The development and application of models, which take the evolution of network dynamics into account, are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the...

We propose a fully automatic procedure for the construction of irregular histograms. For a given number of bins, the maximum likelihood histogram is known to be the result of a dynamic programming algorithm. To choose the number of bins, we propose two different penalties motivated by recent...

Standardmethoden zur Schätzung von Disparitätsmaßen aus klassierten Daten basieren entweder auf der Bestimmung von Schranken, die den wahren Wert des jeweiligen Disparitätsmaßes einschließen (nichtparametrischer Ansatz) oder aber auf Annahmen bezüglich der den Daten zugrunde liegenden...

This paper explores the theory behind the rich and robust family of »-stable distributions to estimate parameters from financial asset log-returns data. We discuss four-parameter estimation methods including the quantiles, logarithmic moments method, maximum likelihood (ML), and the empirical...

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This study conducts an investigation on the application of classical unit-root tests using parametric tests (the augmented Dickey-Fuller, 1979 – ADF), and nonparametric tests (Phillips and Perron, 1988—PP) to corn and soybean yields in the Delta states using county-level data from 1961 to...

We show that Bertrand et al.'s (QJE, 2015, ) finding of a sharp drop in the relative income distribution within married couples at the point where wives start to earn more than their husbands is unstable across different estimation procedures and varies across contexts. We apply the estimators...

Movement models predict positions of players (or objects in general) over time and are thus key to analyzing spatiotemporal data as it is often used in sports analytics. Existing movement models are either designed from physical principles or are entirely data-driven. However, the former suffers...