podcast.de
Anmeldung
Anmeldung Registrierung
Erweiterte Suche
  1. Startseite
  2. Podcasts
  3. Mathematik, Informatik und Statistik - Open Access LMU - Teil 01/03 Podcast
  4. On Ill-Conditioned Generalized Estimating Equations and Toward Unified Biased Estimation
I address the issue of ill-conditioned regressors within
generalized estimating equations (GEEs). In such a setting,
standard GEE approaches can have problems with: convergence, large
coefficient variances, poor prediction, deflated power of tests,
and in some extreme cases, e.g. functional regressors, may not even
exist. I modify the quasi-likelihood score functions, while
presenting a variety of biased estimators that simultaneously
address the issues of (severe) ill-conditioning and correlated
response variables. To simplify the presentation, I attempt to
unite or link these estimators as much as possible. Some
properties, as well as some guidelines for choosing the meta or
penalty parameters are suggested.

Weitere Episoden

Quantifying overdispersion effects in count regression data

Quantifying overdispersion effects in count regression data

vor 24 Jahren
Graphical chain models for the analysis of complex genetic diseases: an application to hypertension

Graphical chain models for the analysis of complex genetic diseases: an application to hypertension

vor 24 Jahren
Geo-additive models of Childhood Undernutrition in three Sub-Saharan African Countries

Geo-additive models of Childhood Undernutrition in three Sub-Saharan African Countries

vor 24 Jahren
Parametric and Nonparametric Regression with Missing X's - A Review

Parametric and Nonparametric Regression with Missing X's - A Review

vor 24 Jahren
A Selection Model for Bivariate Normal Data, with a Flexible Nonparametric Missing Model and a Focus on Variance Estimates

A Selection Model for Bivariate Normal Data, with a Flexible Nonparametric Missing Model and a Focus on Variance Estimates

vor 24 Jahren
Alle Episoden anzeigen
In Podcasts werben
Schalte jetzt Werbung in Podcasts.

Abonnenten

Teilen