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  • Cited by 12
  • Alan D. Chave, Woods Hole Oceanographic Institution, Massachusetts
Publisher:
Cambridge University Press
Online publication date:
October 2017
Print publication year:
2017
Online ISBN:
9781316156100

Book description

Based on a course taught by the author, this book combines the theoretical underpinnings of statistics with the practical analysis of Earth sciences data using MATLAB. The book is organized to introduce the underlying concepts, and then extends these to the data, covering methods that are most applicable to Earth sciences. Topics include classical parametric estimation and hypothesis testing, and more advanced least squares-based, nonparametric, and resampling estimators. Multivariate data analysis, not often encountered in introductory texts, is presented later in the book, and compositional data is treated at the end. Datasets and bespoke MATLAB scripts used in the book are available online, as well as additional datasets and suggested questions for use by instructors. Aimed at entering graduate students and practicing researchers in the Earth and ocean sciences, this book is ideal for those who want to learn how to analyse data using MATLAB in a statistically-rigorous manner.

Reviews

'One of the main strengths of this book is the combination of mathematical rigor with extensive examples, allowing readers to work through case studies to better understand the concepts presented. The tool used for this purpose is MATLAB, which is widely used in the earth science community. Examples are drawn from geophysics, astrophysics, and anthropology (among others). Both the scripts and the data examples used in the book are available for download from the publisher's website. … This book is an ideal guide for graduate students seeking a comprehensive and rigorous understanding of statistical methods in earth sciences. For the more mature earth scientist (and I include myself in that number), it provides a useful reference to widely used statistical concepts that many of us regularly encounter.'

Lucy MacGregor Source: The Leading Edge

'… this book will be a welcome and invaluable addition to any earth scientist's library.'

