Multivariate nonparametric regression and visualization: with R and applications to finance
"This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of v...
Gespeichert in:
| Format: | Karte |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2014
|
| Schriftenreihe: | Wiley series in computational statistics
699 |
| Schlagworte: | |
| Online-Zugang: | Cover image Inhaltsverzeichnis |
| Zusammenfassung: | "This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site".. |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXIII, 367 S. Ill., graph. Darst. |
| ISBN: | 9780470384428 |
Internformat
MARC
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| 245 | 1 | 0 | |a Multivariate nonparametric regression and visualization |b with R and applications to finance |c Jussi Klemelä |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2014 | |
| 300 | |a XXIII, 367 S. |b Ill., graph. Darst. | ||
| 336 | |b cri |2 rdacontent | ||
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| 490 | 1 | |a Wiley series in computational statistics |v 699 | |
| 500 | |a Includes bibliographical references and index | ||
| 520 | |a "This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site".. | ||
| 650 | 7 | |a MATHEMATICS / Probability & Statistics / General |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS / Probability & Statistics / Regression Analysis |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Programming Languages / Visual BASIC. |2 bisacsh | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Finance |x Mathematical models | |
| 650 | 4 | |a Visualization | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a MATHEMATICS / Probability & Statistics / General | |
| 650 | 4 | |a MATHEMATICS / Probability & Statistics / Regression Analysis | |
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Datensatz im Suchindex
| DE-BY-862_location | 2000 |
|---|---|
| DE-BY-FWS_call_number | 2000/QH 500 K64 |
| DE-BY-FWS_katkey | 653105 |
| DE-BY-FWS_media_number | 083000517975 |
| _version_ | 1815031029848604672 |
| adam_text | CONTENTS
Preface
xvii
Introduction
xix
1.1
Estimation of Functionals of Conditional Distributions
xx
1.2 Quantitative Finance
xxi
1.3 Visualization
xxi
1.4 Literature
xxiii
PART I METHODS OF REGRESSION AND CLASSIFICATION
1
Overview of Regression and Classification
3
1.1
Regression
3
1.1.1
Random Design and Fixed Design
4
1.1.2
Mean Regression
5
1.1.3
Partial Effects and Derivative Estimation
8
1.1.4
Variance Regression
9
1.1.5
Covariance and Correlation Regression
13
1.1.6
Quantile Regression
14
1.1.7
Approximation of the Response Variable
18
1.1.8
Conditional Distribution and Density
21
ix
1.1.9
Time Seríes
Data
23
1.1.10
Stochastic Control
25
1.1.11
Instrumental Variables
26
1.2
Discrete Response Variable
29
1.2.1
Binary Response Models
29
1.2.2
Discrete Choice Models
31
1.2.3
Count Data
33
1.3
Parametric Family Regression
33
1.3.1
General Parametric Family
З З
1.3.2
Exponential Family Regression
35
1.3.3
Copula Modeling
3 6
1.4
Classification
37
1.4.1
Bayes
Risk
38
1.4.2
Methods of Classification
39
1.5
Applications in Quantitative Finance
42
1.5.1
Risk Management
42
1.5.2
Variance Trading
44
1.5.3
Portfolio Selection
45
1.5.4
Option Pricing and Hedging
50
1.6
Data Examples
52
1.6.1
Time Series of S&P
500
Returns
52
1.6.2
Vector Time Series of S&P
500
and Nasdaq-
100
