Statistics and data analysis for financial engineering: with R examples
Gespeichert in:
| Beteiligte Personen: | , |
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| Format: | Buch |
| Sprache: | Englisch |
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Springer
2015
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| Ausgabe: | Second edition |
| Schriftenreihe: | Springer texts in statistics
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| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028128474&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xxvi, 719 Seiten Diagramme |
| ISBN: | 9781493926138 9781493951734 |
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| 100 | 1 | |a Ruppert, David |d 1948- |e Verfasser |0 (DE-588)133112209 |4 aut | |
| 245 | 1 | 0 | |a Statistics and data analysis for financial engineering |b with R examples |c David Ruppert ; David S. Matteson |
| 250 | |a Second edition | ||
| 264 | 1 | |a New York ; Heidelberg ; Dordrecht ; London |b Springer |c 2015 | |
| 300 | |a xxvi, 719 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Springer texts in statistics | |
| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Financial Engineering |0 (DE-588)4208404-0 |2 gnd |9 rswk-swf |
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| 689 | 0 | 2 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
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| 700 | 1 | |a Matteson, David S. |e Verfasser |0 (DE-588)1071738224 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-4939-2614-5 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028128474&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
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Contents
Notation.XXV
1 Introduction. 1
1.1 Bibliographie Notes. 4
References. 4
2 Returns. 5
2.1 Introduction. 5
2.1.1 Net Returns. 5
2.1.2 Gross Returns. 6
2.1.3 Log Returns. 6
2.1.4 Adjustment for Dividends . 7
2.2 The Random Walk Model. 8
2.2.1 Random Walks. 8
2.2.2 Geometric Random Walks. 9
2.2.3 Are Log Prices a Lognormal Geometric Random
Walk?. 9
2.3 Bibliographic Notes. 10
2.4 R Lab. 11
2.4.1 Data Analysis. 11
2.4.2 Simulations. 13
2.4.3 Simulating a Geometric Random Walk. 14
2.4.4 Let’s Look at McDonald’s Stock. 15
2.5 Exercises. 16
References. 18
3 Fixed Income Securities. 19
3.1 Introduction. 19
3.2 Zero-Coupon Bonds . 20
3.2.1 Price and Returns Fluctuate with the Interest Rate . 20
xi
xii
Contents
3.3 Coupon Bonds. 22
3.3.1 A General Formula. 23
3.4 Yield to Maturity. 23
3.4.1 General Method for Yield to Maturity. 25
3.4.2 Spot Rates . 25
3.5 Term Structure . 26
3.5.1 Introduction: Interest Rates Depend Upon Maturity . . 26
3.5.2 Describing the Term Structure. 27
3.6 Continuous Compounding. 32
3.7 Continuous Forward Rates. 33
3.8 Sensitivity of Price to Yield. 35
3.8.1 Duration of a Coupon Bond . 35
3.9 Bibliographic Notes. 36
3.10 R Lab. 37
3.10.1 Computing Yield to Maturity. 37
3.10.2 Graphing Yield Curves. 38
3.11 Exercises. 40
References. 43
4 Exploratory Data Analysis. 45
4.1 Introduction. 45
4.2 Histograms and Kernel Density Estimation. 47
4.3 Order Statistics, the Sample CDF, and Sample Quantiles. 52
4.3.1 The Central Limit Theorem for Sample Quantiles. 54
4.3.2 Normal Probability Plots. 54
