Statistical computing with R:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
Chapman & Hall/CRC
2008
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| Schriftenreihe: | Computer science and data analysis series
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 399 S. graph. Darst. |
| ISBN: | 1584885459 9781584885450 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
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| 008 | 071017s2008 xxud||| |||| 00||| eng d | ||
| 010 | |a 2007034218 | ||
| 020 | |a 1584885459 |c alk. paper |9 1-58488-545-9 | ||
| 020 | |a 9781584885450 |9 978-1-58488-545-0 | ||
| 035 | |a (OCoLC)165958428 | ||
| 035 | |a (DE-599)BVBBV022886810 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c US | ||
| 049 | |a DE-N2 |a DE-703 |a DE-945 |a DE-91G |a DE-473 |a DE-20 |a DE-19 |a DE-739 |a DE-11 |a DE-384 |a DE-188 |a DE-578 | ||
| 050 | 0 | |a QA276.45.R3 | |
| 082 | 0 | |a 519.50285/5133 | |
| 084 | |a QH 231 |0 (DE-625)141546: |2 rvk | ||
| 084 | |a ST 250 |0 (DE-625)143626: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 084 | |a DAT 368f |2 stub | ||
| 084 | |a MAT 620f |2 stub | ||
| 100 | 1 | |a Rizzo, Maria L. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Statistical computing with R |c Maria L. Rizzo |
| 264 | 1 | |a Boca Raton [u.a.] |b Chapman & Hall/CRC |c 2008 | |
| 300 | |a XVI, 399 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Computer science and data analysis series | |
| 650 | 7 | |a Statistics |2 cabt | |
| 650 | 7 | |a Computers |2 cabt | |
| 650 | 7 | |a Software |2 cabt | |
| 650 | 7 | |a R |2 cabt | |
| 650 | 7 | |a Dataprocessing |2 gtt | |
| 650 | 4 | |a Estadística matemática | |
| 650 | 7 | |a Programmeertalen |2 gtt | |
| 650 | 7 | |a R (computerprogramma) |2 gtt | |
| 650 | 7 | |a Statistiek |2 gtt | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Mathematical statistics |x Data processing | |
| 650 | 4 | |a Statistics |x Data processing | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistische Analyse |0 (DE-588)4116599-8 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Statistische Analyse |0 (DE-588)4116599-8 |D s |
| 689 | 0 | 1 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016091710&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016091710 | ||
Datensatz im Suchindex
| _version_ | 1804137153137999872 |
|---|---|
| adam_text | Contents
Preface
xv
1
Introduction
1
1.1
Computational Statistics and Statistical Computing
..... 1
1.2
The
R
Environment
....................... 3
1.3
Getting Started with
R
..................... 4
1.4
Using the
R
Online Help System
................ 7
1.5
Functions
............................. 8
1.6
Arrays, Data Frames, and Lists
................ 9
1.7
Workspace and Files
....................... 15
1.8
Using Scripts
........................... 17
1.9
Using Packages
.......................... 18
1.10
Graphics
............................. 19
2
Probability and Statistics Review
21
2.1
Random Variables and Probability
............... 21
2.2
Some Discrete Distributions
.................. 25
2.3
Some Continuous Distributions
................. 29
2.4
Multivariate Normal Distribution
............... 33
2.5
Limit Theorems
......................... 35
2.6
Statistics
............................. 35
2.7
Bayes
Theorem and Bayesian Statistics
............ 40
2.8
Markov Chains
......................... 42
3
Methods for Generating Random Variables
47
3.1
Introduction
........................... 47
3.2
The Inverse Transform Method
................. 49
3.3
The Acceptance-Rejection Method
............... 55
3.4
Transformation Methods
.................... 58
3.5
Sums and Mixtures
....................... 61
3.6
Multivariate Distributions
................... 69
3.7
Stochastic Processes
....................... 82
Exercises
................................ 94
Visualization of Multivariate Data
97
4.1
Introduction
........................... 97
4.2
Panel Displays
.......................... 97
4.3
Surface Plots and
3D
Scatter Plots
.............. 100
4.4
Contour Plots
.......................... 106
4.5
Other 2D Representations of Data
............... 110
4.6