Returns
53
1.7
Data Transformations
53
1.7.1
Data Sphering
54
1.7.2
Copula Transformation
55
1.7.3
Transformations of the Response Variable
56
1.8
Central Limit Theorems
58
1.8.1
Independent Observations
58
1.8.2
Dependent Observations
58
1.8.3
Estimation of the Asymptotic Variance
60
1.9
Measuring the Performance of Estimators
61
1.9.1
Performance of Regression Function Estimators
61
1.9.2
Performance of Conditional Variance Estimators
66
1.9.3
Performance of Conditional Covariance Estimators
68
1.9.4
Performance of Quantile Function Estimators
69
1.9.5
Performance of Estimators of Expected Shortfall
71
1.9.6
Performance of Classifiers
72
1.10
Confidence Sets
73
1.10.1
Pointwise
Confidence
Intervals
73
1.10.2
Confidence
Bands
75
1.11
Testing
75
Linear Methods and Extensions
77
2.1
Linear Regression
78
2.1.1
Least Squares Estimator
79
2.1.2
Generalized Method of Moments Estimator
81
2.1.3
Ridge Regression
84
2.1.4
Asymptotic Distributions for Linear Regression
87
2.1.5
Tests and Confidence Intervals for Linear Regression
90
2.1.6
Variable Selection
92
2.1.7
Applications of Linear Regression
94
2.2
Varying Coefficient Linear Regression
97
2.2.1
The Weighted Least Squares Estimator
97
2.2.2
Applications of Varying Coefficient Regression
98
2.3
Generalized Linear and Related Models
102
2.3.1
Generalized Linear Models
102
2.3.2
Binary Response Models
104
2.3.3
Growth Models
107
2.4
Series Estimators
107
2.4.1
Least Squares Series Estimator
107
2.4.2
Orthonormal
Basis Estimator
108
2.4.3
Splines
110
2.5
Conditional Variance and ARCH Models
111
2.5.1
Least Squares Estimator
112
2.5.2
ARCH Model
113
2.6
Applications in Volatility and
Quantité
Estimation
116
2.6.1
Benchmarks for Quantile Estimation
116
2.6.2
Volatility and Quantiles with the LS Regression
118
2.6.3
Volatility with the Ridge Regression
121
2.6.4
Volatility and Quantiles with ARCH
122
2.7
Linear Classifiers
124
Kernel Methods and Extensions
127
3.1
Regressogram
129
3.2
Kernel Estimator
130
3.2.1
Definition of the Kernel Regression Estimator
130
3.2.2
Comparison to the
Regressogram
132
3.2.3 Gasser-Müller
and Priestley-Chao Estimators
134
3.2.4
Moving Averages
134
3.2.5
Locally Stationary Data
136
3.2.6
Curse of Dimensionality
140
3.2.7
Smoothing Parameter Selection
140
3.2.8
Effective Sample Size
142
3.2.9
Kernel Estimator of Partial Derivatives
145
3.2.10
Confidence Intervals in Kernel Regression
146
3.3
Nearest-Neighbor Estimator
147
3.4
Classification with Local Averaging
148
3.4.1
Kernel Classification
148
3.4.2
Nearest-Neighbor Classification
149
3.5
Median Smoothing
151
3.6
Conditional Density Estimation
152
3.6.1
Kernel Estimator of Conditional Density
152
3.6.2
Histogram Estimator of Conditional Density
156
3.6.3
Nearest-Neighbor Estimator of Conditional Density
157
3.7
Conditional Distribution Function Estimation
158
3.7.1
Local Averaging Estimator
159
3.7.2
Time-Space Smoothing
159
3.8
Conditional Quantile Estimation
160
3.9
Conditional Variance Estimation
162
3.9.1
State-Space Smoothing and Variance Estimation
162
3.9.2
GARCH and Variance Estimation
163
3.9.3
Moving Averages and Variance Estimation
172
3.10
Conditional Covariance Estimation
176
3.10.1
State-Space Smoothing and Covariance Estimation
178
3.10.2
GARCH and Covariance Estimation
178
3.10.3
Moving Averages and Co variance Estimation
181
3.11
Applications in Risk Management
181
