1  20
Next
 Franke, Jürgen.
 New York, NY : Springer US, 1984.
 Description
 Book — 1 online resource (286 pages) Digital: text file.PDF.
 Summary

 On the Use of Bayesian Models in Time Series Analysis
 Order Determination for Processes with Infinite Variance
 Asymptotic Behaviour of the Estimates Based on Residual Autocovariances for ARMA Models
 Parameter Estimation of Stationary Processes with Spectra Containing Strong Peaks
 Linear ErrorinVariables Models
 MinimaxRobust Filtering and FiniteLength Robust Predictors
 The Problem of Unsuspected Serial Correlations
 The Estimation of ARMA Processes
 How to Determine the Bandwidth of some Nonlinear Smoothers in Practice
 Remarks on NonGaussian Linear Processes with Additive Gaussian Noise
 GrossError Sensitivies of GM and RAEstimates
 Some Aspects of Qualitative Robustness in Time Series
 Tightness of the Sequence of Empiric C.D.F. Processes Defined from Regression Fractiles
 Robust Nonparametric Autoregression
 Robust Regression by Means of SEstimators
 On Robust Estimation of Parameters for Autoregressive Moving Average Models.
 Description
 Book — 21p.
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4. Applied nonparametric regression [1990]
 Härdle, Wolfgang.
 Cambridge [England] ; New York : Cambridge University Press, 1990.
 Description
 Book — xv, 333 p. : ill. ; 24 cm.
 Summary

 Preface
 Part I. Regression Smoothing: 1. Introduction
 2. Basic idea of smoothing 3. Smoothing techniques
 Part II. The Kernel Method: 4. How close is the smooth to the true curve?
 5. Choosing the smoothing parameter
 6. Data sets with outliers
 7. Smoothing with correlated data
 8. Looking for special features (qualitative smoothing)
 9. Incorporating parametric components and alternatives
 Part III. Smoothing in High Dimensions: 10. Investigating multiple regression by additive models
 Appendices
 References
 List of symbols and notation.
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QA278.2 .H38 1990  Available 
 Härdle, Wolfgang.
 New York, NY : Springer New York, 1991.
 Description
 Book — 1 online resource (xi, 261 pages 87 illustrations) Digital: text file.PDF.
 Summary

 I. Density Smoothing.
 1. The Histogram. 1.0 Introduction. 1.1 Definitions of the Histogram. The Histogram as a Frequency Counting Curve. The Histogram as a Maximum Likelihood Estimate. Varying the Binwidth. 1.2 Statistics of the Histogram. 1.3 The Histogram in S. 1.4 Smoothing the Histogram by WARPing. WARPing Algorithm. WARPing in S. Exercises.
 2. Kernel Density Estimation. 2.0 Introduction. 2.1 Definition of the Kernel Estimate. Varying the Kernel. Varying the Bandwidth. 2.2 Kernel Density Estimation in S. Direct Algorithm. Implementation in S. 2.3 Statistics of the Kernel Density. Speed of Convergence. Confidence Intervals and Confidence Bands. 2.4 Approximating Kernel Estimates by WARPing. 2.5 Comparison of Computational Costs. 2.6 Comparison of Smoothers Between Laboratories. Keeping the Kernel Bias the Same. Keeping the Support of the Kernel the Same. Canonical Kernels. 2.7 Optimizing the Kernel Density. 2.8 Kernels of Higher Order. 2.9 Multivariate Kernel Density Estimation. Same Bandwidth in Each Component. Nonequal Bandwidths in Each Component. A Matrix of Bandwidths. Exercises.
 3. Further Density Estimators. 3.0 Introduction. 3.1 Orthogonal Series Estimators. 3.2 Maximum Penalized Likelihood Estimators. Exercises.
 4. Bandwidth Selection in Practice. 4.0 Introduction. 4.1 Kernel Estimation Using Reference Distributions. 4.2 PlugIn Methods. 4.3 CrossValidation. 4.3.1 Maximum Likelihood CrossValidation. Direct Algorithm. 4.3.2 LeastSquares CrossValidation. Direct Algorithm. 4.3.3 Biased CrossValidation. Algorithm. 4.4 CrossValidation for WARPing Density Estimation. 4.4.1 Maximum Likelihood CrossValidation. 4.4.2 LeastSquares CrossValidation. Algorithm. Implementation in S. 4.4.3 Biased CrossValidation. Algorithm. Implementation in S. Exercises. II. Regression Smoothing.
 5. Nonparametric Regression. 5.0 Introduction. 5.1 Kernel Regression Smoothing. 5.1.1 The NadarayaWatson Estimator. Direct Algorithm. Implementation in S. 5.1.2 Statistics of the NadarayaWatson Estimator. 5.1.3 Confidence Intervals. 5.1.4 Fixed Design Model. 5.1.5 The WARPing Approximation. Basic Algorithm. Implementation in S. 5.2 kNearest Neighbor (kNN). 5.2.1 Definition of the kNN Estimate. 5.2.2 Statistics of the kNN Estimate. 5.3 Spline Smoothing. Exercises.
 6. Bandwidth Selection. 6.0 Introduction. 6.1 Estimates of the Averaged Squared Error. 6.1.0 Introduction. 6.1.1 Penalizing Functions. 6.1.2 CrossValidation. Direct Algorithm. 6.2 Bandwidth Selection with WARPing. Penalizing Functions. CrossValidation. Basic Algorithm. Implementation in S. Applications. Exercises.
 7. Simultaneous Error Bars. 7.1 Golden Section Bootstrap. Algorithm for Golden Section Bootstrapping. Implementation in S. 7.2 Construction of Confidence Intervals. Exercises. Tables. Solutions. List of Used S Commands. Symbols and Notation. References.
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 Härdle, Wolfgang.
 New York : SpringerVerlag, 1991.
 Description
 Book — xi, 261 p. : ill. ; 25 cm.
 Summary