4.3.3 Half-Normal Plots. 58
4.3.4 Quantile-Quantile Plots. 61
4.4 Tests of Normality. 64
4.5 Boxplots. 65
4.6 Data Transformation . 67
4.7 The Geometry of Transformations. 71
4.8 Transformation Kernel Density Estimation . 75
4.9 Bibliographic Notes. 77
4.10 R Lab. 77
4.10.1 European Stock Indices . 77
4.10.2 McDonald’s Prices and Returns. 80
4.11 Exercises. 81
References. 83
5 Modeling Univariate Distributions. 85
5.1 Introduction. 85
5.2 Parametric Models and Parsimony. 85
5.3 Location, Scale, and Shape Parameters. 86
Contents xiii
5.4 Skewness, Kurtosis, and Moments. 87
5.4.1 The Jarque֊Bera Test. 91
5.4.2 Moments . 92
5.5 Heavy-Tailed Distributions. 93
5.5.1 Exponential and Polynomial Tails. 93
5.5.2 ¿-Distributions. 94
5.5.3 Mixture Models. 96
5.6 Generalized Error Distributions. 99
5.7 Creating Skewed from Symmetric Distributions .101
5.8 Quantile-Based Location, Scale, and Shape Parameters.103
5.9 Maximum Likelihood Estimation.104
5.10 Fisher Information and the Central Limit Theorem
for the MLE.105
5.11 Likelihood Ratio Tests.107
5.12 AIC and BIC.109
5.13 Validation Data and Cross-Validation.110
5.14 Fitting Distributions by Maximum Likelihood.113
5.15 Profile Likelihood.119
5.16 Robust Estimation.121
5.17 Transformation Kernel Density Estimation with a Parametric
Transformation .123
5.18 Bibliographic Notes.126
5.19 R Lab.127
5.19.1 Earnings Data.127
5.19.2 DAX Returns.129
5.19.3 McDonald’s Returns.130
5.20 Exercises.131
References.134
Resampling.137
6.1 Introduction. 137
6.2 Bootstrap Estimates of Bias, Standard Deviation, and MSE . . 139
6.2.1 Bootstrapping the MLE of the ¿-Distribution.139
6.3 Bootstrap Confidence Intervals.142
6.3.1 Normal Approximation Interval.143
6.3.2 Bootst rap-1 Intervals.143
6.3.3 Basic Bootstrap Interval.146
6.3.4 Percentile Confidence Intervals.146
6.4 Bibliographic Notes.150
6.5 R Lab.150
6.5.1 BMW Returns.150
6.5.2 Simulation Study: Bootstrapping the Kurtosis.152
6.6 Exercises.154
References.156
XIV
Contents
7 Multivariate Statistical Models.157
7.1 Introduction.157
7.2 Covariance and Correlation Matrices.157
7.3 Linear Functions of Random Variables.159
7.3.1 Two or More Linear Combinations of Random
Variables.161
7.3.2 Independence and Variances of Sums.162
7.4 Scatterplot Matrices.162
7.5 The Multivariate Normal Distribution.164
7.6 The Multivariate ¿-Distribution.165
7.6.1 Using the ¿-Distribution in Portfolio Analysis.167
7.7 Fitting the Multivariate ¿-Distribution by Maximum
Likelihood.168
7.8 Elliptically Contoured Densities.170
7.9 The Multivariate Skewed ¿-Distributions .172
7.10 The Fisher Information Matrix.174
7.11 Bootstrapping Multivariate Data.175
7.12 Bibliographic Notes.177
7.13 R Lab.177
7.13.1 Equity Returns.177
7.13.2 Simulating Multivariate ¿-Distributions.178
7.13.3 Fitting a Bivariate ¿-Distribution.180
7.14 Exercises. 181
References.182 8 *
8 Copulas. 183
8.1 Introduction.183
8.2 Special Copulas .185
8.3 Gaussian and ¿-Copulas.186
8.4 Archimedean Copulas.187
8.4.1 Frank Copula.187
8.4.2 Clayton Copula.189
8.4.3 Gumbel Copula.191
8.4.4 Joe Copula.192
8.5 Rank Correlation.193
8.5.1 Kendall’s Tau.194
8.5.2 Spearman’s Rank Correlation Coefficient.195
8.6 Tail Dependence.196
8.7 Calibrating Copulas.198
8.7.1 Maximum Likelihood .199
8.7.2 Pseudo-Maximum Likelihood.199