Other Approaches to Data Visualization
........... 115
Exercises
................................ 116
Monte Carlo Integration and Variance Reduction
119
5.1
Introduction
........................... 119
5.2
Monte Carlo Integration
.................... 119
5.3
Variance Reduction
....................... 126
5.4
Antithetic Variables
....................... 128
5.5
Control
Variâtes
......................... 132
5.6
Importance Sampling
...................... 139
5.7
Stratified Sampling
....................... 144
5.8
Stratified Importance Sampling
................ 147
Exercises
................................ 149
R
Code
................................. 152
Monte Carlo Methods in Inference
153
6.1
Introduction
........................... 153
6.2
Monte Carlo Methods for Estimation
............. 154
6.3
Monte Carlo Methods for Hypothesis Tests
.......... 162
6.4
Application
............................ 174
Exercises
................................ 180
Bootstrap and Jackknife
183
7.1
The Bootstrap
.......................... 183
7.2
The Jackknife
.......................... 190
7.3
Jackknife-after-Bootstrap
.................... 195
7.4
Bootstrap Confidence Intervals
................. 197
7.5
Better Bootstrap Confidence Intervals
............. 203
7.6
Application
............................ 207
Exercises
................................ 212
Permutation Tests
215
8.1
Introduction
........................... 215
8.2
Tests for Equal Distributions
.................. 219
8.3
Multivariate Tests for Equal Distributions
.......... 222
8.4
Application
............................ 235
Exercises
................................ 242
9
Markov Chain Monte Carlo Methods
245
9.1
Introduction
........................... 245
9.2
The Metropolis-Hastings Algorithm
.............. 247
9.3
The Gibbs Sampler
....................... 263
9.4
Monitoring Convergence
.................... 266
9.5
Application
............................ 271
Exercises
................................ 277
R
Code
................................. 279
10
Probability Density Estimation
281
10.1
Univariate Density Estimation
................. 281
10.2
Kernel Density Estimation
................... 296
10.3
Bivariate and Multivariate Density Estimation
........ 305
10.4
Other Methods of Density Estimation
............. 314
Exercises
................................ 314
R
Code
................................. 317
11
Numerical Methods in
R
319
11.1
Introduction
........................... 319
11.2
Root-finding in One Dimension
................ 326
11.3
Numerical Integration
...................... 330
11.4
Maximum Likelihood Problems
................. 335
11.5
One-dimensional Optimization
................. 338
11.6
Two-dimensional Optimization
................. 342
11.7
The EM Algorithm
....................... 345
11.8
Linear Programming
-
The Simplex Method
......... 348
11.9
Application
............................ 349
Exercises
................................ 353
A Notation
355
В
Working with Data Frames and Arrays
357
B.I Resampling and Data Partitioning
............... 357
B.2 Subsetting and Reshaping Data
................ 360
B.3 Data Entry and Data Analysis
................. 364
References
375
Index
395
List of Tables
1.1
R
Syntax and Commonly Used Operators
........... 5
1.2
Commonly Used Functions
................... 6
1.3
R
Syntax and Functions for Vectors and Matrices
....... 6
1.4
Some Basic Graphics Functions in
R
(graphics) and Other
Packages
.............................. 19
3.1
Selected Univariate Probability Functions
........... 49
4.1
Graphics Functions for Multivariate Data in
R
(graphics) and
Other Packages
.......................... 98
6.1
Estimates of Mean Squared Error for the kth Level Trimmed
Mean in Example
6.3....................... 158
6.2
Empirical Power of Three Tests of Normality against a Con¬
taminated Normal Alternative in Example
6.11........ 175
8.1
Significant Tests of
Divariate
Normal Location Alternatives
Fi
=N2((0,0)T,I2),
F2 = N2((0,ő)T,I2)
............ 235
8.2
Power of dCov Test of Independence in Example
8.14 .... 242
9.1
Quantiles of Target Distribution and Chains in Example
9.4 . 256
10.1
