Computing and Graphics in Statistics

Computing and Graphics in Statistics

by ANDREAS BUJA
     
 

ISBN-10: 0387976337

ISBN-13: 9780387976334

Pub. Date: 01/28/1997

Publisher: Springer New York

Computing and Graphics in Statistics presents issues that arise in the development of integrated statistical software systems which have led to the adaptation of ideas from computer science, particularly programming environments, programming paradigms, and artificial intelligence. Examples are given that distinguish statistics from many physical sciences such as

Overview

Computing and Graphics in Statistics presents issues that arise in the development of integrated statistical software systems which have led to the adaptation of ideas from computer science, particularly programming environments, programming paradigms, and artificial intelligence. Examples are given that distinguish statistics from many physical sciences such as genuine high-dimensional objects - multivariate data or functions of many variables. Demonstrates automatic methods for finding reasonable domains and ranges for plotting univariate functions. Deals with computer intensive methodology including the bootstrap method, Bayesian inferrence and its associated integration problems.

Product Details

ISBN-13:
9780387976334
Publisher:
Springer New York
Publication date:
01/28/1997
Series:
IMA Volumes in Mathematics and Its Appli, #36
Pages:
279

Table of Contents

An interface between S and Mathematica.- Looking at large data sets using binned data plots.- Integrating a robust option into a multiple regression computing environment.- Algorithm development for nonstandard least squares problems. Repeated categorical responses with missing values: A case study.- Importance sampling for Bayesian estimation.- A software model for statistical graphics.- Geometric abstractions for constrained optimization of layouts.- Construction of line densities for parallel coordinate plots.- GLIMPSE, a knowledge-based front end for GLIM.- GENSTAT as a computing environment.- Situations, summaries and model objects.- On estimation and visualization of higher dimensional surfaces.- Odds plots: A graphical aid for finding associations between views of a data set.- A Shastic Approach to Load Balancing in Coarse Grain Parallel Computers.- Algorithms for choosing the domain and range when plotting a function.- High-dimensional depth-cuing for guided tours of multivariate data.- Towards a structured data analysis environment: A cognition-based design.

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