8.7.3 Calibrating Meta-Gaussian and Meta-t-Distributions . . 200
8.8 Bibliographic Notes.207
Contents
XV
8.9 R Lab.208
8.9.1 Simulating from Copula Models.208
8.9.2 Fitting Copula Models to Bivariate Return Data.210
8.10 Exercises.213
References.214
9 Regression: Basics .217
9.1 Introduction.217
9.2 Straight-Line Regression.218
9.2.1 Least-Squares Estimation.218
9.2.2 Variance of ß1.222
9.3 Multiple Linear Regression .223
9.3.1 Standard Errors, t-Values, and p-Values.225
9.4 Analysis of Variance, Sums of Squares, and R2.227
9.4.1 ANOVA Table.227
9.4.2 Degrees of Freedom (DF).229
9.4.3 Mean Sums of Squares (MS) and T-Tests.229
9.4.4 Adjusted R2.231
9.5 Model Selection.231
9.6 Collinearity and Variance Inflation .233
9.7 Partial Residual Plots. 240
9.8 Centering the Predictors.242
9.9 Orthogonal Polynomials. 243
9.10 Bibliographic Notes.243
9.11 R Lab. 243
9.11.1 U.S. Macroeconomic Variables .243
9.12 Exercises.245
References. 248 10 *
10 Regression: Troubleshooting.249
10.1 Regression Diagnostics.249
10.1.1 Leverages.251
10.1.2 Residuals.252
10.1.3 Cook’s Distance. 253
10.2 Checking Model Assumptions.255
10.2.1 Nonnormality.256
10.2.2 Nonconstant Variance.258
10.2.3 Nonlinearity. 259
10.3 Bibliographic Notes.262
10.4 R Lab.263
10.4.1 Current Population Survey Data .263
10.5 Exercises.265
References.268
XVI
Contents
11 Regression: Advanced Topics.269
11.1 The Theory Behind Linear Regression .269
11.1.1 Maximum Likelihood Estimation for Regression.270
11.2 Nonlinear Regression .271
11.3 Estimating Forward Rates from Zero-Coupon Bond Prices . 276
11.4 Transform-Both-Sides Regression.281
11.4.1 How TBS Works.283
11.5 Transforming Only the Response.284
11.6 Binary Regression. 286
11.7 Linearizing a Nonlinear Model.291
11.8 Robust Regression.293
11.9 Regression and Best Linear Prediction.295
11.9.1 Best Linear Prediction.295
11.9.2 Prediction Error in Best Linear Prediction.297
11.9.3 Regression Is Empirical Best Linear Prediction.298
11.9.4 Multivariate Linear Prediction.298
11.10 Regression Hedging.298
11.11 Bibliographic Notes.300
11.12 R Lab.300
11.12.1 Nonlinear Regression.300
11.12.2 Response Transformations.302
11.12.3 Binary Regression: Who Owns an Air Conditioner? . . . 303
11.13 Exercises. 304
References.305 12
12 Time Series Models: Basics.307
12.1 Time Series Data.307
12.2 Stationary Processes.307
12.2.1 White Noise . .310
12.2.2 Predicting White Noise. 311
12.3 Estimating Parameters of a Stationary Process.312
12.3.1 ACF Plots and the Ljung-Box Test.312
12.4 AR(1) Processes .314
12.4.1 Properties of a Stationary AR(1) Process.315
12.4.2 Convergence to the Stationary Distribution.316
12.4.3 Nonstationary AR(1) Processes.317
12.5 Estimation of AR(1) Processes.318
12.5.1 Residuals and Model Checking. 318
12.5.2 Maximum Likelihood and Conditional Least-Squares . . 323
12.6 AR(p) Models .325
12.7 Moving Average (MA) Processes.328
12.7.1 MA(1) Processes.328
12.7.2 General MA Processes .330
12.8 ARM A Processes.331
12.8.1 The Backwards Operator .331
Contents xvii
12.8.2 The ARMA Model.332
12.8.3 ARMA(1,1) Processes.332
12.8.4 Estimation of ARM A Parameters.333
12.8.5 The Differencing Operator.333
12.9 ARIMA Processes.334
12.9.1 Drifts in ARIMA Processes.337
12.10 Unit Root Tests.338
12.10.1 How Do Unit Root Tests Work? .341