Estimated Best Number of Class Intervals for Simulated Data
According to Three Rules for Histograms
........... 289
10.2
Kernel Functions for Density Estimation
............ 299
11.1
Payoff Matrix of the Game of
Morra
.............. 350
їх
List of Figures
3.1
Probability density histogram of a random sample generated
by the inverse transform method in Example
3.2....... 51
3.2
QQ Plot comparing the Beta(3,
2)
distribution with a simu¬
lated random sample in Example
3.8............. 60
3.3
Histogram of a simulated convolution of Gamma(2,
2)
and
Gamma(2,
4)
random variables, and a
50%
mixture of the
same variables, from Example
3.11 .............. 65
3.4
Density estimates from Example
3.12:
A mixture (thick line)
of several gamma densities (thin lines)
............ 66
3.5
Densities from Example
3.14:
A mixture (thick line) of several
gamma densities (thin lines)
.................. 69
3.6
Scatterplot of a bivariate normal sample in Example
3.16 .. 73
3.7
Pairs plot of the bivariate marginal distributions of a simu¬
lated multivariate normal random sample in Example
3.18 . 75
3.8
Histograms of the marginal distributions of multivariate nor¬
mal location mixture data generated in Example
3.20 .... 80
3.9
A random sample of
200
points from the bivariate distribution
(Хь-Хг)
that is uniformly distributed on the unit circle in
Example
3.21 .......................... 82
3.10
Sequence of sample means of a simulated renewal process in
Example
3.25 .......................... 90
3.11
Partial realization of a symmetric random walk in Example
3.26................................ 91
4.1
Scatterplot matrix (pairs) comparing four measurements of
iris virginica species in Example
4.1.............. 99
4.2
Scatterplot matrix (splom) comparing four measurements of
iris data in Example
4.1 .................... 100
4.3
Perspective plot of the standard bivariate normal density in
Example
4.2........................... 102
4.4
Perspective plot with elements added using the viewing trans¬
formation returned by persp in Example
4.3......... 103
4.5 3D
scatterplots of iris data produced by cloud (lattice) in
Example
4.5........................... 106
4.6
Contour and levelplot of volcano data in Examples
4.6-4.7 . 108
xi
4.7
Flat density histogram of bivariate normal data with hexago¬
nal bins produced by hexbin in Example
4.8......... 109
4.8
Andrews curves for leaf shape
17
(DAAG)
data at latitude
17.1
in Example
4.9 ......................... 112
4.9
Parallel coordinate plots in Example
4.10
for a subset of the
crabs (MASS) data
....................... 114
4.10
Segment plot of a subset of the males in the crabs (MASS)
data set in Example
4.11.................... 115
5.1
Importance functions in Example
5.10 ............ 142
6.1
Empirical power
π(θ)
±
se
(π
(6»))
for the
í-
test of Ho
:
θ
= 500
vs Hi
:
θ
> 500
in Example
6.9 ................ 170
6.2
Empirical power
π(ε) ±
śe(n(e))
for the skewness test of nor¬
mality against
ε
-contaminated normal scale mixture alterna¬
tive in Example
6.10 ...................... 171
6.3
Empirical power of three tests of normality in Example
6.11 175
6.4
Boxplots showing extreme points for the Count Five statistic
in Example
6.12......................... 176
7.1
Bootstrap replicates for law school data in Example
7.2 . . . 188
7.2
Four proposed models for ironslag data in Example
7.17 . 209
7.3
Residuals of the quadratic model in Example
7.17...... 211
8.1
Permutation distribution of replicates in Example
8.1
(left)
and Example
8.2
(right)
.................... 219
8.2
Permutation distribution of
Tn¡3
in Example
8.6....... 229
8.3
Permutation distribution of
e in
Example
8.7......... 234
8.4
Permutation distribution of dCov in Example
8.13...... 241
8.5
Power comparison of dCov and
W
in Example
8.14..... 242
9.1
Part of a chain generated by a Metropolis-Hastings sampler
of a Rayleigh distribution in Example
9.1........... 251
9.2
Histogram with target Rayleigh density and QQ plot for a
Metropolis-Hastings chain in Example
9.1 .......... 252
9.3
Random walk Metropolis chains generated by proposal distri¬
butions with different variances in Example
9.3 ....... 255
9.4
Random walk Metropolis chain for
