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Farrukh Jamal, Mohammad A. Aljarrah, M. H. Tahir and M. Arslan Nasir

References [1] C. Alexander, G.M. Cordeiro, E.M.M. Ortega and J.M. Sarabia, Generalized beta-generated distributions. Computational Statistics and Data Analysis 56 (2012), 1880-1897. [2] M.A. Aljarrah, C. Lee and F. Famoye, On generating T-X family of distributions using quantile functions. Journal of Statistical Distributions and Applications 1 (2014), Art. 2. Doi: 10.1186/2195-5832-1-2 [3] M. Almheidat, F. Famoye and C. Lee, Some generalized families of Weibull distribution: Prop- erties and applications

Open access

Farrukh Jamal, M. H. Tahir, Morad Alizadeh and M. A. Nasir

-111. [26] M. A Nasir, M. H. Tahir, F. Jamal and G. Ozel, A new generalized Burr family of distributions for the lifetime data. Journal of Statistics Applications and Probability 6(2) (2017), 401-417. [27] M. A Nasir , M. Aljarrah, F. Jamal and M. H. Tahir, A new generalized Burr family of distributions based on quantile function. Journal of Statistics Applications and Probability 6(3) (2017), 1-14. [28] J. G. Surles and W. J. Padgett, Some properties of a scaled Burr type X distribution, to appear in the Journal of Statistical Planning and

Open access

Grażyna Trzpiot

References Buchinsky M. (1998), Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research, Journal of Human Resources, 33 Chambers R., Pratesi M., Salvati N., Tzavidis N. (2005), Small Area Estimation: spatial information and M- quantile regression to estimate the average production of olive per farm, Wp 279, Dipatimento di Statistica e Matematica Applicata all’Economia, Universita di Pisa Chernozhukov V., Hansen Ch.. (2006), Instrumental Quantile Regression Inference

Open access

El Hadj Mokhtari, Boualem Remini and Saad Abdelamir Hamoudi

Abstract

The purpose of this study is to make a hydrologic modelling type of rain–flow on watershed of wadi Cheliff-Ghrib, by means of HEC-HMS model. Afterwards, this model is used to predict hydrologic response of the basin to the climate changes scenarios and land use. The model calibration was made in two phases; the first one is to select events, formalism of transfer function and appropriate NRCS downpour. The second is to deduce optimised parameters set which is used in validation. By using optimised parameters set, we were able to predict impact of quantiles downpours, changes in land use due to urbanisation, deforestation and reforestation on the peak flow and on runoff volume. Towards the end, we reconfirmed that influence of land use decreases for extreme storms.

Open access

Filip Edmund Gęstwicki and Ewa Wędrowska

Abstract

The increase in income and wealth inequality observed in the last decade of the twentieth century and the first decade of the twenty-first century is the subject of many analyses and discussions. Research shows that major changes in household incomes in Poland took place in the early years of transition (1990–1992), known as a ‘revolution in income’. The article focuses on the assessment of the degree of household income inequality after the Poland’s accession to the European Union. The most commonly used measures in income inequality studies are the measures of inequality based on the Lorenz function – a popular Gini coefficient and the Schutz ratio, measures using the concept of entropy, measures based on welfare function, or measures based on income distribution quantiles. The article proposes the possibility of broadening the measuring spectrum of income inequality analysis of the Csiszár’s divergence measures. The main research objective of the article is to assess the divergence in the distribution of household equivalent disposable income in Poland in the years 2005–2013. The data used in the analysis come from the European Survey on Income and Living Conditions (EU-SILC).

