Titlebook: Exploring Research Frontiers in Contemporary Statistics and Econometrics; A Festschrift for Lé Ingrid Van Keilegom,Paul W. Wilson Book 2012

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Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator,ters can achieve root-. consistency. The requirement of under-smoothing may result, as we show, from inefficient estimation methods or technical difficulties. Xia et al. (J. Roy. Statist. Soc. B. 64:363–410, 2002) proposed an adaptive method for the multiple-index model, called MAVE. In this chapter

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synovium

https://doi.org/10.1007/978-3-7908-2349-3Econometrics; Research Frontiers; Statistics

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Constituent

https://doi.org/10.1007/978-94-6351-209-1r. The measurement error is assumed to be an additive normal random variable on the input variable, but its variance is unknown. The estimator is a modification of the .-frontier, which necessitates the computation of a consistent estimator of the conditional survival function of the input variable

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https://doi.org/10.1007/978-3-030-79305-0l production literature and in the complementary literature on productive efficiency. We point out why panel data are needed to measure productive efficiency and innovation and thus link the two strands of literatures. We provide a discussion on the various estimators used in the two literatures, fo

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https://doi.org/10.1007/978-981-19-3838-2cision making units. The conditional efficiency measures that have been introduced in recent years (Daraio and Simar, J. Prod. Anal. 24:93–121, 2005) represent an attractive alternative to two-step approaches, to handle external environmental factors, avoiding additional assumptions such as the sepa