3.11.1
Volatility Estimation
182
3.11.2
Covariance and Correlation Estimation
193
3.11.3
Quantile Estimation
198
3.12
Applications in Portfolio Selection
205
3.12.1
Portfolio Selection Using Regression Functions
205
3.12.2
Portfolio Selection Using Classification
215
3.12.3
Portfolio Selection Using
Markowitz
Criterion
223
4
Semiparametric and Structural Models
229
4.1
Single-Index Model
230
4.1.1
Definition of the Single-Index Model
230
4.1.2
Estimators in the Single-Index Model
230
4.2
Additive Model
234
4.2.1
Definition of the Additive Model
234
4.2.2
Estimators in the Additive Model
235
4.3
Other Semiparametric Models
237
4.3.1
Partially Linear Model
237
4.3.2
Related Models
238
5
Empirical Risk Minimization
241
5.1
Empirical Risk
243
5.1.1
Conditional Expectation
243
5.1.2
Conditional Quantile
244
5.1.3
Conditional Density
245
5.2
Local Empirical Risk
247
5.2.1
Local Polynomial Estimators
247
5.2.2
Local Likelihood Estimators
255
5.3
Support Vector Machines
257
5.4
Stagewise Methods
259
5.4.1
Forward Stagewise Modeling
259
5.4.2
Stagewise Fitting of Additive Models
261
5.4.3
Projection Pursuit Regression
262
5.5
Adaptive
Regressograms
264
5.5.1
Greedy
Regressograms
264
5.5.2
CART
268
5.5.3
Dyadic CART
271
5.5.4
Bootstrap Aggregation
272
PART II VISUALIZATION
6
Visualization of Data
277
6.1
Scatter Plots
278
6.1.1
Two-Dimensional Scatter Plots
278
6.1.2
One-Dimensional Scatter Plots
278
6.1.3
Three- and Higher-Dimensional Scatter Plots
282
6.2
Histogram and Kernel Density Estimator
283
6.3 Dimension
Reduction
284
6.3.1
Projection Pursuit
284
6.3.2
Multidimensional Scaling
286
6.4
Observations as Objects
288
6.4.1
Graphical Matrices
289
6.4.2
Parallel Coordinate Plots
290
6.4.3
Other Methods
293
7
Visualization of Functions
295
7.1
Slices
296
7.2
Partial Dependence Functions
298
7.3
Reconstruction of Sets
299
7.3.1
Estimation of Level Sets of a Function
300
7.3.2
Point Cloud Data
303
7.4
Level Set Trees
304
7.4.1
Definition and Illustrations
304
7.4.2
Calculation of Level Set Trees
308
7.4.3
Volume Function
313
7.4.4
Barycenter Plot
321
7.4.5
Level Set Trees in Regression Function Estimation
322
7.5
Unimodal Densities
325
7.5.1
Probability Content of Level Sets
327
7.5.2
Set Visualization
327
Appendix A: R Tutorial
329
A.1 Data Visualization
329
A.1.1 QQ Plots
329
A.
1.2
Tail Plots
330
A.
1.3
Two-Dimensional Scatter Plots
330
A.
1.4
Three-Dimensional Scatter Plots
331
A.2 Linear Regression
331
A.3 Kernel Regression
332
A.
3.1
One-Dimensional Kernel Regression
332
A.
3.2
Moving Averages
333
A.
3.3
Two-Dimensional Kernel Regression
334
A.
3.4
Three-and Higher-Dimensional Kernel Regression
336
A.3.
5
Kernel Estimator of Derivatives
338
A.
3.6
Combined State-and Time-Space Smoothing
340
A.4 Local Linear Regression
341
A.4.1
One-Dimensional Local
Linear Regression 341
Α.
4.2 Two-Dimensional
Local
Linear Regression 342
A.4.3
Three- and Higher-Dimensional Local
Linear
Regression 343
A.4.4
Local
Linear Derivative
Estimation
343
A.5 Additive Models: Backfitting 344
A.6 Single-Index Regression 345
A.6.1
Estimating the
Index 346
A.6.2
Estimating the Link Function
346
Α.
6.3
Plotting the Single-Index Regression Function
346
A.7 Forward Stagewise Modeling
347
АЛ.
1
Stagewise Fitting of Additive Models
347
A.I
.2
Projection Pursuit Regression
348
A.
8
Quantité
Regression
349
A.8.1 Linear Quantile Regression
349
A.