 Density smoothing  the histogram
 kernel density estimation
 further density estimator
 bandwidth selection in practice
 regression smoothing  nonparametric regression
 bandwidth selection
 simultaneous error bars.
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QA278 .H348 1990  Available 
7. Computer intensive methods in statistics [1993]
 Heidelberg : Springer, [1993]
 Description
 Book — 1 online resource (vi, 176 pages) : illustrations
 Summary

 Bayesian EdgeDetection in Images via Changepoint Methods / D.A. Stephens and A.F.M. Smith
 Efficient Computer Generation of MatricVariate t Drawings with an Application to Bayesian Estimation of Simple Market Models / F. Kleibergen and H.K. van Dijk
 Approximate HPD Regions for Testing Residual Autocorrelation Using Augmented Regressions / L. Bauwens and A. Rasquero
 Intensive Numerical and Symbolic Computing in Parametric Test Theory / D. Wauters and L. Vermeire
 Learning Data Analysis and Mathematical Statistics with a Macintosh / A. Antoniadis, J. Berruyer and R. Carmona
 Bayesian Electromagnetic Imaging / M. Roussignol, V. Jouanne, M. Menvielle and P. Tarits
 Markov Random Field Models in Image Remote Sensing / V. Granville and J.P. Rasson
 Minimax Linewise Algorithm for Image Reconstruction / A.P. Korostelev and A.B. Tsybakov
 Bandwidth Selection for Kernel Regression: a Survey / P. Vieu.
 Practical Use of Bootstrap in Regression / M.A. Gruet, S. Huet and E. Jolive
 Application of Resampling Methods to the Choice of Dimension in Principal Component Analysis / Ph. Besse and A. de Falguerolles.
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 Härdle, Wolfgang.
 New York : SpringerVerlag, [1995]
 Description
 Book — xvi, 387 p. : ill. (some col.) ; 24 cm.
 Summary

This book describes the statistical computing environment called XploRe which is a widely available package (details on how to obtain it are provided in the book). As its name suggests, XploRe provides a highly interactive graphics interface for exploratory statistical analysis and provides for userwritten macros and smoothing procedures for effective highdimensional data analysis. The main aim of the book is to show how XploRe can be used for a wide variety of statistical tasks ranging from basic data manipulation to interactive customizing of graphs and dynamic fitting of highdimensional statistical models. As a result, it may be used as the basis of a course in model building, computational statistics, applied multivariate analysis, and econometrics.
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QA276.4 .H365 1995  Available 
 Härdle, Wolfgang.
 New York, NY : Springer New York, 1995.
 Description
 Book — 1 online resource (xxxii, 387 pages 143 illustrations) Digital: text file.PDF.
 Summary

