W. Hardle – författare

Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be­ cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica­ tion. They have none-the-less stressed the importance of such optimali­ ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum­ ed model.

Most statistical applications involve computational work with data stored on a computer. The mechanics of interaction with the data is a function of the sta­ tistical computing environment. This application guide is intended for slightly experienced statisticians in computer-aided data analysis who desire to learn advanced applications in various fields of statistics. The prerequisities for XploRe-the statistic computing environment-are an introductory course in statistics or mathematics. This book is designed as an e-book which means that the text contained in here is also available as an integrated document in HTML and PDF format. The reader of this application guide should therefore be familiar with the basics of Acrobat Reader and of HTML browsers in order to profit from direct computing possibilities within this document. The quantlets presented here may be used together with the academic edi­ tion of XploRe (http://www.i-xplore.de) or via the XploRe Quantlet Client (XQC) on http://www.xplore-stat.de. The book comes together with a CD­ Rom that contains the XploRe Quantlet Server (XQS) and the full Auto Pilot Support System (APSS). With this e-book bundle one may directly try the application without being dependent on a specific software version. The quantlets described in the book can be accessed via the links included All executable quantlets are denoted by the symbol . Some in the text.

It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This learning guide is intended for beginners in computer-aided statistical data analysis. The prerequisites for XploRe - the statistical computing environment - are an introductory course in statistics or mathematics. The reader of this book should be familiar with basic elements of matrix algebra and the use of HTML browsers. This guide is designed to help students to XploRe their data, to learn (via data interaction) about statistical methods and to disseminate their findings via the HTML outlet. The XploRe APSS (Auto Pilot Support System) is a powerful tool for finding the appropriate statistical technique (quantlet) for the data under analysis. Homogeneous quantlets are combined in XploRe into quantlibs. The XploRe language is intuitive and users with prior experience of other sta­ tistical programs will find it easy to reproduce the examples explained in this guide. The quantlets in this guide are available on the CD-ROM as well as on the Internet. The statistical operations that the student is guided into range from basic one-dimensional data analysis to more complicated tasks such as time series analysis, multivariate graphics construction, microeconometrics, panel data analysis, etc. The guide starts with a simple data analysis of pullover sales data, then in­ troduces graphics. The graphics are interactive and cover a wide range of dis­ plays of statistical data.