Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data

Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data (1998)

Cached

Download Links

[www.cs.helsinki.fi]

by Henry Tirri , Tomi Silander

Venue:	in Questionnaire Data”. the Annual American Educational Research Association Meeting, SIG Educational Statisticians
Citations:	2 - 0 self

Versions

Version History

Metadata Version 2

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data	user correction - Legacy Corrections
AUTHOR NAME	Henry Tirri	SVM HeaderParse 0.1
AUTHOR AFFIL	Complex Systems Computation Group (CoSCo); P.O.Box 26, Department of Computer Science	SVM HeaderParse 0.2
AUTHOR ADDR	FIN-00014 University of Helsinki, Finland	SVM HeaderParse 0.1
AUTHOR NAME	Tomi Silander	SVM HeaderParse 0.1
AUTHOR AFFIL	Complex Systems Computation Group (CoSCo); P.O.Box 26, Department of Computer Science	SVM HeaderParse 0.2
AUTHOR ADDR	FIN-00014 University of Helsinki, Finland	SVM HeaderParse 0.1
ABSTRACT	this paper we study a new information-theoretically justified approach to missing data estimation for multivariate categorical data. The approach discussed is a model-based imputation procedure relative to a model class (i.e., a functional form for the probability distribution of the complete data matrix), which in our case is the set of multinomial models with some independence assumptions. Based on the given model class assumption an information-theoretic criterion can be derived to select between the different complete data matrices. Intuitively this general criterion, called stochastic complexity, represents the shortest code length needed for coding the complete data matrix relative to the model class chosen. Using this information-theoretic criteria, the missing data problem is reduced to a search problem, i.e., finding the data completion with minimal stochastic complexity. In the experimental part of the paper we present empirical results of the approach using two real data sets, and compare these results to those achived by commonly used techniques such as case deletion and imputating sample averages. Introduction	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	in Questionnaire Data”. the Annual American Educational Research Association Meeting, SIG Educational Statisticians	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	34 found	ParsCit 1.0