12.11 Automatic Selection of an ARIMA Model.342
12.12 Forecasting.342
12.12.1 Forecast Errors and Prediction Intervals . . .344
12.12.2 Computing Forecast Limits by Simulation.346
12.13 Partial Autocorrelation Coefficients.349
12.14 Bibliographic Notes.352
12.15 R Lab.352
12.15.1 T-bill Rates.352
12.15.2 Forecasting.355
12.16 Exercises.356
References .360
13 Time Series Models: Further Topics.361
13.1 Seasonal ARIMA Models.361
13.1.1 Seasonal and Nonseasonal Differencing.362
13.1.2 Multiplicative ARIMA Models.362
13.2 Box-Cox Transformation for Time Series. 365
13.3 Time Series and Regression.367
13.3.1 Residual Correlation and Spurious Regressions.368
13.3.2 Heteroscedasticity and Autocorrelation Consistent
(HAC) Standard Errors.373
13.3.3 Linear Regression with ARM A Errors.377
13.4 Multivariate Time Series . 380
13.4.1 The Cross-Correlation Function.380
13.4.2 Multivariate White Noise.382
13.4.3 Multivariate ACF Plots and the Multivariate
Ljung-Box Test.383
13.4.4 Multivariate ARMA Processes .384
13.4.5 Prediction Using Multivariate AR Models .387
13.5 Long-Memory Processes. 389
13.5.1 The Need for Long-Memory Stationary Models.389
13.5.2 Fractional Differencing.390
13.5.3 FARIMA Processes.391
13.6 Bootstrapping Time Series.394
13.7 Bibliographic Notes .395
13.8 R Lab.395
13.8.1 Seasonal ARIMA Models.395
13.8.2 Regression with HAC Standard Errors.396
xviii Contents
13.8.3 Regression with ARMA Noise.397
13.8.4 VAR Models.397
13.8.5 Long-Memory Processes.399
13.8.6 Model-Based Bootstrapping of an ARIMA Process . 400
13.9 Exercises.401
References. 403
14 G ARCH Models.405
14.1 Introduction.405
14.2 Estimating Conditional Means and Variances.406
14.3 ARCH(l) Processes.407
14.4 The AR(1)+ARCH(1) Model .409
14.5 ARCH(p) Models. 411
14.6 ARIMA(p^,d,gjvf)H“GARCH(pv)Qv) Models.411
14.6.1 Residuals for ARIMA(pm,d, 7m)+GARCH(pv5 qv)
Models.412
14.7 G ARCH Processes Have Heavy Tails.413
14.8 Fitting ARMA+GARCH Models.413
14.9 GARCH Models as ARMA Models.418
14.10 GARCH(1,1) Processes .419
14.11 APARCH Models .421
14.12 Linear Regression with ARMA+GARCH Errors.424
14.13 Forecasting ARMA+GARCH Processes.426
14.14 Multivariate GARCH Processes.428
14.14.1 Multivariate Conditional Heteroscedasticity.428
14.14.2 Basic Setting.431
14.14.3 Exponentially Weighted Moving Average (EWMA)
Model.432
14.14.4 Orthogonal GARCH Models.433
14.14.5 Dynamic Orthogonal Component (DOC) Models.436
14.14.6 Dynamic Conditional Correlation (DCC) Models.439
14.14.7 Model Checking.441
14.15 Bibliographic Notes.443
14.16 R Lab.443
14.16.1 Fitting GARCH Models.443
14.16.2 The GARCH-in-Mean (GARCH-M) Model .445
14.16.3 Fitting Multivariate GARCH Models.445
14.17 Exercises.447
References.451 15 *
15 Cointegration.453
15.1 Introduction.453
15.2 Vector Error Correction Models.455
15.3 Trading Strategies. 459
15.4 Bibliographic Notes.460
Contents
XIX
15.5 R Lab. 460
15.5.1 Cointegration Analysis of Midcap Prices.460
15.5.2 Cointegration Analysis of Yields.460
15.5.3 Cointegration Analysis of Daily Stock Prices.461
15.5.4 Simulation.462
15.6 Exercises.462
References.463
16 Portfolio Selection.465
16.1 Trading Off Expected Return and Risk.465
16.2 One Risky Asset and One Risk-Free Asset.465
16.2.1 Estimating E{R) and tr.467
16.3 Two Risky Assets.468
