Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
MP estimation is a method which concerns estimating of the location parameters when the probabilistic models of observations differ from the normal distributions in the kurtosis or asymmetry. The system of Pearson’s distributions is the probabilistic basis for the method. So far, such a method was applied and analyzed mostly for leptokurtic or mesokurtic distributions (Pearson’s distributions of types IV or VII), which predominate practical cases. The analyses of geodetic or astronomical observations show that we may also deal with sets which have moderate asymmetry or small negative excess kurtosis. Asymmetry might result from the influence of many small systematic errors, which were not eliminated during preprocessing of data. The excess kurtosis can be related with bigger or smaller (in relations to the Hagen hypothesis) frequency of occurrence of the elementary errors which are close to zero. Considering that fact, this paper focuses on the estimation with application of the Pearson platykurtic distributions of types I or II. The paper presents the solution of the corresponding optimization problem and its basic properties. Although platykurtic distributions are rare in practice, it was an interesting issue to find out what results can be provided by MP estimation in the case of such observation distributions. The numerical tests which are presented in the paper are rather limited; however, they allow us to draw some general conclusions.
Wydawca
Czasopismo
Rocznik
Tom
Strony
117--135
Opis fizyczny
Bibliogr. 45 poz., tab., wykr.
Twórcy
autor
- University of Warmia and Mazury Institute of Geodesy 1 Oczapowskiego St., 10-957 Olsztyn, Poland
Bibliografia
- [1] Baarda, W. (1968). A test procedure for use in geodetic networks. Neth. Geod. Comm. Publ. Geod., New Ser. Vol. 2(5): 27-55.
- [2] Bera, A. K. and Jarque, C. (1980). Effi cient Test for Normality, Heteroscedasticity and Serial Independence of Regression Residuals. Economic Letters, Vol. 6: 255-259.
- [3] Cellmer, S. (2014). Least fourth powers: optimisation method favouring outliers. Survey Review, Vol. 47(345): 411-417. DOI: 10.1179/1752270614Y.0000000142.
- [4] D’agostino, R., Belanger, A. and D’agostıno, R.A. (1990). A suggestion for using powerful and informative tests of normality. The American Statistician, Vol. 44(4): 316–321. DOI:10.1080/00031305.1990.10475751.
- [5] Dorić, D., Nikolić-Dorić, E., Jeveremović, V. and Mališić, J. (2009). On measuring skewness and kurtosis. Quality and Quantity, Vol. 43(3): 481-493. DOI: 10.1007/s11135-007-9128-9.
- [6] Duchnowski, R. and Wiśniewski, Z. (2011). Estimation of the shift between parameters of functional models of geodetic observations by applying Msplit estimation. Journal of Surveying Engineering Vol. 138(1): 1-8. DOI: 10.1061/(ASCE)SU.1943-5428.0000062.
- [7] Duchnowski, R. and Wiśniewski, Z. (2014). Comparison of two unconventional methods of estimation applied to determine network point displacement. Survey Review, Vol. 46(339): 401-405. DOI: 10.1179/1752270614Y.0000000127.
- [8] Duchnowski, R. and Wiśniewski, Z. (2016). Accuracy of the Hodges–Lehmann estimates computed by applying Monte Carlo simulations. Acta Geod Geophys. DOI 10.1007/s40328-016-0186-0.
- [9] Dzhun’, I.V. (1992). Pearson distribution of type VII used to approximate observation errors in astronomy. Measurement Techniques, Vol. 35(3): 277-282.
- [10] Dzhun’, I.V. (2011). Method for diagnostics of mathematical models in theoretical astronomy and astrometry. Kinematics and Physics of Celestial Bodies, Mathematical Processing of Astronomical Data, Vol. 27(5): 260-264.
- [11] Dzhun’, I.V. (2012). What should be the observation-calculation residuals in modern astrometric experiments. Kinematics and Physics of Celestial Bodies, Mathematical Processing of Astronomical Data, Vol. 28(1): 43-47. DOI: 10.3103/S0884591312010096.
- [12] Elderton, W.P. (1953). Frequency curves and correlation. Cambridge University Press.
- [13] Fischer, H. (2011). A history of central limit theorem. From classical to modern probability theory. Sources and Studies in the History of Mathematics and Physical Sciences, Springer New York-Dordrecht-Heidelberg-London, Book Chapter: 75-137.
- [14] Friori, A.M. and Zenga, M. (2009). Karl Pearson and the origin of kurtosis. International Statistical Review, Vol. 77(1): 40-50. DOI: 10.1111/j.1751-5823.2009.00076.x.
- [15] Gleinsvik, P. (1971). Zur Leistungsfähigkeit der Methode der kleinsten Quadrate bei Ausgleichung nich normalverteilter Beobachtungen. Theoretische Untersuchungen. Zeitschrift für Vermessungswesen, Vol. 6: 224-233.
- [16] Hampel, F.R. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association, Vol. 69(346): 383-397.
- [17] Hampel, F.R., Ronchetti, E.M., Rousseuw, P.J. and Stahel, W.A. (1986). Robust statistics. The approach based on influence functions. John Wiley & Sons, New York.
- [18] Hu, X., Huang, C. and Liao, X. (2001). A new solution assessment approach and its application to space geodesy data analysis. Celestial Mechanics and Dynamical Astronomy, Vol. 81(4): 265-278. DOI: 10.1023/A:1013204418865.
- [19] Huber, P.J. (1964). Robust estimation of location parameter. The Annals of Mathematical Statistics. Vol. 35(1): 73-101. DOI:10.1214/aoms/1177703732.