 I A Beginner's Course. 1 Un AmuseGueule. 2 An XploRe Tutorial. 2.1 Getting Started. 2.2 TwoDimensional Plots. 2.3 Creating a Macro. 2.4 The Interactive Help System. 2.5 ThreeDimensional Plots. 2.6 Reading and Writing Data. 3 The Integrated Working Environment. 3.1 Introduction. 3.2 The Editor. 3.3 How to Run and Debug a Program. 3.4 The ContextSensitive Help System. 3.5 Graphic Tools in XploRe. 3.6 Multiple Window Displays. 3.7 Manipulating Windows. 3.8 How to Print a Graphic. 3.9 The Static 2D Window. 3.10 The Dynamic 3D Window. 3.11 The Boxplot Window. 3.12 The Flury Faces Window. 3.13 How to Use and Create Libraries. II XploRe in Use. 4 Graphical Aids for Statistical Data Analysis. 4.1 Introduction. 4.2 First Pictures. 4.3 Stratifications. 5 Density and Regression Smoothing. 5.1 Introduction. 5.2 Density Estimation. 5.3 NadarayaWatson Nonparametric Regression. 5.4 Local Polynomial Fitting. 5.5 Estimation of Regression Derivatives. 5.6 Variable Amount of Smoothing. 6 Bandwidth Selection in Density Estimation. 6.1 Introduction. 6.2 Choosing the Smoothing Parameter. 6.3 Density Estimation in Action. 7 Interactive Graphics for Teaching Simple Statistics. 7.1 Introduction. 7.2 The General Structure of the Teachware System. 7.3 Description of the Main Macros in the Module. 7.4 Details on XploRe Language. 7.5 Conclusions. 8 XClust: Clustering in an Interactive Way. 8.1 Introduction. 8.2 Cluster Analysis and Classification. 8.3 The KMeans Method in XploRe. 8.4 The Adaptive KMeans Method. 8.5 The Hierarchical Cluster Analysis. 8.6 Classification and Regression Tree (CART). 8.7 The Investigation of the Stability of Adaptive Weights. 9 Exploratory Projection Pursuit. 9.1 Introduction. 9.2 Projection Pursuit Indices. 9.3 The Index Functions in Practice. 9.4 What Will Be Found on Real Data?. 9.5 Computational Aspects. 9.6 The PPEXPL Macro. 10 Generalized Linear Models. 10.1 Introduction. 10.2 Some Theory. 10.3 Implementation. 10.4 Example for a Gamma Model. 10.5 Negative Binomial Regression. 10.6 GLM Extensions: Parametric Survival Models. 11 Additive Modeling. 11.1 Introduction. 11.2 Generalized Additive Models. 11.3 Sliced Inverse Regression. 11.4 Average Derivative Estimation. 12 Comparing Parametric and Semiparametric Binary Response Models. 12.1 Introduction. 12.2 The Data. 12.3 Parametric and Semiparametric Binary Response Models. 12.4 Estimation. 12.5 Testing the Adequacy of the Logit Link. 12.6 Summary and Conclusions. 13 Approximative Methods for Regression Models with Errors in Covariates. 13.1 Introduction. 13.2 Regression Calibration. 13.3 Simulation and Extrapolation (SIMEX). 14 Nonlinear Time Series Analysis. 14.1 Introduction. 14.2 Parametric Approaches. 14.3 Nonparainetric Approach. 14.4 Nonlinearity Tests. 14.5 Nonlinear Prediction. 15 Un Digestif. 15.1 A Whole Bunch of Pictures. 15.2 Interactive Contouring. A Questions to XploRe. B Language Reference. B.1 Operators. B.2 Flow Control. B.3 Commands. References. Author Index.
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 Heidelberg : PhysicaVerlag, ©1996.
 Description
 Book — 1 online resource (viii, 265 pages) : illustrations Digital: text file.PDF.
 Summary