16.3.1 Risk Versus Expected Return.468
16.4 Combining Two Risky Assets with a Risk-Free Asset.469
16.4.1 Tangency Portfolio with Two Risky Assets.469
16.4.2 Combining the Tangency Portfolio with the Risk-FYee
Asset .471
16.4.3 Effect of pi2 .472
16.5 Selling Short.473
16.6 Risk-Efficient Portfolios with N Risky Assets.474
16.7 Resampling and Efficient Portfolios.479
16.8 Utility. 484
16.9 Bibliographic Notes.488
16.10 R Lab. 488
16.10.1 Efficient Equity Portfolios.488
16.10.2 Efficient Portfolios with Apple, Exxon-Mobil, Target,
and McDonald’s Stock.489
16.10.3 Finding the Set of Possible Expected Returns.490
16.11 Exercises.491
References. 493 17 *
17 The Capital Asset Pricing Model.495
17.1 Introduction to the CAPM.495
17.2 The Capital Market Line (CML).496
17.3 Betas and the Security Market Line .499
17.3.1 Examples of Betas.500
17.3.2 Comparison of the CML with the SML.500
17.4 The Security Characteristic Line.501
17.4.1 Reducing Unique Risk by Diversification.503
17.4.2 Are the Assumptions Sensible?.504
17.5 Some More Portfolio Theory.504
17.5.1 Contributions to the Market Portfolio’s Risk.505
17.5.2 Derivation of the SML.505
17.6 Estimation of Beta and Testing the CAPM.507
XX
Contents
17.6.1 Estimation Using Regression.507
17.6.2 Testing the CAPM .509
17.6.3 Interpretation of Alpha.509
17.7 Using the CAPM in Portfolio Analysis.510
17.8 Bibliographic Notes.510
17.9 R Lab.510
17.9.1 Zero-beta Portfolios.512
17.10 Exercises.512
References. 515
18 Factor Models and Principal Components.517
18.1 Dimension Reduction.517
18.2 Principal Components Analysis. 517
18.3 Factor Models .527
18.4 Fitting Factor Models by Time Series Regression.528
18.4.1 Fama and French Three-Factor Model.529
18.4.2 Estimating Expectations and Covariances of Asset
Returns.534
18.5 Cross-Sectional Factor Models .538
18.6 Statistical Factor Models.540
18.6.1 Varimax Rotation of the Factors.545
18.7 Bibliographic Notes.546
18.8 R Lab. 546
18.8.1 PCA.546
18.8.2 Fitting Factor Models by Time Series Regression.548
18.8.3 Statistical Factor Models. 550
18.9 Exercises.551
References.552 19 *
19 Risk Management. 553
19.1 The Need for Risk Management.553
19.2 Estimating VaR and ES with One Asset.555
19.2.1 Nonparametric Estimation of VaR and ES.555
19.2.2 Parametric Estimation of VaR and ES .557
19.3 Bootstrap Confidence Intervals for VaR and ES.559
19.4 Estimating VaR and ES Using ARMA+GARCH Models.561
19.5 Estimating VaR and ES for a Portfolio of Assets.563
19.6 Estimation of VaR Assuming Polynomial Tails.565
19.6.1 Estimating the Tail Index .567
19.7 Pareto Distributions.571
19.8 Choosing the Horizon and Confidence Level.571
19.9 VaR and Diversification.573
19.10 Bibliographic Notes.575
19.11 R Lab.575
19.11.1 Univariate VaR and ES .575
19.11.2 VaR Using a Multivariate-t Model.576
Contents xxi
19.12 Exercises.577
References.578
20 Bayesian Data Analysis and MCMC.581
20.1 Introduction.581
20.2 Bayes’s Theorem.582
20.3 Prior and Posterior Distributions.584
20.4 Conjugate Priors.586
20.5 Central Limit Theorem for the Posterior .592
20.6 Posterior Intervals.593
20.7 Markov Chain Monte Carlo.595
20.7.1 Gibbs Sampling. 596
20.7.2 Other Markov Chain Monte Carlo Samplers.597
20.7.3 Analysis of MCMC Output.597
20.7.4 JAGS.598
20.7.5 Monitoring MCMC Convergence and Mixing.602