Open access

Ladislav Gaál, Ján Szolgay, Silvia Kohnová, Kamila Hlavčová and Alberto Viglione

Inclusion of historical information in flood frequency analysis using a Bayesian MCMC technique: a case study for the power dam Orlík, Czech Republic

The paper deals with at-site flood frequency estimation in the case when also information on hydrological events from the past with extraordinary magnitude are available. For the joint frequency analysis of systematic observations and historical data, respectively, the Bayesian framework is chosen, which, through adequately defined likelihood functions, allows for incorporation of different sources of hydrological information, e.g., maximum annual flood peaks, historical events as well as measurement errors. The distribution of the parameters of the fitted distribution function and the confidence intervals of the flood quantiles are derived by means of the Markov chain Monte Carlo simulation (MCMC) technique.

The paper presents a sensitivity analysis related to the choice of the most influential parameters of the statistical model, which are the length of the historical period h and the perception threshold X 0. These are involved in the statistical model under the assumption that except for the events termed as ‘historical’ ones, none of the (unknown) peak discharges from the historical period h should have exceeded the threshold X 0. Both higher values of h and lower values of X 0 lead to narrower confidence intervals of the estimated flood quantiles; however, it is emphasized that one should be prudent of selecting those parameters, in order to avoid making inferences with wrong assumptions on the unknown hydrological events having occurred in the past.

The Bayesian MCMC methodology is presented on the example of the maximum discharges observed during the warm half year at the station Vltava-Kamýk (Czech Republic) in the period 1877-2002. Although the 2002 flood peak, which is related to the vast flooding that affected a large part of Central Europe at that time, occurred in the near past, in the analysis it is treated virtually as a ‘historical’ event in order to illustrate some crucial aspects of including information on extreme historical floods into at-site flood frequency analyses.

Open access

Zelimir Kurtanjek

Statistical Software, 2011, 39(5), 1-13. URL http://www.jstatsoft.org/v39/i05/. 18. Meinshausen N., Quantile Regression Forests, 2016; https://CRAN.R-project.org/package=quantregForest 19. McGill R, Tukey JW, Larsen WA. Variations of Box Plots, AM STAT. The American Statistician 1978; (32): 12-16. 20. Gregory R. Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, Lumley T, Maechler M, Magnusson R, Moeller S, Schwartz M, Venables B, 2016, URL https://CRAN.R-project.org/package=gplots 21. Zhang J

Open access

Mateusz Buczyński and Marcin Chlebus

United Kingdom Inflation. Econometrica, 50 (4), 987-1007. Engle, R.F. (2001). GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics. Journal of Economic Perspectives, 15 (4), 157-168. Engle, R.F. (2004). Risk and Volatility: Econometric Models and Financial Practice. The American Economic Review, 94 (3), 405-420. Engle, R.F., Manganelli, S. (2001). Value at Risk Models in Finance. European Central Bank Working Papers, 75. Engle, R F., Manganelli, S. (2004). CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles

Open access

Jiří Mazurek

References Cobb, C. W., Douglas, P. H. (1928), A Theory of Production, ‘American Economic Review’, 18. Dissanayake, J., Sim, N. (2010), Cross Country Empirical Investigation of the Aggregate Production Function Using Panel Quantile Regression. [In]: Proceedings of the 39th Australian Conference of Economists (ACE 10). Duffy, J., Papageorgiou, C. (2000), A Cross-Country Empirical Investigation of the Aggregate Production Function Specification, ‘Journal of Economic Growth’, 5. Eurostat. (2016

Open access

Yves G. Berger and Juan F. Munoz

Population Level Information: An Empirical-Likelihood-Based Approach.” Journal of the Royal Statistical Society - Series B (Statistical Methodology) 70: 311-328. DOI: http://dx.doi.org/10.1111/j.1467-9868.2007.00637.x. Chen, J. and C. Wu. 2002. “Estimation of Distribution Function and Quantiles Using Model-Calibrated Pseudo Empirical Likelihood Method.” Statistica Sinica 12: 1223-1239. Deville, J.C. and C.-E. Särndal. 1992. “Calibration Estimators in Survey Sampling.” Journal of the American Statistical Association 87: 376-382. DOI: http://dx.doi.org/ 10