Porównanie modeli regresji logistycznej odpornych na problem całkowitego rozdzielenia

• Authors: Kamil Fijorek

Title variants

EN

Logistic Regression with Completely Separtated Data: A Comparison of Two Methods

• Languages of publication: PL

Abstracts

EN

Applying logistic regression to small-sized data sets very often leads to the problem of complete separation. Generally speaking, separation is caused by a linear combination of covariates that perfectly separates successes (events) from failures (non-events). In such cases, results obtained by maximum likelihood method should not be trusted, since at least one parameter estimate diverges to infinity. A systematic review of the literature resulted in two theoretically sound procedures which always arrive at finite estimates, i.e. those of H. Heinze and S. Schemper (2002) and also R. Rousseeuw and C. Christmann (2003). The main goal of the paper is to compare them.

Keywords

EN

LOGISTIC REGRESSION; SMALL SAMPLE

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie
• Journal: Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie
• Year: 2012
• Issue: 884
• Pages: 143-156

Contributors

author

Kamil Fijorek

• Uniwersytet Ekonomiczny w Krakowie, Katedra Statystyki, ul. Rakowicka 27, 31-510 Kraków, Poland

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