20.7.6 DIC and pjj for Model Comparisons.609
20.8 Hierarchical Priors.612
20.9 Bayesian Estimation of a Covariance Matrix.618
20.9.1 Estimating a Multivariate Gaussian Covariance
Matrix. 618
20.9.2 Estimating a Multivariate-i Scale Matrix.620
20.9.3 Non-Wishart Priors for the Covariate Matrix.623
20.10 Stochastic Volatility Models.623
20.11 Fitting GARCH Models with MCMC.: . 626
20.12 Fitting a Factor Model. 629
20.13 Sampling a Stationary Process.632
20.14 Bibliographic Notes.635
20.15 E Lab.636
20.15.1 Fitting a ¿-Distribution by MCMC.636
20.15.2 AR Models.639
20.15.3 MA Models.640
20.15.4 ARMA Models.641
20.16 Exercises. 642
References. 643 21
21 Nonparametric Regression and Splines.645
21.1 Introduction. 645
21.2 Local Polynomial Regression.648
21.2.1 Lowess and Loess.652
21.3 Linear Smoothers .653
21.3.1 The Smoother Matrix and the Effective Degrees
of Freedom . .653
21.3.2 AIC, CV, and GCV.654
21.4 Polynomial Splines.654
21.4.1 Linear Splines with One Knot.655
xxii Contents
21.4.2 Linear Splines with Many Knots. 656
21.4.3 Quadratic Splines.656
21.4.4 pth Degree Splines.657
21.4.5 Other Spline Bases .658
21.5 Penalized Splines .658
21.5.1 Cubic Smoothing Splines.659
21.5.2 Selecting the Amount of Penalization.659
21.6 Bibliographic Notes.664
21.7 R Lab.664
21.7.1 Additive Model for Wages, Education,
and Experience.664
21.7.2 An Extended CKLS Model for the Short Rate.665
21.8 Exercises.666
References.667
A Facts from Probability, Statistics, and Algebra.669
A.l Introduction.669
A.2 Probability Distributions. 669
A.2.1 Cumulative Distribution Functions.669
A.2.2 Quantiles and Percentiles.670
A.2.3 Symmetry and Modes. 670
A.2.4 Support of a Distribution.670
A.3 When Do Expected Values and Variances Exist? .671
A.4 Monotonie Functions . 672
A.5 The Minimum, Maximum, Infinum, and Supremum of a Set . . 672
A.6 Functions of Random Variables . 672
A.7 Random Samples.673
A.8 The Binomial Distribution.674
A.9 Some Common Continuous Distributions.674
A.9.1 Uniform Distributions. 674
A.9.2 Transformation by the CDF and Inverse CDF.675
A.9.3 Normal Distributions .676
A.9.4 The Lognormal Distribution.676
A.9.5 Exponential and Double-Exponential Distributions. . . . 678
A.9.6 Gamma and Inverse-Gamma Distributions.678
A.9.7 Beta Distributions.679
A.9.8 Pareto Distributions.680
A. 10 Sampling a Normal Distribution.681
A.10.1 Chi-Squared Distributions.681
A.10.2 F-Distributions .681
A. 11 Law of Large Numbers and the Central Limit Theorem
for the Sample Mean.682
A. 12 Bivariate Distributions.682
Contents xxiii
A. 13 Correlation and Covariance.683
A. 13.1 Normal Distributions: Conditional Expectations
and Variance.687
A. 14 Multivariate Distributions.687
A. 14.1 Conditional Densities.688
A. 15 Stochastic Processes.688
A. 16 Estimation.689
A. 16.1 Introduction.689
A. 16.2 Standard Errors.689
A. 17 Confidence Intervals.690
A.17.1 Confidence Interval for the Mean.690
A. 17.2 Confidence Intervals for the Variance
and Standard Deviation.692
A. 17.3 Confidence Intervals Based on Standard Errors.693
A. 18 Hypothesis Testing.693
A.18.1 Hypotheses, Types of Errors, and Rejection Regions . . 693
A.18.2 p֊Values.693
A. 18.3 Two-Sample t-Tests.694
A.18.4 Statistical Versus Practical Significance.697
A. 19 Prediction.697
A.20 Facts About Vectors and Matrices.698