Sven Treitel Source: The Leading Edge

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Contents

References

Aitchison, J. (1986). The Statistical Analysis of Compositional Data. London: Chapman & Hall.
Aitchison, J., & Greenacre, M. (2002). Biplots of compositional data. Appl. Stat., 51, 375–92.
Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis, 3rd edn. New York: Wiley.
Anderson, T. W., & Darling, D. (1952). Asymptotic theory of certain goodness of fit criteria based on stochastic processes. Ann. Math. Stat., 23, 193212.
Anderson, T. W., & Darling, D. (1954). A test of goodness of fit. J. Am. Stat. Assoc., 49, 765–9.
Andrews, D. F., & Herzberg, A. M. (1985). Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer.
Ansari, A. R., & Bradley, R. A. (1960). Rank-sum tests for dispersions. Ann. Math. Stat., 31, 1174–89.
Arbuthnott, J. (1710). An argument for divine providence, taken from the constant regularity in the births of both sexes. Philos. Trans. R. Soc. Lond., 27, 186–90.
Azzalini, A., & Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. J. R. Stat. Soc., C39, 357–65.
Bartlett, M. S. (1937). Properties of sufficiency and statistical tests. Proc. R. Soc. Lond., A160, 268–82.
Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philos. Trans. R. Soc. Lond., 53, 370418.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc., B57, 289300.
Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93, 491507.
Berry, K. J., Mielke, P. W. Jr., & Johnston, J. E. (2016). Permutation Statistical Methods: An Integrated Approach. New York: Springer.
Birnbaum, A. (1954). Combining independent tests of significance. J. Am. Stat. Assoc., 49, 559–74.
Blackwell, D. (1947). Conditional expectation and unbiased sequential estimation. Ann. Math. Stat., 18, 105–10.
Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J. Am. Stat. Assoc., 90, 443–50.
Box, G. E. P. (1949). A general distribution theory for a class of likelihood criteria. Biometrika, 36, 317–46.
Brofitt, J. D., & Randles, R. H. (1977). A power approximation for the chi-square goodness-of-fit test: simple hypothesis case. J. Am. Stat. Assoc., 72, 604–7.
Carroll, R. J., & Welsh, A. H. (1988). A note on asymmetry and robustness in linear regression. Am. Stat., 42, 285–7.
Carter, M., & van Brunt, B. (2000). The Lebesgue-Stieltjes Integral: A Practical Introduction. New York: Springer.
Chave, A. D. (2014). Magnetotelluric data, stable distributions and impropriety: an existential combination. Geophys. J. Int., 198, 622–36.
Chave, A. D. (2015). A note about Gaussian statistics on a sphere. Geophys. J. Int., 203, 893–5.
Chave, A. D., & Thomson, D. J. (2003). A bounded influence regression estimator based on the statistics of the hat matrix. J. R. Stat. Soc., C52, 307–22.
Chave, A. D., & Thomson, D. J. (2004). Bounded influence estimation of magnetotelluric response functions. Geophys. J. Int., 157, 9881006.
Chave, A. D., Thomson, D. J., & Ander, M. E. (1987). On the robust estimation of power spectra, coherences and transfer functions. J. Geophys. Res., 92, 633–48.
Clarke, R. D. (1946). An application of the Poisson distribution. J. Inst. Actuaries, 22, 32.
Cochran, W. G. (1934). The distribution of quadratic forms in a normal system, with applications to the analysis of covariance. Math. Proc. Camb. Philos. Soc., 30, 178–91.
Cochran, W. G. (1952). The χ2 test of goodness of fit. Ann. Math. Stat., 23, 3545.
Cramér, H. (1945). Mathematical Methods of Statistics. Uppsala: Almqvist & Wiksell.
Csörgő, S., & Faraway, J. J. (1996). The exact and asymptotic distributions of Cramér–von Mises statistics. J. R. Stat. Soc., B58, 221–34.
David, H. A. (1981). Order Statistics, 2nd edn. New York: Wiley.
David, H. A. (2009). A historical note on zero correlation and independence. Am. Stat., 63, 185–6.
David, H. A., & Nagaraja, H. N. (2003). Order Statistics, 3rd edn. New York: Wiley.
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press.
De Groot, M. H., & Schervish, M. J. (2011). Probability and Statistics, 4th edn. London: Pearson.
De Moivre, A. (1711). De mensura sortis. Philos. Trans. R. Soc. Lond., 27, 213–64.
Doob, J. L. (1993). Measure Theory. New York: Springer.
DuMouchel, W. H. (1975). Stable distributions in statistical inference. 2. Information from stably distributed samples. J. Am. Stat. Assoc., 70, 386–93.
Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression, part I. Biometrika, 37, 409–28.
