Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Nadaraya’s Estimates for Large Quantiles and Free Disposal Support Curves Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

3. Nonparametric Frontier Estimation from Noisy Data

verfasst von : Maik Schwarz, Sébastien Van Bellegem, Jean-Pierre Florens

Erschienen in: Exploring Research Frontiers in Contemporary Statistics and Econometrics

Verlag: Physica-Verlag HD

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A new nonparametric estimator of production frontiers is defined and studied when the data set of production units is contaminated by measurement error. 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 m-frontier, which necessitates the computation of a consistent estimator of the conditional survival function of the input variable given the output variable. In this paper, the identification and the consistency of a new estimator of the survival function is proved in the presence of additive noise with unknown variance. The performance of the estimator is also studied using simulated data.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Production Efficiency versus Ownership: The Case of China
Nächstes Kapitel Estimating Frontier Cost Models Using Extremiles
Literatur
Bigot, J., & Van Bellegem, S. (2009). Log-density deconvolution by wavelet thresholding. Scandinavian Journal of Statistics, 36, 749–763.MATHCrossRef
Butucea, C., & Matias, C. (2005). Minimax estimation of the noise level and of the deconvolution density in a semiparametric deconvolution model. Bernoulli, 11, 309–340.MathSciNetMATHCrossRef
Butucea, C., Matias, C., & Pouet, C. (2008). Adaptivity in convolution models with partially known noise distribution. Electronic Journal of Statistics, 2, 897–915.MathSciNetCrossRef
Carroll, R., & Hall, P. (1988). Optimal rates of convergence for deconvolving a density. Journal of the American Statistical Association, 83, 1184–1186.MathSciNetMATHCrossRef
Cazals, C., Florens, J. P., & Simar, L. (2002). Nonparametric frontier estimation: a robust approach. Journal of Econometrics, 106, 1–25.MathSciNetMATHCrossRef
Daouia, A., Florens, J., & Simar, L. (2009). Regularization in nonparametric frontier estimators (Discussion Paper No. 0922). Université catholique de Louvain, Belgium: Institut de statistique, biostatistique et sciences actuarielles.
Daskovska, A., Simar, L., & Van Bellegem, S. (2010). Forecasting the Malmquist productivity index. Journal of Productivity Analysis, 33, 97–107.CrossRef
De Borger, B., Kerstens, K., Moesen, W., & Vanneste, J. (1994). A non-parametric free disposal hull (FDH) approach to technical efficiency: an illustration of radial and graph efficiency measures and some sensitivity results. Swiss Journal of Economics and Statistics, 130, 647–667.
Delaigle, A., Hall, P., & Meister, A. (2008). On deconvolution with repeated measurements. The Annals of Statistics, 36, 665–685.MathSciNetMATHCrossRef
Deprins, D., Simar, L., & Tulkens, H. (1984). Measuring labor inefficiency in post offices. In M. Marchand, P. Pestieau, & H. Tulkens (Eds.), The performance of public enterprises: Concepts and measurements (pp. 243–267). Amsterdam: North-Holland.
Fan, J. (1991). On the optimal rate of convergence for nonparametric deconvolution problems. The Annals of Statistics, 19, 1257–1272.MathSciNetMATHCrossRef
Färe, R., Grosskopf, S., & Knox Lovell, C. (1985). The measurements of efficiency of production (Vol. 6). New York: Springer.
Hall, P., & Simar, L. (2002). Estimating a changepoint, boundary, or frontier in the presence of observation error. Journal of the American Statistical Association, 97, 523-534.MathSciNetMATHCrossRef
Johannes, J., & Schwarz, M. (2009). Adaptive circular deconvolution by model selection under unknown error distribution (Discussion Paper No. 0931). Université catholique de Louvain, Belgium: Institut de statistique, biostatistique et sciences actuarielles.
Johannes, J., Van Bellegem, S., & Vanhems, A. (2010). Convergence rates for ill-posed inverse problems with an unknown operator. Econometric Theory. (forthcoming)
Johnstone, I., Kerkyacharian, G., Picard, D., & Raimondo, M. (2004). Wavelet deconvolution in a periodic setting. Journal of the Royal Statistical Society Series B, 66, 547–573.MathSciNetMATHCrossRef
Kneip, A., Park, B., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14, 783–793.MathSciNetCrossRef
Leleu, H. (2006). A linear programming framework for free disposal hull technologies and cost functions: Primal and dual models. European Journal of Operational Research, 168, 340–344.MathSciNetMATHCrossRef
Li, T., & Vuong, Q. (1998). Nonparametric estimation of the measurement error model using multiple indicators. Journal of Multivariate Analysis, 65, 139–165.MathSciNetMATHCrossRef
Meister, A. (2006). Density estimation with normal measurement error with unknown variance. Statistica Sinica, 16, 195–211.MathSciNetMATH
Meister, A. (2007). Deconvolving compactly supported densities. Mathematical Methods in Statistics, 16, 195–211.
Meister, A., Stadtmüller, U., & Wagner, C. (2010). Density deconvolution in a two-level heteroscedastic model with unknown error density. Electronic Journal of Statistics, 4, 36–57.MathSciNetCrossRef
Neumann, M. H. (2007). Deconvolution from panel data with unknown error distribution. Journal of Multivariate Analysis, 98, 1955–1968.MathSciNetMATHCrossRef
Park, B. U., Sickles, R. C., & Simar, L. (2003). Semiparametric efficient estimation of AR(1) panel data models. Journal of Econometrics, 117, 279-311.MathSciNetMATHCrossRef
Park, B. U., Simar, L., & Weiner, C. (2000). The FDH estimator for productivity efficiency scores: asymptotic properties. Econometric Theory, 16, 855–877.MathSciNetMATHCrossRef
Pensky, M., & Vidakovic, B. (1999). Adaptive wavelet estimator for nonparametric density deconvolution. The Annals of Statistics, 27, 2033–2053.MathSciNetMATHCrossRef
Schwarz, M., & Van Bellegem, S. (2010). Consistent density deconvolution under partially known error distribution. Statistics and Probability Letters, 80, 236–241.MathSciNetMATHCrossRef
Seiford, L., & Thrall, R. (1990). Recent developments in DEA: The mathematical programming approach to frontier analysis. Journal of Econometrics, 46, 7–38.MathSciNetMATHCrossRef
Shephard, R. W. (1970). Theory of cost and production functions. Princeton, NJ: Princeton University Press.MATH
Simar, L. (2007). How to improve the performances of DEA/FDH estimators in the presence of noise? Journal of Productivity Analysis, 28, 183–201.CrossRef
Metadaten
Titel
Nonparametric Frontier Estimation from Noisy Data
verfasst von
Maik Schwarz
Sébastien Van Bellegem
Jean-Pierre Florens
Copyright-Jahr
2012
Verlag
Physica-Verlag HD
Buch
Exploring Research Frontiers in Contemporary Statistics and Econometrics

Print ISBN: 978-3-7908-2348-6

Electronic ISBN: 978-3-7908-2349-3

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-7908-2349-3_3