β
in Example
9.5 .... 259
9.5
Distribution of the independence sampler chain for
ρ
with pro¬
posal distribution Beta(l,
1)
in Example
9.6......... 262
9.6
Chains generated by independence sampler for
ρ
with proposal
distributions Beta(l,
1)
and Beta(5,
2)
in Example
9.6 ... 262
9.7
Bivariate normal chain generated by the Gibbs sampler in
Example
9.7........................... 265
хш
9.8
Sequences of the running means
φ
for four Metropolis-Hastings
chains in Example
9.8...................... 270
9.9
Sequence of the Gelman-Rubin
R
for four Metropolis-Hastings
chains in Example
9.8
(a)
σ
= 0.2,
(b)
σ
= 2......... 271
9.10
Number of annual coal mining disasters in Example
9.9 . . . 272
9.11
Output of the Gibbs sampler in Example
9.9......... 276
9.12
Distribution of
μ, λ,
and
к
from the change point analysis for
coal mining disasters in Example
9.9............. 276
10.1
Histogram estimates of normal density in Example
10.1
for
samples of size (a)
25
and (b)
1000
with standard normal
density curve
........................... 285
10.2
Histogram estimate of Old Faithful waiting time density in
Example
10.3 .......................... 289
10.3
Frequency polygon estimate of Old Faithful waiting time den¬
sity in Example
10.4 ...................... 292
10.4
Histogram estimates of a normal sample with equal bin width
but different bin origins, and standard normal density curve
294
10.5
ASH density estimate of Old Faithful waiting times in Exam¬
ple
10.6.............................. 296
10.6
Kernel density estimates using a Gaussian kernel with band¬
width
h
.............................. 298
10.7
Kernel functions for density estimation
............ 299
10.8
Gaussian kernel density estimates of Old Faithful waiting time
in Example
10.7
using density with different bandwidths
. 301
10.9
Kernel density estimates of precipitation data in Example
10.8
using density with different bandwidths
........... 302
10.10
Reflection boundary technique in Example
10.10....... 304
10.11
Density polygon of bivariate normal data in Example
10.13,
using normal reference rule (Sturges Rule) to determine bin
widths
.............................. 308
10.12
Bivariate ASH density estimates of bivariate normal data in
Example
10.14.......................... 311
10.13
Product kernel estimates of normal mixture in Example
10.15 313
11.1
Example
11.8
(n
= 10,
r
= 0.5,
p =
0.2)
(a) Integrand,
(b) Value of the integral as a function of
p
.......... 332
11.2
Density of the correlation statistic for sample size
10 .... 335
11.3
The function f(x) in Example
11.11.............. 339
11.4
Replicates of maximum likelihood estimates by numerical op¬
timization of the likelihood of a Gamma(r
= 5,
λ
= 2)
random
variable in Example
11.12 ................... 341
|
| adam_txt |
Contents
Preface
xv
1
Introduction
1
1.1
Computational Statistics and Statistical Computing
. 1
1.2
The
R
Environment
. 3
1.3
Getting Started with
R
. 4
1.4
Using the
R
Online Help System
. 7
1.5
Functions
. 8
1.6
Arrays, Data Frames, and Lists
. 9
1.7
Workspace and Files
. 15
1.8
Using Scripts
. 17
1.9
Using Packages
. 18
1.10
Graphics
. 19
2
Probability and Statistics Review
21
2.1
Random Variables and Probability
. 21
2.2
Some Discrete Distributions
. 25
2.3
Some Continuous Distributions
. 29
2.4
Multivariate Normal Distribution
. 33
2.5
Limit Theorems
. 35
2.6
Statistics
. 35
2.7
Bayes'
Theorem and Bayesian Statistics
. 40
2.8
Markov Chains
. 42
3
Methods for Generating Random Variables
47
3.1
Introduction
. 47
3.2
The Inverse Transform Method
. 49
3.3
The Acceptance-Rejection Method
. 55
3.4
Transformation Methods
. 58
3.5
Sums and Mixtures
. 61
3.6
Multivariate Distributions
. 69
3.7
Stochastic Processes
. 82
Exercises
. 94
Visualization of Multivariate Data
97
4.1
Introduction
. 97
4.2
Panel Displays
. 97
4.3
Surface Plots and
3D
Scatter Plots
. 100
4.4
Contour Plots
. 106
4.5
Other 2D Representations of Data
. 110
4.6
Other Approaches to Data Visualization
. 115
Exercises
. 116
Monte Carlo Integration and Variance Reduction
119
5.1
Introduction
. 119
5.2
Monte Carlo Integration
. 119
5.3
Variance Reduction
. 126
5.4
Antithetic Variables
. 128
5.5
Control