8.2
Kernel Quantile Regression
349
References
351
Author Index
361
Topic Index
365
|
| any_adam_object | 1 |
| author_GND | (DE-588)171774884 |
| building | Verbundindex |
| bvnumber | BV042152657 |
| callnumber-first | H - Social Science |
| callnumber-label | HG176 |
| callnumber-raw | HG176.5 |
| callnumber-search | HG176.5 |
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| callnumber-subject | HG - Finance |
| classification_rvk | QH 500 |
| ctrlnum | (OCoLC)891517581 (DE-599)BVBBV042152657 |
| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Map |
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Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site"..</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MATHEMATICS / Probability & Statistics / General</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MATHEMATICS / Probability & Statistics / Regression Analysis</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Programming Languages / Visual BASIC.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematisches Modell</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Finance</subfield><subfield code="x">Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visualization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MATHEMATICS / Probability & Statistics / General</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MATHEMATICS / Probability & Statistics / Regression Analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS / Programming Languages / Visual BASIC.</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Visualisierung</subfield><subfield code="0">(DE-588)4188417-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Regressionsanalyse</subfield><subfield code="0">(DE-588)4129903-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Nichtparametrische Schätzung</subfield><subfield code="0">(DE-588)4203980-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Regressionsanalyse</subfield><subfield code="0">(DE-588)4129903-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Nichtparametrische Schätzung</subfield><subfield code="0">(DE-588)4203980-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Visualisierung</subfield><subfield code="0">(DE-588)4188417-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Klemelä, Jussi</subfield><subfield code="e">Sonstige</subfield><subfield code="0">(DE-588)171774884</subfield><subfield code="4">oth</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Wiley series in computational statistics</subfield><subfield code="v">699</subfield><subfield code="w">(DE-604)BV041458063</subfield><subfield code="9">699</subfield></datafield><datafield tag="856" ind1="4" ind2=" "><subfield code="u">http://catalogimages.wiley.com/images/db/jimages/9780470384428.jpg</subfield><subfield code="3">Cover image</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027592458&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027592458</subfield></datafield></record></collection> |
| id | DE-604.BV042152657 |
| illustrated | Illustrated |
| indexdate | 2024-11-07T04:01:16Z |
| institution | BVB |
| isbn | 9780470384428 |
| language | English |
| lccn | 013042095 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027592458 |
| oclc_num | 891517581 |
| open_access_boolean | |
| owner | DE-739 DE-862 DE-BY-FWS |
| owner_facet | DE-739 DE-862 DE-BY-FWS |
| physical | XXIII, 367 S. Ill., graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Wiley |
| record_format | marc |
| series | Wiley series in computational statistics |
| series2 | Wiley series in computational statistics |
| spellingShingle | Multivariate nonparametric regression and visualization with R and applications to finance Wiley series in computational statistics MATHEMATICS / Probability & Statistics / General bisacsh MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh COMPUTERS / Programming Languages / Visual BASIC. bisacsh Mathematisches Modell Finance Mathematical models Visualization Regression analysis MATHEMATICS / Probability & Statistics / General MATHEMATICS / Probability & Statistics / Regression Analysis COMPUTERS / Programming Languages / Visual BASIC. Visualisierung (DE-588)4188417-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd Nichtparametrische Schätzung (DE-588)4203980-0 gnd |
| subject_GND | (DE-588)4188417-6 (DE-588)4129903-6 (DE-588)4203980-0 |
| title | Multivariate nonparametric regression and visualization with R and applications to finance |
| title_auth | Multivariate nonparametric regression and visualization with R and applications to finance |
| title_exact_search | Multivariate nonparametric regression and visualization with R and applications to finance |
| title_full | Multivariate nonparametric regression and visualization with R and applications to finance Jussi Klemelä |
| title_fullStr | Multivariate nonparametric regression and visualization with R and applications to finance Jussi Klemelä |
| title_full_unstemmed | Multivariate nonparametric regression and visualization with R and applications to finance Jussi Klemelä |
| title_short | Multivariate nonparametric regression and visualization |
| title_sort | multivariate nonparametric regression and visualization with r and applications to finance |
| title_sub | with R and applications to finance |
| topic | MATHEMATICS / Probability & Statistics / General bisacsh MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh COMPUTERS / Programming Languages / Visual BASIC. bisacsh Mathematisches Modell Finance Mathematical models Visualization Regression analysis MATHEMATICS / Probability & Statistics / General MATHEMATICS / Probability & Statistics / Regression Analysis COMPUTERS / Programming Languages / Visual BASIC. Visualisierung (DE-588)4188417-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd Nichtparametrische Schätzung (DE-588)4203980-0 gnd |
| topic_facet | MATHEMATICS / Probability & Statistics / General MATHEMATICS / Probability & Statistics / Regression Analysis COMPUTERS / Programming Languages / Visual BASIC. Mathematisches Modell Finance Mathematical models Visualization Regression analysis Visualisierung Regressionsanalyse Nichtparametrische Schätzung |
| url | http://catalogimages.wiley.com/images/db/jimages/9780470384428.jpg http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027592458&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV041458063 |
| work_keys_str_mv | AT klemelajussi multivariatenonparametricregressionandvisualizationwithrandapplicationstofinance |
Inhaltsverzeichnis
Sonderstandort Fakultät
| Signatur: |
2000 QH 500 K64 |
|---|---|
| Exemplar 1 | nicht ausleihbar Checked out – Rückgabe bis: 31.12.2099 Vormerken |