A.21 Roots of Polynomials and Complex Numbers .699
A.22 Bibliographic Notes.700
References.700
Index
703 |
| any_adam_object | 1 |
| author | Ruppert, David 1948- Matteson, David S. |
| author_GND | (DE-588)133112209 (DE-588)1071738224 |
| author_facet | Ruppert, David 1948- Matteson, David S. |
| author_role | aut aut |
| author_sort | Ruppert, David 1948- |
| author_variant | d r dr d s m ds dsm |
| building | Verbundindex |
| bvnumber | BV042696901 |
| classification_rvk | SK 980 QP 890 QK 600 QH 234 |
| ctrlnum | (OCoLC)915419738 (DE-599)BVBBV042696901 |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV042696901 |
| illustrated | Not Illustrated |
| indexdate | 2025-03-04T05:00:12Z |
| institution | BVB |
| isbn | 9781493926138 9781493951734 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028128474 |
| oclc_num | 915419738 |
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| physical | xxvi, 719 Seiten Diagramme |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in statistics |
| spelling | Ruppert, David 1948- Verfasser (DE-588)133112209 aut Statistics and data analysis for financial engineering with R examples David Ruppert ; David S. Matteson Second edition New York ; Heidelberg ; Dordrecht ; London Springer 2015 xxvi, 719 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Springer texts in statistics Datenanalyse (DE-588)4123037-1 gnd rswk-swf Financial Engineering (DE-588)4208404-0 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Financial Engineering (DE-588)4208404-0 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s b DE-604 Matteson, David S. Verfasser (DE-588)1071738224 aut Erscheint auch als Online-Ausgabe 978-1-4939-2614-5 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028128474&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ruppert, David 1948- Matteson, David S. Statistics and data analysis for financial engineering with R examples Datenanalyse (DE-588)4123037-1 gnd Financial Engineering (DE-588)4208404-0 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4208404-0 (DE-588)4056995-0 |
| title | Statistics and data analysis for financial engineering with R examples |
| title_auth | Statistics and data analysis for financial engineering with R examples |
| title_exact_search | Statistics and data analysis for financial engineering with R examples |
| title_full | Statistics and data analysis for financial engineering with R examples David Ruppert ; David S. Matteson |
| title_fullStr | Statistics and data analysis for financial engineering with R examples David Ruppert ; David S. Matteson |
| title_full_unstemmed | Statistics and data analysis for financial engineering with R examples David Ruppert ; David S. Matteson |
| title_short | Statistics and data analysis for financial engineering |
| title_sort | statistics and data analysis for financial engineering with r examples |
| title_sub | with R examples |
| topic | Datenanalyse (DE-588)4123037-1 gnd Financial Engineering (DE-588)4208404-0 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Datenanalyse Financial Engineering Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028128474&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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