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Books on the topic 'Regressionsmodell Regressionsmodell'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Kaufmann, Gwen. Rückwirkungen von Markentransfers auf die Muttermarke: Messmethoden und empirische Befunde. Wiesbaden: Dt. Univ.-Verl., 2005.

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2

Knautz, Henning. Nichtlineare Schätzung des Parametervektors im linearen Regressionsmodell. Frankfurt am Main: A. Hain, 1993.

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3

Schmidt, Karsten. Parameterschätzung im linearen Regressionsmodell bei Vorinformation in Ungleichungsform. Frankfurt am Main: A. Hain, 1992.

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4

Stahlecker, Peter. A priori Information und Minimax-Schätzung im linearen Regressionsmodell. Frankfurt am Main: Athenäum, 1987.

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5

Fry, John M. (John Michael), 1980-, ed. Regression: Linear models in statistics. London: Springer, 2010.

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6

Linden, Martin. Das Risikoverhalten linearer und adaptiver verzerrter Schätzer im linearen Regressionsmodell. Bergisch Gladbach: Eul, 1994.

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7

Neue Ansätze zur Lösung des Problems fehlender Werte im linearen Regressionsmodell. Frankfurt am Main: P. Lang, 1993.

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8

Bährens, Henning. Partielle und simultane Prüfung auf Autokorrelation und Heteroskedastizität der Störvariablen im linearen Regressionsmodell. Göttingen: Unitext, 1992.

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9

Borowiak, Dale S. Model discrimination for nonlinear regression models. New York: M. Dekker, 1989.

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10

Säfken, Benjamin. Semiparametrische Regressionsmodelle in der Versorgungsplanung. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08786-9.

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11

Giesselmann, Marco, and Michael Windzio. Regressionsmodelle zur Analyse von Paneldaten. Wiesbaden: VS Verlag für Sozialwissenschaften, 2012. http://dx.doi.org/10.1007/978-3-531-18695-5.

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12

Windzio, Michael. Regressionsmodelle für Zustände und Ereignisse. Wiesbaden: Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-531-18852-2.

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13

service), SpringerLink (Online, ed. Business Analytics for Managers. New York, NY: Springer Science+Business Media, LLC, 2011.

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14

Konstantinos, Fokianos, ed. Regression models for time series analysis. Hoboken, N.J: Wiley-Interscience, 2002.

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15

Likelihoodbasierte marginale Regressionsmodelle für korrelierte kategoriale Daten. Frankfurt am Main: P. Lang, 1998.

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16

Survival analysis using the SAS system: A practical guide. Cary, NC: SAS Institute, 1995.

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17

Rost, Erika. Regressionsmodelle mit stochastischen Koeffizienten im Kontext der Kalman-Filter-Theorie. Frankfurt [am Main]: R.G. Fischer, 1987.

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18

Kublin, E. Grenzen der klassischen Regressionsmodelle: Modellalternativen für aktuelle Fragestellungen der forstlichen Biometrie. Freiburg i.B: Forstliche Versuchs- und Forschungsanstalt Baden-Württemberg, 1987.

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19

Hastie, Trevor. Generalized additive models: Some applications. Toronto: University of Toronto, Dept. of Statistics, 1985.

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20

Hastie, Trevor. Generalized additive models. London: Chapman and Hall, 1990.

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21

Hastie, Trevor. Generalized additive models. Boca Raton, Fla: Chapman & Hall/CRC, 1999.

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22

Hastie, Trevor. Generalized additive models. Toronto: University of Toronto, Dept. of Statistics, 1985.

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23

Hess, Peter N. Population growth and socioeconomic progress in less developed countries: Determinants of fertility transition. New York: Praeger, 1988.

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24

Population growth and socioeconomic progress in less developed countries: Determinants of fertility transition. New York: Praeger, 1988.

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25

Pilz, Jürgen. Bayesian estimation and experimental design in linear regression models. Chichester: Wiley, 1991.

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26

Hart, Jeffrey D. Nonparametric smoothing and lack-of-fit tests. New York: Springer, 1997.

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27

Bingham, N. H., and John M. Fry. Regression. Springer, 2010.

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28

Schatzverfahren Im Linearen Regressionsmodell Bei Partiellen Und Unscharfen Parameterrestriktionen (Volkswirtschaftliche Analysen). Peter Lang Publishing, 2004.

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29

Regressionsmodelle Zur Analyse Von Paneldaten. Vs Verlag F R Sozialwissenschaften, 2012.

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30

Windzio, Michael. Regressionsmodelle für Zustände und Ereignisse: Eine Einführung. Springer VS, 2013.

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31

Frost, Irasianty. Einfache lineare Regression: Die Grundlage für komplexe Regressionsmodelle verstehen. Springer VS, 2017.

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32

Fehlende Kovariablenwerte Bei Linearen Regressionsmodellen (Texte Und Untersuchungen Zur Germanistik Und Skandinavistik). Peter Lang Publishing, 2001.

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