Variâtes
. 132
5.6
Importance Sampling
. 139
5.7
Stratified Sampling
. 144
5.8
Stratified Importance Sampling
. 147
Exercises
. 149
R
Code
. 152
Monte Carlo Methods in Inference
153
6.1
Introduction
. 153
6.2
Monte Carlo Methods for Estimation
. 154
6.3
Monte Carlo Methods for Hypothesis Tests
. 162
6.4
Application
. 174
Exercises
. 180
Bootstrap and Jackknife
183
7.1
The Bootstrap
. 183
7.2
The Jackknife
. 190
7.3
Jackknife-after-Bootstrap
. 195
7.4
Bootstrap Confidence Intervals
. 197
7.5
Better Bootstrap Confidence Intervals
. 203
7.6
Application
. 207
Exercises
. 212
Permutation Tests
215
8.1
Introduction
. 215
8.2
Tests for Equal Distributions
. 219
8.3
Multivariate Tests for Equal Distributions
. 222
8.4
Application
. 235
Exercises
. 242
9
Markov Chain Monte Carlo Methods
245
9.1
Introduction
. 245
9.2
The Metropolis-Hastings Algorithm
. 247
9.3
The Gibbs Sampler
. 263
9.4
Monitoring Convergence
. 266
9.5
Application
. 271
Exercises
. 277
R
Code
. 279
10
Probability Density Estimation
281
10.1
Univariate Density Estimation
. 281
10.2
Kernel Density Estimation
. 296
10.3
Bivariate and Multivariate Density Estimation
. 305
10.4
Other Methods of Density Estimation
. 314
Exercises
. 314
R
Code
. 317
11
Numerical Methods in
R
319
11.1
Introduction
. 319
11.2
Root-finding in One Dimension
. 326
11.3
Numerical Integration
. 330
11.4
Maximum Likelihood Problems
. 335
11.5
One-dimensional Optimization
. 338
11.6
Two-dimensional Optimization
. 342
11.7
The EM Algorithm
. 345
11.8
Linear Programming
-
The Simplex Method
. 348
11.9
Application
. 349
Exercises
. 353
A Notation
355
В
Working with Data Frames and Arrays
357
B.I Resampling and Data Partitioning
. 357
B.2 Subsetting and Reshaping Data
. 360
B.3 Data Entry and Data Analysis
. 364
References
375
Index
395
List of Tables
1.1
R
Syntax and Commonly Used Operators
. 5
1.2
Commonly Used Functions
. 6
1.3
R
Syntax and Functions for Vectors and Matrices
. 6
1.4
Some Basic Graphics Functions in
R
(graphics) and Other
Packages
. 19
3.1
Selected Univariate Probability Functions
. 49
4.1
Graphics Functions for Multivariate Data in
R
(graphics) and
Other Packages
. 98
6.1
Estimates of Mean Squared Error for the kth Level Trimmed
Mean in Example
6.3. 158
6.2
Empirical Power of Three Tests of Normality against a Con¬
taminated Normal Alternative in Example
6.11. 175
8.1
Significant Tests of
Divariate
Normal Location Alternatives
Fi
=N2((0,0)T,I2),
F2 = N2((0,ő)T,I2)
. 235
8.2
Power of dCov Test of Independence in Example
8.14 . 242
9.1
Quantiles of Target Distribution and Chains in Example
9.4 . 256
10.1
Estimated Best Number of Class Intervals for Simulated Data
According to Three Rules for Histograms
. 289
10.2
Kernel Functions for Density Estimation
. 299
11.1
Payoff Matrix of the Game of
Morra
. 350
їх
List of Figures
3.1
Probability density histogram of a random sample generated
by the inverse transform method in Example
3.2. 51
3.2
QQ Plot comparing the Beta(3,
2)
distribution with a simu¬
lated random sample in Example
3.8. 60
3.3
Histogram of a simulated convolution of Gamma(2,
2)
and
Gamma(2,
4)
random variables, and a
50%
mixture of the
same variables, from Example
3.11 . 65
3.4
Density estimates from Example
3.12:
A mixture (thick line)
of several gamma densities (thin lines)
. 66
3.5
Densities from Example
3.14:
A mixture (thick line) of several
gamma densities (thin lines)
. 69
3.6
Scatterplot of a bivariate normal sample in Example
3.16 . 73
3.7
Pairs plot of the bivariate marginal distributions of a simu¬
lated multivariate normal random sample in Example
3.18 . 75
3.8
Histograms of the marginal distributions of multivariate nor¬
mal location mixture data generated in Example
3.20 . 80
3.9
A random sample of
200
points from the bivariate distribution
(Хь-Хг)
that is uniformly distributed on the unit circle in
Example
3.21 . 82
3.10
Sequence of sample means of a simulated renewal process in
Example
3.25 . 90
3.11
Partial realization of a symmetric random walk in Example
3.26. 91
4.1
Scatterplot matrix (pairs) comparing four measurements of
iris virginica species in Example
4.1. 99
4.2
Scatterplot matrix (splom) comparing four measurements of
