SOME PROPERTIES OF SPATIAL QUANTILES

Authors

  • Grażyna Trzpiot University of Economics in Katowice, Department of Demography and Economics Statistics.

Keywords:

Multivariate quantile analysis, spatial quantiles, spatial quantiles estimators.

Abstract

Conditional quantiles are required in various economic, biomedical or industrial problems. Lack of objective basis for ordering multivariate observations is a major problem in extending the notion of quantiles or conditional quantiles (also called regression quantiles) in a multidimensional setting. We present characterisations of the spatial quantiles and the corresponding estimators. Nonparametric inference is very naturally quantile-based, and in recent years various notions of multivariate quantiles the spatial quantile function for whose sample version have been recalled.

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References

Abdous B., Theodorescu R. (1992), Note on the spatial quantile of a random vector, “Statistics and Probability Letter”, 13, pp. 333-336.
Google Scholar

Barnett V. (1976), The ordering of multivariate data (with comments), “Journal of Royal Statistical Society”, Ser. A, 139, pp. 318-354.
Google Scholar

Chakraborty B. (2001), On affine equivariant multivariate quantiles, T”he Institute of Statistical Mathematics”, 53, pp. 380-403.
Google Scholar

Chaudhuri P. (1992a), Multivariate location estimation using extension of R-estimates through U-statistics type approach, “Annals of Statistics”, 20, pp. 897-916.
Google Scholar

Chaudhuri P. (1992b), Generalized regression quantiles: Forming a useful toolkit for robust linear regression, (in:) Dodge Y. (ed.), L1 Statistical Analysis and Related Methods, Amsterdam: North-Holland, pp. 169-185.
Google Scholar

Chaudhuri P. (1996), On a geometric notation of quantiles for multivariate data, “Journal of the American Statistical Association”, 91, pp. 862-872.
Google Scholar

Chaouch M., Gannoun A., Saracco J. (2008), Conditional Spatial Quantile: Characterization and Nonparametric Estimation, Cahier Du Gretha – 10.
Google Scholar

Dabo-Niang S., Thiam (2010), Robust quantile estimation and prediction for spatial processes, “Statistics and Probability Letters”, 80, pp. 1447-1458.
Google Scholar

Eddy W. F. (1985), Ordering of Multivariate Data, (in:) Billard L. (ed.), Computer Science and Statistics: The Interface, Amesterdam: North-Holland, pp. 25-30.
Google Scholar

Efron B. (1991), Regression percentiles using asymmetric squared error loss, “Statistica Sinica”, 1,
Google Scholar

pp. 93-125.
Google Scholar

Ferguson T. (1967), Mathematical Statistics: A Decision Theory Approach, Academic Press: New York.
Google Scholar

Koenker R., Basset G. (1978), Regression Quantiles, “Econometrica”, 46, pp. 33-50.
Google Scholar

Koenker R., Portnoy S. (1987), L Estimation for linear models, “Journal of the American statistical Association”, 82, pp. 851-857.
Google Scholar

Oja H. (1983), Descriptive Statistics for Multivariate Trimming, “Statistics and Probability Letters”, 1, pp. 327-332.
Google Scholar

Plackett R. L. (1976), Comment on Ordering of multivariate data by V. Barnett, “Journal of the Royal Statistical Society”, Ser. A, 139, pp. 344-346.
Google Scholar

Reiss R. D. (1989), Approximation distributions of order statistics with applications to nonparametric statistics, New York: Springer.
Google Scholar

Serfling R. (1980), Approximation theorem of mathematical statistics, New York: John Wiley.
Google Scholar

Serfling R. (2002), Quantile functions for multivariate analysis: approaches and applications, “Annals of Statistics”, 25, pp. 435-477.
Google Scholar

Trzpiot G. (2008), The Implementation of Quantile Regression Methodology in VaR Estimation, “Studies and Researches of Faculty of Economics and Management University of Szczecin”.
Google Scholar

Trzpiot G. (2009a), Quantile Regression Model versus Factor Model Estimation, “Financial Investments and Insurances”, Vol 60.
Google Scholar

Trzpiot G. (2009b), Application weighted VaR in capital allocation, “Polish Journal of Environmental Studies”, Vol 18, 5B.
Google Scholar

Trzpiot G. (2009c), Estimation methods for quantile regression, “Economics Studies”, 53.
Google Scholar

Trzpiot G. (2010), Quantile Regression Model of Return Rate Relation – Volatility for Some Warsaw Stock Exchange Indexes, “Finances, Financial Markets and Insurances. Capital Market”, Vol 28, pp. 61-76.
Google Scholar

Trzpiot G. (2011a), Bayesian Quantile Regression, „Studia Ekonomiczne”, Zeszyty Naukowe nr 65, pp. 33-44.
Google Scholar

Trzpiot G. (2011b), Some tests for quantile regression models, “Acta Universitatis Lodziensis Folia Economica”, 255, pp. 125-135.
Google Scholar

Trzpiot G. (2012), Spatial quantile regression, “Comparative Economic Research. Central and Eastern Europe”, vol. 15, no 4, pp. 265-279.
Google Scholar

Trzpiot G. (2013), Properties of transformation quantile regression model, “Acta Universitatis Lodziensis Folia Economica”, 285, pp. 125-137.
Google Scholar

Zuo Y., Serfling R. (2000), General notions of statistical depth function, “Annals of Statistics”, 28, pp. 461-482.
Google Scholar

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Published

2015-05-18

How to Cite

Trzpiot, G. (2015). SOME PROPERTIES OF SPATIAL QUANTILES. Acta Universitatis Lodziensis. Folia Oeconomica, 5(307). Retrieved from https://www.czasopisma.uni.lodz.pl/foe/article/view/299

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Section

Regional econometrics