Durbin, J. & Watson, G. S. (1951). Testing for serial correlation in least squares regression, part II. Biometrika, 38, 159–77.
Durbin, J., & Watson, G. S. (1971). Testing for serial correlation in least squares regression, part III. Biometrika, 58, 119.
Efron, B. (1979). Bootstrap methods: another look at the jackknife. Ann. Stat., 7, 126.
Efron, B., & Stein, C. (1981). The jackknife estimate of variance. Ann. Stat., 9, 586–96.
Efron, B., & Tibshirani, R. (1998). An Introduction to the Bootstrap. London: Chapman & Hall.
Ernst, M. D. (2004). Permutation methods: a basis for exact inference. Stat. Sci., 19, 676–85.
Feller, W. (1948). On the Kolmogorov-Smirnov limit theorems for empirical distributions. Ann. Math. Stat., 19, 177–89.
Feller, W. (1950). Errata: On the Kolmogorov-Smirnov limit theorems for empirical distributions. Ann. Math. Stat., 21, 301–2.
Feller, W. (1971). An Introduction to Probability Theory and Its Applications, vol. 2. New York: Wiley.
Fieller, E. C. (1940). The biological standardization of insulin. J. R. Stat. Soc., 7(Suppl.), 164.
Fieller, E. C. (1944). A fundamental formula in the statistics of biological assays and some applications. Q. J. Pharm. Pharmacol., 17, 117–23.
Fieller, E. C. (1954). Some problems in interval estimation. J. R. Stat. Soc., 16, 175–85.
Fisher, N. I. (1995). Statistical Analysis of Circular Data. Cambridge University Press.
Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, 10, 507–21.
Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philos. Trans. R. Soc. Lond., A222, 309–68.
Fisher, R. A. (1928). The general sampling distribution of the multiple correlation coefficient. Proc. R. Stat. Soc., A121, 654–73.
Fisher, R. A. (1932). Statistical Methods for Research Workers, 4th edn. Edinburgh: Oliver & Boyd.
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Ann. Eugenics, 7, 179–88.
Fisher, R. A. (1953). Dispersion on a sphere. Proc. R. Soc. Lond., A217, 295305.
Gamble, T. D., Goubau, W. M., & Clarke, J. (1979). Magnetotellurics with a remote reference. Geophysics, 44, 5368.
Gauss, K. F. (1823). Theoria Combinationis Observationum Erroribus Minimis Oboxiae. Göttingen: Dieterich.
Geary, R. C. (1949). Determination of linear relationships between systematic parts of variables with errors of observation the variances of which are unknown. Econometrica, 17, 3058.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., et al. (2013). Bayesian Data Analysis, 3rd edn. Boca Raton, FL: Taylor & Francis.
Genovese, C., & Wasserman, L. (2002). Operating characteristics and extension of the false discovery rate procedure. J. R. Stat. Soc., B64, 499517.
Gleser, L. J., & Hwang, J. T. (1987). The nonexistence of 100(1−α)% confidence sets of finite expected diameter in errors-in-variables and related models. Ann. Stat., 15, 1351–62.
Good, P. I. (2000). Permutation Methods, 2nd edn. New York: Springer.
Good, P. I. (2005). Permutation, Parametric, and Bootstrap Tests of Hypotheses, 3rd edn. New York: Springer.
Gossett, W. S. (1908). The probable error of a mean. Biometrika, 6, 125.
Gradshteyn, I. S., & Ryzhik, I. M. (1980). Table of Integrals, Series and Products. San Diego: Academic Press.
Guenther, W. C. (1977). Power and sample size for approximate chi-square tests. Am. Stat., 31, 83–5.
Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics. New York: Wiley.
Hänggi, P., Roesel, F., & Trautmann, P. (1978). Continued fraction expansion in scattering theory and statistical non-equilibrium mechanics. Z. Naturforsch., 33a, 402–17.
Hand, D. J., Daly, F., McConway, K., Lunn, D., & Ostrowski, E. (1994). A Handbook of Small Data Sets. London: Chapman & Hall.
Handschin, E., Schweppe, F. C., Kohlas, J., & Fiechter, A. (1975). Bad data analysis of power system state analysis. IEEE Trans. Power Appar. Syst., PAS-94, 329–37.
Hanley, J. A., Julien, M., & Moodie, E. E. M. (2008). Student’s z, t, and s: what if Gossett had R?. Am. Stat., 62, 64–9.
Hastie, T., Tibshirani, R., & Friedman, J. (2008). The Elements of Statistical Learning, 2nd edn. New York: Springer.
Herschel, J. F. W. (1850). Quetelet on probabilities. Edinb. Rev., 92, 157.
Hettmansperger, T. P., & McKean, J. W. (1998). Robust Nonparametric Statistical Methods. London: Edward Arnold.
Hirschberg, J., & Lye, J. (2010). A geometric comparison of the delta and Fieller confidence intervals. Am. Stat., 64, 234–41.
Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (1991). Fundamentals of Exploratory Analysis of Variance. New York: Wiley.
Hodges, J. L., & Lehmann, E. L. (1956). The efficiency of some nonparametric competitors of the t-test. Ann. Math. Stat., 27, 324–35.
Hoeffding, W. (1952). The large-sample power of tests based on permutations of observations. Ann. Math. Stat., 23, 169–92.
Hoeffding, W. (1963). Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc., 58, 1330.
Hogg, R. V., & Craig, A. T. (1995). Introduction to Mathematical Statistics, 5th edn. Saddle River, NJ: Prentice-Hall.
Hotelling, H. (1931). The generalization of Student’s ratio. Ann. Math. Stat., 2, 360–78.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. J. Educ. Psychol., 24, 417–41.
Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321–77.
Hotelling, H. (1953). New light on the correlation coefficient and its transforms. Proc. R. Stat. Soc., B15, 193232.
Hubble, E. (1929). A relation among distance and radial velocity among extra-galactic nebulae. Proc. Nat. Acad. Sci. USA, 15, 168–73.
Huber, P. (1964). Robust estimation of a location parameter. Ann. Math. Stat., 35, 73101.
Huber, P. (2011). Data Analysis: What Can Be Learned from the Past 50 Years. New York: Wiley.
Jarque, C. M., & Bera, A. K. (1987). A test for normality of observations and regression residuals. Int. Stat. Rev., 55, 163–72.
Johnson, N. L., Kotz, S., & Balikrishnan, N. (1994). Continuous Univariate Distributions, vol. 1, 2nd edn. New York: Wiley.
Johnson, N. L., Kotz, S., & Balikrishnan, N. (1995). Continuous Univariate Distributions, vol. 2, 2nd edn. New York: Wiley.
Johnson, N. L., Kotz, S., & Balakrishnan, N. (1997). Discrete Multivariate Distributions. New York: Wiley.
Johnson, N. L., Kotz, S., & Kemp, A. W. (1993). Univariate Discrete Distributions, 2nd edn. New York: Wiley.
Jolliffe, I. T. (2002). Principal Component Analysis, 2nd edn. New York: Springer.
Kolmogorov, A. (1933). Sulla determinazione empirica di una legge di distribuzione. Inst. Ital. Attuarai, Giorn., 4, 111.
Kotz, S., & Johnson, N. L. (eds.) (1992). Breakthroughs in Statistics, vol. II: Methodology and Distributions. New York: Springer.
Kotz, S., Balakrishnan, N., & Johnson, N. L. (2000). Continuous Multivariate Distributions, vol 1: Models and Applications, 2nd edn. New York: Wiley.
Kvam, P. H., & Vidakovic, B. (2007). Nonparametric Statistics with Applications to Science and Engineering. Hoboken, NJ: Wiley.
Lagarias, J. C., Reeds, J. A., Wright, M. H., & Wright, P. E. (1998). Convergence properties of the Neider-Mead simplex method in low dimensions. SIAM J. Optim., 9, 112–47.
Langevin, P. (1905). Magnétisme et théorie des électrons. Ann. Chim. Phys., 5, 71127.
Lanzante, J. R. (2005). A cautionary note on the use of error bars. J. Climate, 18, 3699–703.
Lehmann, E. L. (1953). The power of rank tests. Ann. Math. Stat., 24, 2343.
Lehmann, E. L., & Schaffer, J. P. (1988). Inverted distributions. Am. Stat., 42, 191–4.
Lévy, P. P. (1925). Calcul des Probabilités. Paris: Gauthier-Villars.
Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc., 62, 399402.
Longley, J. W. (1967). An appraisal of least squares programs for electronic computers from the viewpoint of the user. J. Am. Stat. Assoc., 62, 819–41.
Love, J. J., & Constable, C. G. (2003). Gaussian statistics for paleomagnetic vectors. Geophys. J. Int., 152, 515–65.
Mallows, C. L. (1975). On some topics in robustness. Tech. Mem., Bell Telephone Laboratories.
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 18, 5060.
Mardia, K. V., & Jupp, P. E. (2000). Directional Statistics. New York: Wiley.
Mardia, K. V., Kent, J. T., & Bibby, J. (1979). Multivariate Analysis. London: Academic Press.
Mauchly, J. W. (1940). Significance test for sphericity of a normal n-variate distribution. Ann. Math. Stat., 11, 204–9.
Meerschaert, M. M. (2012). Fractional calculus, anomalous diffusion and probability. In Fractional Dynamics, ed. Lim, S. C., Klafter, J., & Metler, R.. Singapore: World Science Press.
Michael, J. R. (1983). The stabilized probability plot. Biometrika, 70, 1117.
Mitra, S. K. (1958). On the limiting power function of the frequency chi-square test. Ann. Math. Stat., 29, 1221–33.
Moore, D. S., & McCabe, G. P. (1989). Introduction to the Practice of Statistics. New York: W.H. Freeman.
Mosteller, F. (1946). On some useful “inefficient” statistics. Ann. Math. Stat., 17, 377408.