 A Personal View of Smoothing and Statistics
 Smoothing by Local Regression: Principles and Methods
 Variance Properties of Local Polynomials and Ensuing Modifications
 Comments
 Rejoinder
 Robust Bayesian Nonparametric Regression
 The Invariance of Statistical Anaylses with Respect to the Inner Product in the Reproducing Kernel Hilbert Space
 A Note on CrossValidation for Smoothing Splines
 Some Comments on CrossValidation
 Extreme Percentile Regression
 Mean and Dispersion Additive Models
 Interaction in Nonlinear Principal Components Analysis
 Nonparametric Estimation of Additive Separable Regression Models.
 New York : Springer, 1998.
 Description
 Book — xviii, 265 p. : ill. ; 24 cm.
 Summary

 Wavelets * The Haar basis wavelet system * The idea of multiresolution analysis * Some facts from Fourier analysis * Basic relations of wavelet theory * Construction of wavelet bases * Compactly supported wavelets * Wavelets and approximation * Wavelets and Besov spaces * Statistical estimation using wavelets * Wavelet thresholding and adaption * Computational aspects and software.
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12. XploRe  Learning Guide [1999]
 Hardle, Wolfgang.
 Berlin, Heidelberg : Springer Berlin Heidelberg, 1999.
 Description
 Book — 1 online resource (IV, 526 pages)
 Summary

 STATs for basic statistical modelling
 GLM generalized linear models
 SMOOTHER for smoothing methods
 FINANCE for option pricing stock simulation and nonlinear time series analysis
 modern regression technique with wavelets and more networds
 VaR (value at risk) estimation with extreme values and historical simulation
 TWARE for interactive teaching of Statistics.
13. Measuring risk in complex stochastic systems [2000]
 New York : Springer, 2000.
 Description
 Book — xiii, 257 p. ; 24 cm.
 Summary

 Integrated Risk Management and Extreme Value Theory. Coherent Allocation Capital for Credit Portfolios. A Simple Approach to Country Risk. The Structure of Credit Risk. Extreme Value Theory and Risk Management: Basic Results. Sensitivity of Values at Risk. Extremes of ARCH Models. Risk Exposure and its Sensitivity to Model Misspecification. Neural Networks and Applications in Finance. Nonlinear Approximation and Statistical Applications I. Semiparametric Lower Bounds for Tail Index Estimation. Bandwith Choice for Mestimators in Projection Pursuit and Single Index Regression. Semiparametric Indirect Inference. Changepoint Problem in ARCH Models. Change in Polynomial Regression and Related Processes.
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14. Partially linear models [2000]
 Härdle, Wolfgang.
 Heidelberg ; New York : PhysicaVerlag, ©2000.
 Description
 Book — 1 online resource (x, 202 pages) : illustrations Digital: text file.PDF.
 Summary

 Introduction
 Estimation of the Parametric Component
 Estimation of the Nonparametric Component
 Estimation with Measurement Errors
 Some Related Theoretic Topics
 Partially Linear Time Series Models
 Appendix: Basic Lemmas.
15. XploRe®  Application Guide [2000]
 Hardle, Wolfgang.
 Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint : Springer, 2000.
 Description
 Book — 1 online resource (IV, 525 pages) Digital: text file.PDF.
 Summary

 I Regression Models
 1 Quantile Regression
 2 Least Trimmed Squares
 3 ErrorsinVariables Models
 4 SimultaneuosEquations Models
 5 Hazard Regression
 6 Generalized Partial Linear Models
 7 Generalized Additive Models
 II Data Exploration
 8 Growth Regression and Counterfactual Income Dynamics
 9 Cluster Analysis
 10 Classification and Regression Trees
 11 DPLS: Partial Least Squares Program
 12 Uncovered Interest Parity
 13 Correspondence Analysis
 III Dynamic Statistical Systems
 14 LongMemory Analysis
 15 ExploRing Persistence in Financial Time Series
 16 Flexible Time Series Analysis
 17 Multiple Time Series Analysis
 18 Robust Kalman Filtering.
 Härdle, Wolfgang.
 Berlin ; New York : Springer, [2002]
 Description
 Book — 1 online resource (pxx, 401 pages) : illustrations Digital: text file.PDF.
 Summary

