Xplore application guide

W. Hardle, S. Klinke, Z. Hlavka

526 pages, parution le 01/01/2000

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Résumé

This book presents many useful applications of Xplore - the interactive statistical computing environment. It helps users to apply statistics in gradual steps and shows how XploRe can be used for statistical data analysis. The book and the quantlets on CD focus on the analysis of financial markets, microeconometrics, environmental statistics, data exploration, dynamical systems, and regression models. The user-friendly graphics of XploRe provides an effective basis for large-scale statistical analysis, computer intensive research and interactive knowledge discovery. The open architecture and the Auto Pilot Support System (APSSJ guarantee smooth integration of future methods and updated data analysis techniques.The main features of Xplore are:

An extensive set of parametric and nonparametric methods incorporating many state-of-the-art statistical modelling approaches
a high level object oriented programming language
highly interactive graphics for vivid presentation and exploration of data structures
a client/server architecture with a fully capable Java interface
networking facilities providing web-based use of methods and data
dynamic linking with other procedural languages such as C++, Fortran or Pascal
an online help system with a full documentation of all commands available
in XploRe
a set of tutorials to interactively guide you through the use of statistical methods

Xplore is a complete statistical computing environment with a great variety of Quantlets and methods stored in several specialised Quantlibs. These include:

FINANCE for option pricing, stock price simulation and nonlinear time series
GPLM for generalised partial linear models
METRICS microeconometrics Quantlib
VaR (value at risk) estimation with extreme values and historical simulation
modern regression techniques with wavelets and neural networks
nonlinear and multiple time series analysis
KALMAN quantlib for (robust) Kalman filtering
XCLUST for cluster techniques

ContentsI- Regression Models

Quantile Regression
Least Trimmed Squares
Errors-in-Variables Models
Simultaneuos-Equations Models
Hazard Regression
Generalized Partial Linear Models
Generalized Additive Models

II- Data Exploration

Growth Regression and Counterfactual Income Dynamics
Cluster Analysis
Classification and Regression Trees
DPLS: Partial Least Squares Program
Uncovered Interest Parity
Correspondence Analysis

III- Dynamic Statistical Systems

Long-Memory Analysis
ExploRing Persistence in Financial Time Series
Flexible Time Series Analysis
Multiple Time Series Analysis
Robust Kalman Filtering

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	W. Hardle, S. Klinke, Z. Hlavka
Parution	01/01/2000
Nb. de pages	526
Format	15,5 x23,5
Couverture	Broché
Poids	799g
Intérieur	Noir et Blanc
EAN13	9783540675457