Murphy, K. R., Myors, B., & Wolach, A. (2014). Statistical Power Analysis. New York: Routledge.
Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. J. R. Stat. Soc., A135, 370–84.
Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. Lond., A231, 289337.
Nolan, J. P. (1997). Numerical calculation of stable densities and distribution functions. Comm. Stat. Stoch. Mod., 13, 759–74.
Nolan, J. P. (1998). Parameterizations and modes of stable distributions. Stat. Prob. Lett., 38, 187–95.
Nolan, J. P. (2001). Maximum likelihood estimation and diagnostics for stable distributions. In Lévy Processes: Theory and Applications, ed. Barnsdorff-Nielsen, O. E., Mikosch, T., & Resnick, S. I.. Basel, Birkhäuser.
Oldham, K. B., & Spanier, J. (1974). The Fractional Calculus. San Diego: Academic Press.
Patnaik, P. B. (1949). The non-central χ2- and F-distributions and their applications. Biometrika, 36, 202–32.
Pawlowsky-Glahn, V., & Buccianti, A. (eds.) (2011). Compositional Data Analysis: Theory and Applications. New York: Wiley.
Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2015). Modeling and Analysis of Compositional Data. New York: Wiley.
Pearson, K. (1896). On a form of spurious correlation which may arise when indices are used in the measurement of organs. Philos. Trans. R. Soc. Lond., 60, 489–98.
Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos. Mag., 50, 339–57.
Pesarin, F., & Salmaso, L. (2010). Permutation Tests for Complex Data: Theory, Applications and Software. New York: Wiley.
Phipson, B., & Smyth, G. K. (2010). Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol., 9(1), art. 39.
Picinbono, B. (1996). Second order complex random vectors and normal distributions. IEEE Trans. Sig. Proc., 44, 2637–40.
Pitman, E. J. G. (1937a). Significance tests which may be applied to samples from any population. J. R. Stat. Soc. Suppl., 4, 119–30.
Pitman, E. J. G. (1937b). Significance tests which may be applied to samples from any population. II. The correlation coefficient test. J. R. Stat. Soc. Suppl., 4, 225–32.
Pitman, E. J. G. (1938). Significance tests which may be applied to samples from any population. III. The analysis of variance test. Biometrika, 29, 322–35.
Poisson, S. D. (1837). Recherches sur la Probabilité des Jugements en Matiére Criminelle et en Matiére Civile, Précédées des Regles Générales du Calcul des Probabilités. Paris: Bachelier.
Preisendorfer, R. W. (1988). Principal Component Analysis in Meteorology and Oceanography. Amsterdam: Elsevier.
Rao, C. R. (1945). Information and the accuracy attainable in the estimation of statistical parameters. Bull. Calcutta Math. Soc., 37, 8191.
Rao, C. R. (1947). Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Proc. Camb. Philos. Soc., 44, 50–7.
Rao, C. R. (1951). An asymptotic expansion of the distribution of Wilks’ criterion, Bull. Inter. Stat. Inst., 33, 177–80.
Reiersøl, O. (1941). Confluence analysis by means of lag moments and other methods of confluence analysis. Econometrica, 9, 124.
Reiersøl, O. (1945). Confluence analysis by means of instrumental sets of variables. Ark. Mat. Astron. Fys., 32, 1119.
Rencher, A. C. (1995). Methods of Multivariate Analysis. New York: Wiley.
Rencher, A. C. (1998). Multivariate Statistical Inference and Applications. New York: Wiley.
Rice, J. A. (2006). Mathematical Statistics and Data Analysis, 3rd edn. Independence, KY: Cengage Learning.
Romano, J. P. (1990). On the behavior of randomization tests without a group invariance assumption. J. Am. Stat. Assoc., 85, 686–92.
Rousseeuw, P. J. W. (1984). Least median of squares regression. J. Am. Stat. Assoc., 79, 871–80.
Rousseeuw, P. J. W., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. New York: Wiley.
Rousseeuw, P. J. W., & Leroy, A. M. (2005). Robust Regression and Outlier Detection, 2nd edn. New York: Wiley.
Rutherford, E., Geiger, H., & Bateman, H. (1910). The probability variations in the distribution of α particles. Philos. Mag., 20, 698707.
Ryan, T. P. (1997). Modern Regression Methods. New York: Wiley.
Samorodnitsky, G., & Taqqu, M. (1994). Stable Non-Gaussian Random Processes. London: Chapman & Hall.
Satterthwaite, F. E. (1946). An approximate distribution of estimates of variance components. Biomed. Bull., 2, 110–14.
Schenker, J., & Gentleman, J. F. (2001). On judging the significance of differences by examining the overlap between confidence intervals. Am. Stat., 55, 182–6.
Scheffé, H. (1959). Analysis of Variance. New York: Wiley.