iris data in Example
4.1 . 100
4.3
Perspective plot of the standard bivariate normal density in
Example
4.2. 102
4.4
Perspective plot with elements added using the viewing trans¬
formation returned by persp in Example
4.3. 103
4.5 3D
scatterplots of iris data produced by cloud (lattice) in
Example
4.5. 106
4.6
Contour and levelplot of volcano data in Examples
4.6-4.7 . 108
xi
4.7
Flat density histogram of bivariate normal data with hexago¬
nal bins produced by hexbin in Example
4.8. 109
4.8
Andrews curves for leaf shape
17
(DAAG)
data at latitude
17.1
in Example
4.9 . 112
4.9
Parallel coordinate plots in Example
4.10
for a subset of the
crabs (MASS) data
. 114
4.10
Segment plot of a subset of the males in the crabs (MASS)
data set in Example
4.11. 115
5.1
Importance functions in Example
5.10 . 142
6.1
Empirical power
π(θ)
±
se
(π
(6»))
for the
í-
test of Ho
:
θ
= 500
vs Hi
:
θ
> 500
in Example
6.9 . 170
6.2
Empirical power
π(ε) ±
śe(n(e))
for the skewness test of nor¬
mality against
ε
-contaminated normal scale mixture alterna¬
tive in Example
6.10 . 171
6.3
Empirical power of three tests of normality in Example
6.11 175
6.4
Boxplots showing extreme points for the Count Five statistic
in Example
6.12. 176
7.1
Bootstrap replicates for law school data in Example
7.2 . . . 188
7.2
Four proposed models for ironslag data in Example
7.17 . 209
7.3
Residuals of the quadratic model in Example
7.17. 211
8.1
Permutation distribution of replicates in Example
8.1
(left)
and Example
8.2
(right)
. 219
8.2
Permutation distribution of
Tn¡3
in Example
8.6. 229
8.3
Permutation distribution of
e in
Example
8.7. 234
8.4
Permutation distribution of dCov in Example
8.13. 241
8.5
Power comparison of dCov and
W
in Example
8.14. 242
9.1
Part of a chain generated by a Metropolis-Hastings sampler
of a Rayleigh distribution in Example
9.1. 251
9.2
Histogram with target Rayleigh density and QQ plot for a
Metropolis-Hastings chain in Example
9.1 . 252
9.3
Random walk Metropolis chains generated by proposal distri¬
butions with different variances in Example
9.3 . 255
9.4
Random walk Metropolis chain for
β
in Example
9.5 . 259
9.5
Distribution of the independence sampler chain for
ρ
with pro¬
posal distribution Beta(l,
1)
in Example
9.6. 262
9.6
Chains generated by independence sampler for
ρ
with proposal
distributions Beta(l,
1)
and Beta(5,
2)
in Example
9.6 . 262
9.7
Bivariate normal chain generated by the Gibbs sampler in
Example
9.7. 265
хш
9.8
Sequences of the running means
φ
for four Metropolis-Hastings
chains in Example
9.8. 270
9.9
Sequence of the Gelman-Rubin
R
for four Metropolis-Hastings
chains in Example
9.8
(a)
σ
= 0.2,
(b)
σ
= 2. 271
9.10
Number of annual coal mining disasters in Example
9.9 . . . 272
9.11
Output of the Gibbs sampler in Example
9.9. 276
9.12
Distribution of
μ, λ,
and
к
from the change point analysis for
coal mining disasters in Example
9.9. 276
10.1
Histogram estimates of normal density in Example
10.1
for
samples of size (a)
25
and (b)
1000
with standard normal
density curve
. 285
10.2
Histogram estimate of Old Faithful waiting time density in
Example
10.3 . 289
10.3
Frequency polygon estimate of Old Faithful waiting time den¬
sity in Example
10.4 . 292
10.4
Histogram estimates of a normal sample with equal bin width
but different bin origins, and standard normal density curve
294
10.5
ASH density estimate of Old Faithful waiting times in Exam¬
ple
10.6. 296
10.6
Kernel density estimates using a Gaussian kernel with band¬
width
h
. 298
10.7
Kernel functions for density estimation
. 299
10.8
Gaussian kernel density estimates of Old Faithful waiting time
in Example
10.7
using density with different bandwidths
. 301
10.9
Kernel density estimates of precipitation data in Example
10.8
using density with different bandwidths
. 302
10.10
Reflection boundary technique in Example
10.10. 304
10.11
Density polygon of bivariate normal data in Example
10.13,
using normal reference rule (Sturges' Rule) to determine bin
widths
. 308
10.12
Bivariate ASH density estimates of bivariate normal data in
Example
10.14. 311
10.13
Product kernel estimates of normal mixture in Example
10.15 313
