Unification of Statistical Methods for Continuous and Discrete Data

Emanuel Parzen
Additional contact information
Emanuel Parzen: Texas A&M University, Department of Statistics

A chapter in Computing Science and Statistics, 1992, pp 235-242 from Springer

Abstract: Abstract We propose the concept of unification of statistical methods in order to develop a general philosophy of statistical data analysis. We propose that ways of thinking about statistical ends (goals) and means (procedures) are needed that provide a framework for implementing and comparing several different approaches to a data analysis problem. We believe that unification has benefits which include: existing (often parametric) methods will be better understood; many new (often nonparametric) methods will be developed. The new methods are usually computer intensive; consequently unification of statistical methods can be considered to be closely related to computational statistics. We define computational statistical methods as characterized by being graphics intensive and number crunching intensive.

Keywords: Quantile Function; Quadratic Detector; Comparison Density; Linear Rank Statistic; Parametric Probability Model (search for similar items in EconPapers)
Date: 1992
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2856-1_30

Ordering information: This item can be ordered from
http://www.springer.com/9781461228561

DOI: 10.1007/978-1-4612-2856-1_30

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().