Sigbert Klinke

Xplore: learning guide

I: First Steps.- 1 Getting Started.- 1.1 Using XploRe.- 1.1.1 Input and Output Windows.- 1.1.2 Simple Computations.- 1.1.3 First Data Analysis.- 1.1.4 Exploring Data.- 1.1.5 Printing Graphics.- 1.2 Quantlet Examples.- 12.1 Summary Statistics.- 1.2.2 Histograms.- 1.2.3 2D Density Estimation.- 1.2.4 Interactive Kernel Regression.- 1.3 Getting Help.- 1.4 Basic XploRe Syntax.- 1.4.1 Operators.- 1.4.2 Variables.- 1.4.3 Variable Names.- 1.4.4 Functions.- 1.4.5 Quantlet files.- 2. Descriptive Statistics.- 2.1 Data Matrices.- 2.1.1 Creating Data Matrices.- 2.1.2 Loading Data Files.- 2.1.3 Matrix Operations.- 2.2 Computing Statistical Characteristics.- 2.1.1 Minimum and Maximum.- 2.2.2 Mean, Variance and Other Moments.- 2.2.3 Median and Quantiles.- 2.2.4 Covariance and Correlation.- 2.2.5 Categorical Data.- 2.2.6 Missing Values and Infinite Values.- 2.3 Summarizing Statistical Information.- 2.3.1 Summarizing Metric Data.- 2.3.2 Summarizing Categorical Data.- 3 Graphics.- 3.1 Basic Plotting.- 3.1.1 Plotting a Data Set.- 3.1.2 Plotting a Function.- 3.1.3 Plotting Several Functions.- 3.1.4 Coloring Data Sets.- 3.1.5 Plotting Lines from Data Sets.- 3.1.6 Several Plots.- 3.2 Univariate Graphics.- 3.2.1 Boxplots.- 3.2.2 Dotplots.- 3.2.3 Bar Charts.- 3.2.4 Quantile-Quantile Plots.- 3.2.5 Histograms.- 3.3 Multivariate Graphics.- 3.3.1 Three-Dimensional Plots.- 3.3.2 Surface Plots.- 3.3.3 Contour Plots.- 3.3.4 Sunflower Plots.- 3.3.5 Linear Regression.- 3.3.6 Bivariate Plots.- 3.3.7 Star Diagrams.- 3.3.8 Scatter-Plot Matrices.- 3.3.9 Andrews Curves.- 3.3.10 Parallel Coordinate Plots.- 3.4 Advanced Graphics.- 3.4.1 Moving and Rotating.- 3.4.2 Simple Predefined Graphic Primitives.- 3.4.3 Color Models.- 3.5 Graphic Commands.- 3.5.1 Controlling Data Points.- 3.5.2 Color of Data Points.- 3.5.3 Symbol of Data Points.- 3.5.4 Size of Data Points.- 3.5.5 Connection of Data Points.- 3.5.6 Label of Data Points.- 3.5.7 Title and Axes Labels.- 3.5.8 Axes Layout.- 4 Regression Methods.- 4.1 Simple Linear Regression.- 4.2 Multiple Linear Regression.- 4.3 Nonlinear Regression.- 5 Teachware Quantlets.- 5.1 Visualizing Data.- 5.2 Random Sampling.- 5.3 The p-Value in Hypothesis Testing.- 5.4 Approximating the Binomial by the Normal Distribution.- 5.5 The Central Limit Theorem.- 5.6 The Pearson Correlation Coefficient.- 5.7 Linear Regression.- II: Statistical Libraries.- 6 Smoothing Methods.- 6.1 Kernel Density Estimation.- 6.1.1 Computational Aspects.- 6.1.2 Computing Kernel Density Estimates.- 6.1.3 Kernel Choice.- 6.1.4 Bandwidth Selection.- 6.1.5 Confidence Intervals and Bands.- 6.2 Kernel Regression.- 6.2.1 Computational Aspects.- 6.2.2 Computing Kernel Regression Estimates.- 6.2.3 Bandwidth Selection.- 6.2.4 Confidence Intervals and Bands.- 6.2.5 Local Polynomial Regression and Derivative Estimation.- 6.3 Multivariate Density and Regression Functions.- 6.3.1 Computational Aspects.- 6.3.2 Multivariate Density Estimation.- 6.3.3 Multivariate Regression.- 7 Generalized Linear Models.- 7.1 Estimating GLMs.- 7.1.1 Models.- 7.1.2 Maximum-Likelihood Estimation.- 7.2 Computing GLM Estimates.- 7.2.1 Data Preparation.- 7.2.2 Interactive Estimation.- 7.2.2 Noninteractive Estimation.- 7.3 Weights & Constraints.- 7.3.1 Prior Weights.- 7.3.2 Replications in Data.- 7.3.3 Constrained Estimation.- 7.4 Options.- 7.4.1 Setting Options.- 7.4.2 Weights and Offsets.- 7.4.3 Control Parameters.- 7.4.4 Output Modification.- 7.5 Statistical Evaluation and Presentation.- 7.5.1 Statistical Characteristics.- 7.5.2 Output Display.- 7.5.3 Significance of Parameters.- 7.5.4 Likelihood Ratio Tests for Comparing Nested Models.- 7.5.5 Subset Selection.- 8 Neural Networks.- 8.1 Feed-Forward Networks.- 8.2 Computing a Neural Network.- 8.2.1 Controlling the Parameters of the Neural Network.- 8.2.2 The Resulting Neural Network.- 8.3 Running a Neural Network.- 8.3.1 Implementing a Simple Discriminant Analysis.- 8.3.2 Implementing a More Complex Discriminant Analysis.- 9 Time Series.- 9.1 Time Domain and Frequency Domain Analysis.- 9.1.1 Autocovariance and Autocorrelation Function.