Zur Analyse saisonbehafteter Zeitreihen stehen heutzutage eine größere Anzahl von unterschiedlichen methodischen Ansätzen und auch von fertigen Software-Systemen zur Verfügung. Für die praktischen Anwendungen spielen nicht nur methodische Gesichtspunkte, sondern teilweise auch rein...

In this paper a robust data-driven procedure for decomposing seasonal time series based on a generalized Berlin Method (BV, Berliner Verfahren) as proposed by Heiler and Michels (1994) is discussed. The basic robust algorithm used here is an adaptation of the LOWESS (LOcally Weighted Scatterplot...

In this paper a modified double smoothing bandwidth selector, ^h MDS , based on a new criterion, which combines the plug-in and the double smoothing ideas, is proposed. A self-complete iterative double smoothing rule (^h_IDS ) is introduced as a pilot method. The asymptotic properties of both...

Zur Analyse saisonbehafteter Zeitreihen stehen heutzutage eine größere Anzahl von unterschiedlichen methodischen Ansätzen und auch von fertigen Software-Systemen zur Verfügung. Für die praktischen Anwendungen spielen nicht nur methodische Gesichtspunkte, sondern teilweise auch rein...

A bandwidth selector for local polynomial fitting is proposed following the bootstrap idea, which is just a double smoothing bandwidth selector with a bootstrap variance estimator, defined as the mean squared residuals of a pilot estimate. No simulated resampling is required in this context,...

A data-driven optimal decomposition of time series with trend-cyclical and seasonal components as well as the estimation of derivatives of the trend-cyclical is considered. The time series is smoothed by locally weighted regression with polynomials and trigonometric functions as local...

The problem of selecting bandwidth for nonparametric regression is investigated. The methodology used here is a double-smoothing procedure with data-driven pilot bandwidths. After giving an extension of the asymptotic result of Hardle, Hall and Marron (1992) by transfering the ideas of Jones,...