- [20] Huber, P.J. (1981). Robust statistics. The approach based on infl uence functions. John Wiley & Sons, New York.
- [21] Kadaj, R. (1988). Eine verallgemeinerte Klasse von Schätzverfahren mit praktischen Anwendungen. Zeitschrift für Vermessungswesen, Vol. 113: 157-166.
- [22] Kasietczuk, B. (1997). Estimation of asymmetry and kurtosis coeffi cients in the process of geodetic network adjustment by the least-squares method. Journal of Geodesy, Vol. 71(3): 131-136.
- [23] Kayikçi, E.T. and Sopaci, E. (2015). Testing the normality of the residuals of surface temperature data at VLBI/GPS co-located sites by goodness of fi t tests. Arabian Journal of Geosciences, Vol. 8(11):10119–10134. DOI: 10.1007/s12517-015-1911-7.
- [24] Kukuča, J. (1967). Some problems in estimating the accuracy of a measuring method. Studia Geophysica et Geodaetica, Vol. 11(1): 21-33.
- [25] Kutterer, H. (1999). On the sensitivity of the results of least-squares adjustments concerning the stochastic model. Journal of Geodesy, Vol. 73(7): 350 -361.
- [26] Lehmann, R. (2012). Improved critical values for extreme normalized and studentized residuals In Gauss–Markov models. Journal of Geodesy, Vol. 86(12): 1137–1146. DOI: 10.1007/s00190-012-0569-0.
- [27] Lehmann, R. (2015). Observation error model selection by information criteria vs. normality testing. Studia Geophysica et Geodaetica, Vol. 59(4): 489-504. DOI: 10.1007/s11200-015-0725-0.
- [28] Luo, X., Mayer, M. and Heck, B. (2011). On the probability distribution of GNSs carrier phase observations. GPS Solutions, Vol. 15(4): 369-379. DOI: 10.1007/s10291-010-0196-2.
- [29] Mooijaart, A. (1985). Factor analysis for non-normal variables. Psychometrika, Vol. 50( 3): 323-342. DOI: 10.1007/BF02294108.
- [30] Mukhopadhyay, N. (2005). Dependence or independence of the sample mean and variance in non –IID or non-normal cases and the role of some tests of independence. Recents Advances in Applied Probability. Springer Science + Business Media, Inc. Book Chapter: 397-426.
- [31] Pearson, K. (1920). The fundamental problem of practical statistics. Statistics, Vol. 13: 1–16.
- [32] Romanowski, M. (1964). On the normal law of errors. Bulletin Géodésique, Vol. 73(1): 195-215. DOI:10.1007/BF02528935.
- [33] Romanowski, M. and Green, E. (1983). Refl exions on the kurtosis of samples of errors. Bulletin Géodésique, Vol. 57(1): 62-82. DOI: 10.1007/BF02520912.
- [34] Serfling, R. (1980). Approximation theorems of mathematical statistics. John Wiley & Sons (Polish edition, PWN, 1991).
- [35] Tiberius, C.C.J.M. and Borre, K. (2000). Are GPS data normally distributed. In: Schwarz K.P. (Ed.) Geodesy Beyond 2000. International Association of Geodesy Symposia, Vol. 121: 243-248.
- [36] Wassef, A.M. (1959). Note of the application of mathematical statistics to the analysis of levelling errors. Bulletin Géodésique, Vol. 52(1): 19-26.
- [37] Wiśniewski, Z. (1985). The effect of the asymmetry of geodetic observation error distribution on the results of adjustment by least squares method. Geodezja i Kartografia, Vol. 34(1): 11-21.
- [38] Wiśniewski, Z.(1987, Method of geodetic network adjustment in extend to probabilistic measurement error properties. Zeszyty Naukowe Akademii Górniczo-Hutniczej, Geodezja, 95, No. 1127, 73-88.
- [39] Wiśniewski, Z. (1996). Estimation of the third and fourth order central moments of measurement errors from sums of powers of least squares adjustment residuals. Journal of Geodesy, Vol. 70(5): 256-262.
- [40] Wiśniewski, Z. (2009). Estimation of parameters in a split functional model of geodetic observations (Msplit estimation). Journal of Geodesy, Vol. 83( 2): 105-120. DOI: 10.1007/s00190-008-0241-x.
- [41] Wiśniewski, Z. (2010). Msplit(q) estimation: estimation of parameters in a multi split functional model of geodetic observations. Journal of Geodesy, Vol. 84(6): 355-372. DOI: 10.1007/s00190-010-0373-7.
- [42] Wiśniewski, Z. (2014). M-estimation with probabilistic models of geodetic observations. Journal of Geodesy, Vol. 88(10): 941–957. DOI: 10.1007/s00190-014-0735-7.
- [43] Wiśniewski, Z. and Zienkiewicz, M.H. (2016). Shift-M split estimation in deformation analyses. Journal of Surveying Engineering, DOI: 10.1061/(ASCE)SU.1943-5428.0000183.
- [44] Xi, Z., Hu, C. and Youn, B.D. (2012). A comparative study of probability estimation methods for reliability analysis. Structural and Multidisciplinary Optimization, Vol. 45(1): 33-52. DOI: 10.1007/s00158-011-0656-5.
- [45] Yang, Y. (1997). Estimators of covariance matrix at robust estimation based on influence functions. Zeitschrift für Vermessungswesen, Vol. 122: 166-174.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-70851f56-ec02-4b7a-8fd9-5e992d31334d