Abstract
Outcomes of physical measurements are most frequently recorded as signals that need to be processed statistically in order to infer their overall properties and features, and later compared to theoretical or phenomenological models. This chapter starts with an introduction to the basic statistical techniques of computing the averages and moments of distributions and their uncertainties. Particular emphasis is given on ways of identifying outliers and robust estimates of location (averages) and scale (dispersion). We introduce commonly used methods of computing confidence intervals for the sample means and variances, of comparing the means of samples with equal or different variances, of comparing two distributions, and computing correlations. Simple linear and multiple linear, as well as non-linear regression techniques are explained, again with attention to robust measures. Powerful multi-variate methods of principal component analysis, cluster analysis, linear discriminant analysis, and factor analysis are discussed in a separate section each. The illustrations in the Problems include the study of Raman spectra in fabric yarns, the analysis of geyser eruptions, radar reflections in the ionosphere, and the correlation analysis of astrophysical objects.
This is a preview of subscription content, log in via an institution.
References
J.E. Gentle, W. Härdle, Y. Mori (eds.), Handbook of Computational Statistics. Concepts and Methods (Springer, Berlin, 2004)
V. Barnett, T. Lewis, Outliers in Statistical Data, 3rd edn. (Wiley, New York, 1994)
R. Kandel, Our Changing Climate (McGraw-Hill, New York, 1991), p. 110
L. Davies, U. Gather, Robust statistics, in Handbook of Computational Statistics. Concepts and Methods (Springer, Berlin, 2004) pp. 655–695
Analytical Methods Committee, Robust statistics—how not to reject outliers, part 1: basic concepts. Analyst 114, 1693 (1989)
Analytical Methods Committee, Robust statistics—how not to reject outliers, part 2: inter-laboratory trials. Analyst 114, 1699 (1989)
V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey. ACM Comput. Surv. 41, 15 (2009)
A. Patcha, J.-M. Park, An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51, 3448 (2007)
M. Agyemang, K. Barker, R. Alhajj, A comprehensive survey of numeric and symbolic outlier mining techniques. Intell. Data Anal. 10, 521 (2006)
V.J. Hodge, J. Austin, A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85 (2004)
L. Davies, U. Gather, The identification of multiple outliers. J. Am. Stat. Assoc. 88, 782 (1993)
B. Iglewicz, J. Martinez, Outlier detection using robust measures of scale. J. Stat. Comput. Simul. 15, 285 (1982)
F.E. Grubbs, Procedures for detecting outlying observations in samples. Technometrics 11, 1 (1969)
W.J. Dixon, Ratios involving extreme values. Ann. Math. Stat. 22, 68 (1951)
W.J. Dixon, Analysis of extreme values. Ann. Math. Stat. 21, 488 (1950)
R.J. Beckman, R.D. Cook, Outlier..........s. Technometrics 25, 119 (1983)
R.A. Maronna, R.D. Martin, V.J. Yohai, Robust Statistics. Theory and Methods (Wiley, Chichester, 2006)
M.R. Spiegel, Schaum’s Outline of Theory and Problems of Probability and Statistics (McGraw-Hill, New York, 1975)
S. Brandt, Data Analysis, 3rd edn. (Springer, New York, 1999)
H.B. Mann, A. Wald, On the choice of the number of class intervals in the application of the chi square test. Ann. Math. Stat. 13, 306 (1942)
W.C.M. Kallenberg, J. Oosterhoff, B.F. Schriever, The number of classes in chi-squared goodness-of-fit tests. J. Am. Stat. Assoc. 80, 959 (1985), and references therein
W.C. Kallenberg, On moderate and large deviations in multinomial distributions. Ann. Stat. 13, 1554 (1985)
M.A. Stephens, Use of the Kolmogorov–Smirnov, Cramer–Von Mises and related statistics without extensive tables. J. R. Stat. Soc. B 32, 115 (1970)
A.F. Nikiforov, S.K. Suslov, V.B. Uvarov, Classical Orthogonal Polynomials of a Discrete Variable. Springer Series in Computational Physics (Springer, Berlin, 1991)
W.H. Press, B.P. Flannery, S.A. Teukolsky, W.T. Vetterling, Numerical Recipes: The Art of Scientific Computing, 3rd edn. (Cambridge University Press, Cambridge, 2007). See also the equivalent handbooks in Fortran, Pascal and C, as well as http://www.nr.com
C.A. Cantrell, Technical note: Review of methods for linear least-squares fitting of data and application to atmospheric chemistry problems. Atmos. Chem. Phys. 8, 5477 (2008)
D. York et al., Unified equations for the slope, intercept, and standard errors of the best straight line. Am. J. Phys. 72, 367 (2004)
K. Nakamura et al. (Particle Data Group), Review of particle physics. J. Phys. G 37, 075021 (2010). See Sect. 5 of the Introduction
M.C. Ortiz, L.A. Sarabia, A. Herrero, Robust regression techniques. A useful alternative for the detection of outlier data in chemical analysis. Talanta 70, 499 (2006)
J. Ferré, Regression diagnostics, in Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Vol. 3, ed. by S.D. Brown, R. Tauler, B. Walczak (2009), p. 33
