Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/31003 
Year of Publication: 
2005
Series/Report no.: 
Discussion Paper No. 417
Publisher: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Abstract: 
Common approaches to monotonic regression focus on the case of a unidimensional covariate and continuous dependent variable. Here a general approach is proposed that allows for additive and multiplicative structures where one or more variables have monotone influence on the dependent variable. In addition the approach allows for dependent variables from an exponential family, including binary and Poisson distributed dependent variables. Flexibility of the smooth estimate is gained by expanding the unknown function in monotonic basis functions. For the estimation of coefficients and the selection of basis functions a likelihood based boosting algorithm is proposed which is simply to implement. Stopping criteria and inference are based on AIC-type measures. The method is applied to several data sets.
Subjects: 
monotonic regression
additive models
likelihood based boosting
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
949.52 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.