- 9.1.2 The Periodogram and the Spectrum of a Series.- 9.2 Linear Models.- 9.2.1 Autoregressive Models.- 9.2.2 Autoregressive Moving Average Models.- 9.2.3 Estimating ARMA Processes.- 9.3 Nonlinear Models.- 9.3.1 Several Examples of Nonlinear Models.- 9.3.2 Nonlinearity in the Conditional Second Moments.- 9.3.3 Estimating ARCH Models.- 9.3.4 Testing for ARCH.- 10 Kalman Filtering.- 10.1 State-Space Models.- 10.1.1 Examples of State-Space Models.- 10.1.2 Modeling State-Space Models in XploRe.- 10.2 Kalman Filtering and Smoothing.- 10.3 Parameter Estimation in State-Space Models.- 11 Finance.- 11.1 Outline of the Theory.- 11.1.1 Some History.- 11.1.2 The Black-Scholes Formula.- 11.2 Assets.- 11.2.1 Stock Simulation.- 11.2.2 Stock Estimation.- 11.2.3 Stock Estimation and Simulation.- 11.3 Options.- 11.3.1 Calculation of Option Prices and Implied Volatilities.- 11.3.2 Option Price Determining Factors.- 11.3.3 Greeks.- 11.4 Portfolios and Hedging.- 11.4.1 Calculation of Arbitrage.- 11.4.2 Bull-Call Spreads.- 12 Microeconometrics and Panel Data.- 12.1 Limited-Dependent and Qualitative Dependent Variables.- 12.1.1 Probit, Logit and Tobit.- 12.1.2 Single Index Models.- 12.1.3 Average Derivatives.- 12.1.4 Average Derivative Estimation.- 12.1.5 Weighted Average Derivative Estimation.- 12.1.6 Average Derivatives and Discrete Variables.- 12.1.7 Parametric versus Semiparametric Single Index Models.- 12.2 Multiple Index Models.- 12.2.1 Sliced Inverse Regression.- 12.2.2 Testing Parametric Multiple Index Models.- 12.3 Self-Selection Models.- 12.3.1 Parametric Model.- 12.3.2 Semiparametric Model.- 12.4 Panel Data Analysis.- 12.4.1 The Data Set.- 12.4.2 Time Effects.- 12.4.3 Model Specification.- 12.4.4 Estimation.- 12.4.5 An Example.- 12.5 Dynamic Panel Data Models.- 12.6 Unit Root Tests for Panel Data.- 13 Extreme Value Analysis.- 13.1 Extreme Value Models.- 13.2 Generalized Pareto Distributions.- 13.3 Assessing the Adequacy: Mean Excess Functions.- 13.4 Estimation in EV Models.- 13.4.1 Linear Combination of Ratios of Spacings (LRS).- 13.4.2 ML Estimator in the EV Model.- 13.4.3 ML Estimator in the Gumbel Model.- 13.5 Fitting GP Distributions to the Upper Tail.- 13.6 Parametric Estimators for GP Models.- 13.6.1 Moment Estimator.- 13.6.2 ML Estimator in the GP Model.- 13.6.3 Pickands Estimator.- 13.6.4 Drees-Pickands Estimator.- 13.6.5 Hill Estimator.- 13.6.6 ML Estimator for Exponential Distributions.- 13.6.7 Selecting a Threshold by Means of a Diagram.- 13.7 Graphical User Interface.- 13.8 Example.- 14 Wavelets.- 14.1 Quantlib twave.- 14.1.1 Change Basis.- 14.1.2 Change Function.- 14.1.3 Change View.- 14.2 Discrete Wavelet Transform.- 14.3 Function Approximation.- 14.4 Data Compression.- 14.5 Two Sines.- 14.6 Frequency Shift.- 14.7 Thresholding.- 14.7.1 Hard Thresholding.- 14.7.2 Soft Thresholding.- 14.7.3 Adaptive Thresholding.- 14.8 Translation Invariance.- 14.9 Image Denoising.- III: Programming.- 15 Reading and Writing Data.- 15.1 Reading and Writing Data Files.- 15.2 Input Format Strings.- 15.3 Output Format Strings.- 15.4 Customizing the Output Window.- 15.4.1 Headline Style.- 15.4.2 Layer Style.- 15.4.3 Line Number Style.- 15.4.4 Value Formats and Lengths.- 15.4.5 Saving Output to a File.- 16 Matrix Handling.- 16.1 Basic Operations.- 16.1.1 Creating Matrices and Arrays.- 16.1.2 Operators for Numeric Matrices.- 16.2 Comparison Operators.- 16.3 Matrix Manipulation.- 16.3.1 Extraction of Elements.- 16.3.2 Matrix Transformation.- 16.4 Sums and Products.- 16.5 Distance Function.- 16.6 Decompositions.- 16.6.1 Spectral Decomposition.- 16.6.2 Singular Value Decomposition.- 16.6.3 LU Decomposition.- 16.6.4 Cholesky Decomposition.- 16.7 Lists.- 16.7.1 Creating Lists.- 16.7.2 Handling Lists.- 16.7.3 Getting Information on Lists.- 17 Quantlets and Quantlibs.- 17.1 Quantlets.- 17.2 Flow Control.- 17.2.1 Local and Global Variables.- 17.2.2 Conditioning.- 17.2.3 Branching.- 17.2.4 While-Loop.- 17.2.5 Do-Loop.- 17.2.6 Optional Input and Output in Procedures.- 17.2.7 Errors and Warnings.- 17.3 User Interaction.- 17.4 APSS.- 17.5 Quantlibs.

