridge series in statistical and probabilistic mathematics |
| series2 | Cambridge series in statistical and probabilistic mathematics |
| spellingShingle | Ruppert, David 1948- Wand, Matt P. 1963- Carroll, Raymond J. 1949- Semiparametric regression Cambridge series in statistical and probabilistic mathematics Analyse de régression Non-parametrische statistiek gtt Regressieanalyse gtt Statistique non paramétrique Nonparametric statistics Regression analysis Regressionsanalyse (DE-588)4129903-6 gnd Nichtparametrische Statistik (DE-588)4226777-8 gnd |
| subject_GND | (DE-588)4129903-6 (DE-588)4226777-8 |
| title | Semiparametric regression |
| title_auth | Semiparametric regression |
| title_exact_search | Semiparametric regression |
| title_full | Semiparametric regression David Ruppert ; M. P. Wand ; R. J. Carroll |
| title_fullStr | Semiparametric regression David Ruppert ; M. P. Wand ; R. J. Carroll |
| title_full_unstemmed | Semiparametric regression David Ruppert ; M. P. Wand ; R. J. Carroll |
| title_short | Semiparametric regression |
| title_sort | semiparametric regression |
| topic | Analyse de régression Non-parametrische statistiek gtt Regressieanalyse gtt Statistique non paramétrique Nonparametric statistics Regression analysis Regressionsanalyse (DE-588)4129903-6 gnd Nichtparametrische Statistik (DE-588)4226777-8 gnd |
| topic_facet | Analyse de régression Non-parametrische statistiek Regressieanalyse Statistique non paramétrique Nonparametric statistics Regression analysis Regressionsanalyse Nichtparametrische Statistik |
| url | http://www.loc.gov/catdir/samples/cam033/2002041460.html http://www.loc.gov/catdir/toc/cam031/2002041460.html http://www.loc.gov/catdir/description/cam031/2002041460.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013095610&sequence=000007&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013095610&sequence=000008&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV011442366 |
| work_keys_str_mv | AT ruppertdavid semiparametricregression AT wandmattp semiparametricregression AT carrollraymondj semiparametricregression |