P.J. Rousseeuw, A.M. Leroy, Robust Regression and Outlier Detection (Wiley, Hoboken, 2003)
I. Barrodale, F.D.K. Roberts, An improved algorithm for discrete l 1 linear approximation. SIAM J. Numer. Anal. 10, 839 (1973)
S. Portnoy, R. Koenker, The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. Stat. Sci. 12, 279 (1997)
P.J. Rousseeuw, Least median of squares regression. J. Am. Stat. Assoc. 79, 871 (1984)
T. Bernholt, Computing the least median of squares estimator in time \(\mathcal{O}(n^{d})\), in Lecture Notes in Computer Science, vol. 3480, ed. by O. Gervasi et al. (Springer, Berlin, 2005), p. 697
A. Stromberg, Computing the exact least median of squares estimate and stability diagnostics in multiple linear regression. SIAM J. Sci. Comput. 14, 1289 (1993)
B.W. Rust, Fitting nature’s basic functions, part I: polynomials and linear least squares. Comput. Sci. Eng. Sep/Oct, 84 (2001)
B.W. Rust, Fitting nature’s basic functions, part II: estimating uncertainties and testing hypotheses, Comput. Sci. Nov/Dec, 60 (2001)
B.W. Rust, Fitting nature’s basic functions, part III: exponentials, sinusoids, and nonlinear least squares, Comput. Sci. Jul/Aug, 72 (2002)
B.W. Rust, Fitting nature’s basic functions, part IV: the variable projection algorithm, Comput. Sci. Mar/Apr, 74 (2003)
A.J. Izenman, Modern Multivariate Statistical Techniques (Springer, Berlin, 2008)
H. Swierenga, A.P. de Weijer, R.J. van Wijk, L.M.C. Buydens, Strategy for constructing robust multivariate calibration models. Chemom. Intell. Lab. Syst. 49, 1 (1999)
I.T. Jolliffe, Principal Component Analysis, 2nd edn. (Springer, Berlin, 2002)
S. Roweis, Z. Ghahramani, A unifying review of linear Gaussian models. Neural Comput. 11, 305 (1999)
A. Azzalini, A.W. Bowman, A look at some data on the Old Faithful geyser. J. R. Stat. Soc. C 39, 357 (1990)
A.K. Jain, M.N. Murty, Data clustering: a review. ACM Comput. Surv. 31, 264 (1999)
W. Härdle, L. Simar, Applied Multivariate Statistical Analysis (Springer, Berlin, 2007)
R. Xu, D.C. Wunsch II, Clustering (Wiley, Hoboken, 2009)
G. Gan, C. Ma, J. Wu, Data Clustering. Theory, Algorithms, and Applications (Philadelphia, SIAM, 2007)
J. Kogan, Introduction to Clustering Large and High-Dimensional Data (Cambridge University Press, Cambridge, 2007)
J. Valente de Oliveira, W. Pedrycz (eds.), Advances in Fuzzy Clustering and Its Applications (Wiley, Chichester, 2007)
The R Project for Statistical Computing. http://www.r-project.org/. Attention: the R reference manual has approximately 3000 pages!
J. Maindonald, J. Braun, Data Analysis and Graphics Using R, 2nd edn. (Cambridge University Press, Cambridge, 2006). A good introductory text for R, which is an open-source alternative to the S/S+ systems (“R is to S what Octave is to Matlab”)
U. von Luxburg, A tutorial on spectral clustering. Technical Report No. Tr-149, Max-Planck-Institut für biologische Kybernetik, 2006
A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst. 14, 849 (2001). See also Ref. [13] in this paper
O.L. Mangasarian, W.N. Street, W.H. Wolberg, Breast cancer diagnosis and prognosis via linear programming. Oper. Res. 43, 570 (1995)
C. Wolf et al., A catalogue of the Chandra deep field south with multi-colour classification and photometric redshifts from COMBO-17. Astron. Astrophys. 421, 913 (2004)
C. Wolf et al., Calibration update of the COMBO-17 CDFS catalogue. Astron. Astrophys. 492, 933 (2008)
http://www.mpia.de/COMBO/combo_CDFSpublic.html. The data can be found at http://astrostatistics.psu.edu/datasets/COMBO17.html
R.A. Reyment, K.G. Jöreskog, L.F. Marcus, Applied Factor Analysis in the Natural Sciences (Cambridge University Press, Cambridge, 1993)
G. Pison, P.J. Rousseeuw, P. Filzmoser, C. Croux, Robust factor analysis. J. Multivar. Anal. 84, 145 (2003)
P. Filzmoser, K. Hron, C. Reimann, R. Garrett, Robust factor analysis for compositional data. Comput. Geosci. 35, 1854 (2009)
C. Reimann, P. Filzmoser, R.G. Garrett, Factor analysis applied to regional geochemical data: problems and possibilities. Appl. Geochem. 17, 185 (2002)
http://lib.stat.cmu.edu/datasets/bodyfat, where all data is collected and the corresponding original literature is cited
V.G. Sigillito, S.P. Wing, L.V. Hutton, K.B. Baker, Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Tech. Dig. 10, 262 (1989). The corresponding data file can be found at http://archive.ics.uci.edu/ml/datasets.html
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Širca, S., Horvat, M. (2012). Statistical Analysis and Modeling of Data. In: Computational Methods for Physicists. Graduate Texts in Physics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32478-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-32478-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32477-2
Online ISBN: 978-3-642-32478-9
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)