A Monte Carlo simulation was used to generate data for a comparison of five robust regression estimation methods with ordinary least squares (OLS) under 36 different outlier data configurations. Two of the robust estimators, Least Absolute Value (LAV) estimation and MM estimation, are...

Diese empirische Arbeit untersucht Determinanten des Renteneintritts. Sie basiert auf einem Optionswertmodell, um die Bedeutung finanzieller Überlegungen für ein Aufschieben des Renteneintritts zu analysieren. Zusätzlich wird der Einfluss institutioneller Rahmenbedingungen betrachtet. Ein neu...

Ziel dieser Arbeit ist die Untersuchung der Bedeutung der Spezifikation für Ratingmodelle zur Prognose von Kreditausfallwahrscheinlichkeiten. Ausgehend von dem in der Bankenpraxis etablierten Logit-Modell werden verschiedene Modellerweiterungen diskutiert und hinsichtlich ihrer Eigenschaften...

Stochastic volatility (SV) models provide a means of tracking and forecasting the variance of financial asset returns. While SV models have a number of theoretical advantages over competing variance modelling procedures they are notoriously difficult to estimate. The distinguishing feature of...

In this paper I derive the asymptotics of the exact, Euler, and Milstein MLestimators for diffusion models, including general nonstationary diffusions. Thoughthere have been many estimators for the diffusion model, their asymptotic propertieswere generally unknown. This is especially true for...

An important empirical fact in financial market is that return distributions are often skewed and heavy-tailed. This paper employs maximum likelihood estimation to estimate the five parameters of generalized hyperbolic distribution, a highly flexible heavy-tailed distribution. The estimation...

Despite their success and widespread usage in industry and business, ES methods have received little attention from the statistical community. We investigate three types of statistical models that have been found to underpin ES methods. They are ARIMA models, state space models with multiple...

This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of...

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential...

This paper presents a new stochastic multidimensional scaling procedure for the analysis of three-mode, three-way pick any/ J data. The method provides either a vector or ideal-point model to represent the structure in such data, as well as “floating” model specifications (e.g., different...