MD*Book online & e-stat: Generating e-stat Modules from LATEX

We describe the basics of MD*Book and MD*Book online which we use together with XploRe to create interactive electronic books. We generate different output formats (Postscript, PDF, HTML and MM*Stat-like formats) for the internet as well as for CD and/or local installation.

e-Learning Statistics - A Selective Review

Modern computing equipment is present at schools and universities at all levels of education. In the statistical sciences computers offer great opportunities to enrich the learning process by the means of e.g. animations, software integration or on-the-fly computations. A personal review of different e-learning platforms for statistics is done in this paper. This review reveals facts that could be taken into account for future e-learning platforms in statistics. One of the most striking discoveries of our analysis is that students of statistics actually do not use electronic media in the desired frequency and actually rely more on print media such as books,copies of slides, etc.

A New Generation of a Statistical Computing Environment on the Net

With the availability of the net a new generation of computing environments has to be designed for a large scale of statistical tasks ranging from data analysis to highly interactive operations. It must combine the flexibility of multi window desktops with standard operations and interactive user driven actions. It must be equally well suited for first year students and for high demanding researchers. Its design must has various degrees of flexibility that allow to address different levels of user groups. We present here some ideas how a new generation of a computing environment can be used as a student front end tool for teaching elementary statistics as well as a research device for highly computer intensive tasks, e.g. for semiparametric analysis and bootstrapping.

Embedding R in the Mediawiki

Teaching statistics to students in our area of economics and educational science often brings about the problem that students have either forgottentheir statistical knowledge, or have taken different classes than the ones we offer in basic statistics. We therefore need some kind of statistical dictionary where we, as teachers, can refer to a common base and where students can look up specific terms. The Wikipedia - a general online encyclopaedia - compelled us to use a wiki for our dictionary. While the Wikipedia contains a large number of statistical terms, these are often too long and detailed to be visual displayed in lectures very well and some more specific terms are not included.

Statistical User Interfaces

A statistical user interface is an interface between a human user and a statistical software package. Whenever we use a statistical software package we want to solve a specific statistical problem. But very often at first it is necessary to learn specific things about the software package. Everyone of us knows about the ?religious wars? concerning the question which statistical software package/method is the best for a certain task; see Marron (1996) and Cleveland and Loader (1996) and related internet discussions. Experienced statisticians use a bunch of different statistical software packages rather than a single one; although all of the major companies (at least the marketing departments) tell us that we only need their software package.

Visualizing exploratory factor analysis models

Exploratory factor analysis (EFA) is an important tool in data analyses, particularly in social science. Usually four steps are carried out which contain a large number of options. One important option is the number of factors and the association of variables with a factor. Our tools aim to visualize various models with different numbers in parallel of factors and to analyze which consequences a specific option has.We apply our method to data collected at the School of Business and Economics for evaluation of lectures by students. These data were analyzed by Zhou (2004) and Reichelt (2007).

Connected teaching of statistics

Statistics is considered to be a difficult science since it requires a variety of skills including handling of quantitative data, graphical insights as well as mathematical ability. Yet ever increasing special knowledge of statistics is demanded since data of increasing complexity and size need to be understood and analyzed. Although this changing demand on educated statisticians is visible, our methods of teaching statistics follow essentially the ideas developed by our grandfathers in the fifties. An attractive and powerful new way of incorporating todays and future demands is via tools based on an intra- or the internet. In this article we suggest a set of criteria for effective web based teaching and propose the first net based approach to meet these criteria.

Teaching wavelets in XploRe

Teachware is a set of computer software tools for computeraided interactive teaching of certain knowledge elements. The construction of teachware for statistical knowledge is a rather young field since it heavily depends on data structures and graphical interaction possibilities. In this paper we present a teachware module for XploRe - a statistical computing environment. We focus on the situation of teaching wavelets, a technique for adaptation of spatial inhomogeneity.

Classification and Regression Trees

We will call an estimator for the regression function defined by the CART methodology a regression tree. The word CART means classification and regression tree. This chapter will focus only on the regression trees.