Schreier, P. J., & Scharf, L. L. (2010). Statistical Signal Processing of Complex-Valued Data. Cambridge University Press.
Schreier, P. J., Scharf, L. L., & Hanssen, A. (2006). A generalized likelihood ratio test for impropriety of complex signals. IEEE Sig. Proc. Lett., 13, 433–6.
Shaffer, J. P. (1991). The Gauss-Markov theorem and random regressors. Am. Stat., 45, 269–73.
Simpson, J., Olsen, A., & Eden, J. C. (1975). A Bayesian analysis of a multiplicative treatment effect in weather modifications. Technometrics, 17, 161–6.
Siotani, M., Yoshida, K., Kawakami, H., Nojiro, K., Kawashima, K., et al. (1963). Statistical research on the taste judgement: analysis of the preliminary experiment on sensory and chemical characters of Seishu. Proc. Inst. Stat. Math., 10, 99118.
Smirnov, N. V. (1939). On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Bull. Math. Univ. Moscow. Serie Int., 2, 316.
Spearman, C. E. (1904). The proof and measurement of association between two things. Am. J. Psych., 15, 72101.
Stephens, M. A. (1970). Use of the Kolmogorov-Smirnov, Cramer-Von Mises and related statistics without extensive tables. J. R. Stat. Soc., B32, 115–22.
Stephens, M. A. (1976). Asymptotic results for goodness-of-fit statistics with unknown parameters. Ann. Stat., 4, 357–69.
Stigler, S. (1977). Do robust estimators work with real data? Ann. Stat., 5, 1055–98.
Stuart, A., & Ord, J. K. (1994). Kendall’s Advanced Theory of Statistics, vol. 1: Distribution Theory, 6th edn. London: Edward Arnold.
Stuart, A., Ord, J. K., & Arnold, S. (1999). Kendall’s Advanced Theory of Statistics, vol. 2: Classical Inference and the Linear Model, 6th edn. London: Edward Arnold.
Thomson, A., & Randall-Maciver, R. (1905). Ancient Races of the Thebaid. Oxford University Press.
Thomson, D. J. (1977). Spectrum estimation techniques for characterization and development of WT4 waveguide, I. Bell. Syst. Tech. J., 56, 1769–815.
Thomson, D. J., & Chave, A. D. (1991). Jackknifed error estimates for spectra, coherences and transfer functions. In Advances in Spectrum Analysis and Array Processing, vol. 1, ed. Haykin, S.. Englewood Cliffs, NJ: Prentice-Hall, pp. 58113.
Thompson, W. R. (1936). On confidence ranges for the median and other expectation distributions for populations of unknown distribution form. Ann. Math. Stat., 7, 122–8.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. R. Stat. Soc., B58, 267–88.
Uchaikin, V. V., & Zolotarev, V. M. (1999). Chance and Stability. Waterbury, VT: VSP Press.
Van Den Bos, A. (1995). A multivariate complex normal distribution: a generalization. IEEE Trans. Inform. Theory, 41, 537–9.
Von Bortkiewicz, L. J. (1898). Das Gesetz der kleinen Zahlen. Leipzig: B.G. Teubner.
Von Luxburg, U., & Franz, V. H. (2009). A geometric approach to confidence sets for ratios: Fieller’s theorem and the general linear model. Stat. Sinica, 29, 1095–117.
Von Mises, R. (1918). Über die “Ganszzahligkeit” der Atomgewichte und verwandte Fragen. Phys. Z., 19, 490500.
Von Neumann, J. (1951). Various techniques used in connection with random digits: Monte Carlo methods. App. Math. Ser. Nat. Bur. Stand., 12, 36–8.
Wald, A. (1940). The fitting of straight lines if both variables are subject to error. Ann. Math. Stat., 11, 284300.
Wald, A. (1943). Tests of statistical hypotheses concerning several parameters when the number of observations is large. Trans. Am. Math. Soc., 54, 426–82.
Walden, A. T., & Rubin-Delanchy, P. (2009). On testing for impropriety of complex-valued Gaussian vectors. IEEE Trans. Sig. Proc., 57, 825–34.
Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. New York: Springer.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: context, process and purpose. Am. Stat., 70, 129–33.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bull., 1, 80–3.
Wilcoxon, F. (1946). Individual comparisons of grouped data by ranking methods. J. Econ. Entomol., 39, 269–70.
Wilks, S. S. (1932). Certain generalizations in the analysis of variance. Biometrika, 24, 471–94.
Wilks, S. S. (1938). The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat., 9, 60–2.
Wishart, J. (1928). The generalized product moment distribution in samples from a normal multivariate population. Biometrika, 20, 3252.
Yohai, V. J. (1987). High breakdown-point and high efficiency robust estimates for regression. Ann. Stat., 15, 642–56.

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