 Value at Risk. Credit Risk. Implied Volatility. Econometrics.
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 Härdle, Wolfgang.
 Heidelberg : PhysicaVerlag HD : Imprint : Physica, 2002.
 Description
 Book — 1 online resource (xii, 648 pages 205 illustrations)
 Summary

 An Implementation for Regression Quantile Estimation. Computational Methods for Time Series Analysis. Forecasting PCARIMA Models for Functional Data. KyPlot as a Tool for Graphical Data Analysis. Mice and Elephants Visualization of Internet Traffic. Relativity and Resolution for High Dimensional Information Visualization with Generalized Association Plots (GAP). Supervised Learning from Microarray Data. Teaching Statistics with Electronic Textbooks. TransDimensional Markov Chains and their Applications in Statistics. A Bayesian Model for Compositional Data Analysis. A Comparison of Marginal Likelihood Computation Methods. A Hotelling Test Based on MCD. A Resampling Approach to Cluster Validation. A Self Documenting Programming Environment for Weighting. A State Space Model for NonStationary Functional Data. A Wildlife Simulation Package (WiSP). Algorithmical and Computational Procedures for a Markov Model in Survival Analysis. An Algorithm for the Construction of Experimental Designs with Fixed and Random Blocks. An Algorithm to Estimate Time Varying Parameter SURE Models under Different Type of Restrictions. Analyzing Data with Robust Multivariate Methods and Diagnostic Plots. Application of "Aggregated Classifiers" in Survival Time Studies. Application of Hopfieldlike Neural Networks to Nonlinear Factorization. Bagging Tree Classifiers for Glaucoma Diagnosis. Bayesian Automatic Parameter Estimation of Threshold Autoregressive (TAR) Models using Markov Chain Monte Carlo (MCMC). Bayesian Semiparametric Seemingly Unrelated Regression. Blockmodeling Techniques for Web Mining. Bootstrapping Threshold Autoregressive Models. Canonical Variates for Recursive Partitioning in Data Mining. CAnoVa(c): a Software for Causal Modeling. Classification Based on the Support Vector Machine Regression Depth, and Discriminant Analysis. Clockwise Bivariate Boxplots. Combining Graphical Models and PCA for Statistical Process Control. Comparing Two Partitions: Some Proposals and Experiments. Comparison of Nested Simulated Annealing and Reactive Tabu Search for Efficient Experimental Designs with Correlated Data. Computational Connections between Robust Multivariate Analysis and Clustering. Computer Intensive Methods for Mixedeffects Models. Construction of TOptimum Designs for Multiresponse Dynamic Models. Data Compression and Selection of Variables with Respect to Exact Inference. Data Extraction from Dense 3D Surface Models. Detection of Locally Stationary Segments in Time Series. Detection of Outliers in Multivariate Data: A Method Based on Clustering and Robust Estimators. Development of a Framework for Analyzing Process Monitoring Data with Applications to Semiconductor Manufacturing Process. Different Ways to See a Tree  KLIMT. estat: A Webbased Learning Environment in Applied Statistics. estat: Automatic Evaluation of Online Exercises. estat: Basic Stochastic Finance at School Level. estat: Development of a Scenario for Statistics in Chemical Engineering.. estat: Webbased Learning and Teaching of Statistics in Secondary Schools. EMILeAstat: Structural and Didactic Aspects of Teaching Statistics through an Internetbased Multimedial Environment. Evaluating the GPH Estimator via Bootstrap Technique. Evolutionary Algorithms with Competing Heuristics in Computational Statistics. Exact Nonparametric Inference in R. Exploring the Structure of Regression Surfaces by using SiZer Map for Additive Models. Fast and Robus Filtering of Time Series with Trends. Functional Principal Component Modelling of the Intensity of a Doubly Stochastic Poisson Process. Growing and Visualizing Prediction Paths Trees in Market Basket Analysis. Improved Fitting of Constrained Multivariate Regression Models using Automatic Differentiation. Imputation of Continuous Variables Missing at Random using the Method of Simulated Scores. Induction of Association Rules: Apriori Implementation. Intelligent WBT: Specification and Architecture of the Distributed Multimedia eLearning System estat. Interactive Exploratory Analysis of SpatioTemporal Data. Interactive Graphics for Data Mining. Least Squares Reconstruction of Binary Images using Eigenvalue Optimization. Locally Adaptive Function Estimation for Categorical Regression Models. Maneuvering Target Tracking by using Particle Filter Method with Model Switching Structure. mathStatica: Mathematical Statistics with Mathematica. MCMC Model for Estimation Poverty Risk Factors using Household Budget Data. MD *Book online & estat: Generating estat Modules from LaTeX. Missing Data Incremental Imputation through Tree Based Methods. Missing Values Resampling for Time Series. ModelBuilder  an Automated Generaltospecific Modelling Tool. On the Use of Particle Filters for Bayesian Image Restoration. Optimally Trained Regression Trees and Occam's Razor. Parallel Algorithms for Inference in Spatial Gaussian Models. Parameters Estimation of Block Mixture Models. Pattern Recognition of Time Series using Wavelets. Representing Knowledge in the Statistical System Jasp. Robust Estimation with Discrete Explanatory Variables. Robust Principal Components Regression. Robust Time Series Analysis through the Forward Search. Rough Sets and Association Rules  Which is Efficient?. Skewness and Fat Tails in Discrete Choice Models. Standardized Partition Spaces. StatDataML: An XML Format for Statistical Data. Statistical Computing on Web Browsers with the Dynamic Link Library. Statistical Inference for a Robust Measure of Multiple Correlation. Statistical Software VASMM for Variable Selection in Multivariate Methods. Structural Equation Models for Finite Mixtures  Simulation Results and Empirical Applications. Sweave: Dynamic Generation of Statistical Reports using Literate Data Analysis. Testing for Simplification in Spatial Models. The Forward Search. The MISSION Client: Navigating Ontology Information for Query Formulation and Publication in Distributed Statistical Information Systems. Time Series Modelling using Mobile Devices and Broadband Internet. Unbiased Partial Spline Fitting under Autoregressive Errors. Unobserved Heterogeneity in Store Choice Models. Using the Forward Library in Splus. Variance Stabilization and Robust Normalization for Microarray Gene Expression Data. Weights and Fragments. XQS/MD* Crypt as a Means of Education and Computation. Author's Index. Keyword Index.
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18. Applied Multivariate Statistical Analysis [2003]
 Härdle, Wolfgang.
 Berlin, Heidelberg : Springer Berlin Heidelberg, 2003.
 Description
 Book — 1 online resource (iv, 486 pages) Digital: text file.PDF.
 Summary