11.1
Example
11.8
(n
= 10,
r
= 0.5,
p =
0.2)
(a) Integrand,
(b) Value of the integral as a function of
p
. 332
11.2
Density of the correlation statistic for sample size
10 . 335
11.3
The function f(x) in Example
11.11. 339
11.4
Replicates of maximum likelihood estimates by numerical op¬
timization of the likelihood of a Gamma(r
= 5,
λ
= 2)
random
variable in Example
11.12 . 341 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Rizzo, Maria L. |
| author_facet | Rizzo, Maria L. |
| author_role | aut |
| author_sort | Rizzo, Maria L. |
| author_variant | m l r ml mlr |
| building | Verbundindex |
| bvnumber | BV022886810 |
| callnumber-first | Q - Science |
| callnumber-label | QA276 |
| callnumber-raw | QA276.45.R3 |
| callnumber-search | QA276.45.R3 |
| callnumber-sort | QA 3276.45 R3 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 231 ST 250 ST 601 |
| classification_tum | DAT 368f MAT 620f |
| ctrlnum | (OCoLC)165958428 (DE-599)BVBBV022886810 |
| dewey-full | 519.50285/5133 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.50285/5133 |
| dewey-search | 519.50285/5133 |
| dewey-sort | 3519.50285 45133 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV022886810 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:52:10Z |
| indexdate | 2024-07-09T21:07:46Z |
| institution | BVB |
| isbn | 1584885459 9781584885450 |
| language | English |
| lccn | 2007034218 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016091710 |
| oclc_num | 165958428 |
| open_access_boolean | |
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| physical | XVI, 399 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series2 | Computer science and data analysis series |
| spelling | Rizzo, Maria L. Verfasser aut Statistical computing with R Maria L. Rizzo Boca Raton [u.a.] Chapman & Hall/CRC 2008 XVI, 399 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computer science and data analysis series Statistics cabt Computers cabt Software cabt R cabt Dataprocessing gtt Estadística matemática Programmeertalen gtt R (computerprogramma) gtt Statistiek gtt Datenverarbeitung Statistik Mathematical statistics Data processing Statistics Data processing R (Computer program language) R Programm (DE-588)4705956-4 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 s R Programm (DE-588)4705956-4 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016091710&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rizzo, Maria L. Statistical computing with R Statistics cabt Computers cabt Software cabt R cabt Dataprocessing gtt Estadística matemática Programmeertalen gtt R (computerprogramma) gtt Statistiek gtt Datenverarbeitung Statistik Mathematical statistics Data processing Statistics Data processing R (Computer program language) R Programm (DE-588)4705956-4 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4116599-8 |
| title | Statistical computing with R |
| title_auth | Statistical computing with R |
| title_exact_search | Statistical computing with R |
| title_exact_search_txtP | Statistical computing with R |
| title_full | Statistical computing with R Maria L. Rizzo |
| title_fullStr | Statistical computing with R Maria L. Rizzo |
| title_full_unstemmed | Statistical computing with R Maria L. Rizzo |
| title_short | Statistical computing with R |
| title_sort | statistical computing with r |
| topic | Statistics cabt Computers cabt Software cabt R cabt Dataprocessing gtt Estadística matemática Programmeertalen gtt R (computerprogramma) gtt Statistiek gtt Datenverarbeitung Statistik Mathematical statistics Data processing Statistics Data processing R (Computer program language) R Programm (DE-588)4705956-4 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| topic_facet | Statistics Computers Software R Dataprocessing Estadística matemática Programmeertalen R (computerprogramma) Statistiek Datenverarbeitung Statistik Mathematical statistics Data processing Statistics Data processing R (Computer program language) R Programm Statistische Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016091710&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rizzomarial statisticalcomputingwithr |