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Bernd Droge

Name	Bernd Droge
Further information	Humboldt-Univ., Berlin (1993)
Record type	Person
GND-Identifier	171244397
Record ID	171244397
Involved in show all On the Existence of the Moments of the Asymptotic Trace Statistic Panel Cointegration Testing in the Presence of a Time Trend Asymptotic Optimality of Full Cross-validation for Selecting Linear Regression Models On the Minimax Regret Estimation of a Restricted Normal Mean, and Implications On the minimax regret estimation of a restricted normal mean, and implications The relative importance of group-level effects on the performance of German companies Die relative Bedeutung des Einflusses von Firmen- und Industriezweigeffekten auf den Unternehmenserfolg Die relative Bedeutung des Einflusses von Firmen- und Insdustriezweigeffekten auf den Unternehmenserfolg ON SELECTING THE SMOOTHING PARAMETER OF LEAST SQUARES REGRESSION ESTIMATES USING THE MINIMAX REGRET APPROACH On selecting the smoothing parameter of least squares regression estimates using the minimax regret approach HU-PP-Statistik A note on estimating the MSEP in nonlinear regression Bootstrap and cross-validation criteria for selecting linear regression models Some comparisons of criteria for selecting linear regression models

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Katalog der Deutschen Nationalbibliothek

Bernd Droge

On the Existence of the Moments of the Asymptotic Trace Statistic

Panel Cointegration Testing in the Presence of a Time Trend

Asymptotic Optimality of Full Cross-validation for Selecting Linear Regression Models

On the Minimax Regret Estimation of a Restricted Normal Mean, and Implications

On the minimax regret estimation of a restricted normal mean, and implications

The relative importance of group-level effects on the performance of German companies

Die relative Bedeutung des Einflusses von Firmen- und Industriezweigeffekten auf den Unternehmenserfolg

Die relative Bedeutung des Einflusses von Firmen- und Insdustriezweigeffekten auf den Unternehmenserfolg

ON SELECTING THE SMOOTHING PARAMETER OF LEAST SQUARES REGRESSION ESTIMATES USING THE MINIMAX REGRET APPROACH

On selecting the smoothing parameter of least squares regression estimates using the minimax regret approach

HU-PP-Statistik

A note on estimating the MSEP in nonlinear regression

Bootstrap and cross-validation criteria for selecting linear regression models

Some comparisons of criteria for selecting linear regression models