 I Descriptive Techniques: Comparison of Batches. II Multivariate Random Variables: A Short Excursion into Matrix Algebra
 Moving to Higher Dimensions
 Multivariate Distributions
 Theory of the Multinormal
 Theory of Estimation
 Hypothesis Testing. III Multivariate Techniques: Decomposition of Data Matrices by Factors
 Principal Components Analysis
 Factor Analysis
 Cluster Analysis
 Discriminate Analysis. Correspondence Analysis. Canonical Correlation Analysis. Multidimensional Scaling. Conjoint Measurement Analysis. Application in Finance. Highly Interactive, Computationally Intensive Techniques. A: Symbols and Notations. B: Data. Bibliography. Index.
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19. Nonparametric and Semiparametric Models [2004]
 Hardle, Wolfgang.
 Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint : Springer, 2004.
 Description
 Book — 1 online resource (XXVIII, 300 pages) Digital: text file.PDF.
 Summary

 Introduction
 Nonparametric Models
 Semiparametric Models.
 Franke, Jürgen.
 Berlin, Heidelberg : Springer Berlin Heidelberg, 2004.
 Description
 Book — 1 online resource (xxiii, 425 pages) Digital: text file.PDF.
 Summary

 1Option Pricing. Statistical Model of Financial Time Series. Selected Financial Applications.
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