Original title: Nonlinear Trend Modeling in the Analysis of Categorical Data
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /6./, Prague (CZ), 2012-09-13 / 2012-09-15
Year: 2012
Language: eng
Abstract: This paper studies various approaches to testing trend in the context of categorical data. While the linear trend is far more popular in econometric applications, a nonlinear modeling of the trend allows a more subtle information extraction from real data, especially if the linearity of the trend cannot be expected and verified by hypothesis testing. We exploit the exact unconditional approach to propose alternative versions of some trend tests. One of them is the test of relaxed trend (Liu, 1998), who proposed a generalization of the classical Cochran- Armitage test of linear trend. A numerical example on real data reveals the advantages of the test of relaxed trend compared to the classical test of linear trend. Further, we propose an exact unconditional test also for modeling association between an ordinal response and nominal regressor. Further, we propose a robust estimator of parameters in the logistic regression model, which is based on implicit weighting of individual observations. We assess the breakdown point of the newly proposed robust estimator.
Keywords: contingency tables; exact unconditional test; log-linear model; logistic regression; robust estimation
Host item entry: International Days of Statistics and Economics, ISBN 978-80-86175-86-7

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://msed.vse.cz/files/2012/Kalina_2012.pdf
Original record: http://hdl.handle.net/11104/0218489

Permalink: http://www.nusl.cz/ntk/nusl-151630


The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers