Multivariate statistics and computational methods

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Multivariate statistics and computational methods graduate students ought to know: A (draft of) a ... to use utilize software such as SPSS in order to perform statistical techniques they do not .... Biostatistics and Microbiology: A Survival Manual. Springer. ... Computational Biology: A Practical Introduction to BioData Processing.
Multivariate statistics and computational methods graduate students ought to know: A (draft of) a Bibliography Contents & Bibliographic Taxonomy Preface and Preamble Section 1: Elementary & Introductory Texts Section 2: Thinking Critically with Statistical and Probabilistic Reasoning Section 3: Subject Specific Texts Section 4: Some Important Studies on Critical Issues Section 5: History & Development of Statistics Section 6: Statistical & Computational Software Section 7: Some Useful and Important Series of Monographs, Conference Proceedings, and Volumes Section 8: Sample of Texts: Not Your Grandmother’s Statistics Preface and Preamble: Mathematics is the language of the sciences.i However, one area of mathematics dominates in scientific research: statistics. From the statistical mechanics to machine learning and social psychology, the degree of overlap in the statistical methods, measures, and models is rather astounding considering how different typical methods used in these and other fields are, let alone the fields themselves. For example, the following is from a paper by two experienced researchers (one with both an MD and PhD) in a volume on pain research methods Standard tests such as analysis of variance (ANOVA), Scheffé’s F test, and serial ttests...should be used to ensure statistical significance (p < 0.05)ii. What these “standard tests” are is far less important than that they are “standard”. But standard for whom? Naturally, the authors mean “standard” for their target audience: target readers include beginners in pain research who may have substantial training and experiences in other fields; and pain researchers who may have extensive knowledge and experience in a specific field, but who may want to extend their research to a new level” (p. v).

Clearly, business researchers don’t qualify. Yet looking at a textbook on business research methodsiii we find ANOVA, t-tests, & F-tests all on one page in a chapter summary of “Key Concepts” (p. 548). So despite the radical differences between beta testing products and classifying nociceptive neurons in the peripheral nervous system, the same statistical tests are standard. Unfortunately, these standard tests are frequently inferior to readily available and easily used alternatives, and are employed without a sufficient appreciation of their underlying logic and assumptions. The literature on problems with the uses of statistics across different sciences is vast, but the first point at least can be illustrated with a neutral statement based on mathematically demonstrable fact: Many of the statistical methods routinely used in contemporary research are based on a compromise with the ideal... The compromise is represented by most statistical tests in common use, such as the t and F tests…iv The most widely used statistical tests (including the t and F statistics) were developed by Pearson, Galton, Edgeworth, Gosset, & Fisher in the late 19th and early 20th centuries.v Of course, being old isn’t the issue. After all, “ideal [methods]…represented by permutation tests, such as Fisher’s exact test”vi include those that have been around since the ‘30s. It was not until computers that many of these methods could be used due to their computational demands. Today, most researchers carry mobile phones that are more powerful than personal computers from the 80s and 90s, while Fisher’s exact test was formulated roughly a decade prior to perhaps the first rigorously defined theoretical computer.vii Meanwhile, far too many researchers us powerful computers equipped with expensive statistical software only to continue using the statistical tests that, as noted above, were compromises almost a century ago. We have possessed the capability to employ superior methods we do not for far too long. It is time to start exploiting the computational power we have at our disposal. By “exploiting their power” I do not mean the “garbage in, garbage out” approach that seems to necessarily follow from standard curricula. Even in top universities, such as Boston College, we find evidence that graduate curricula is trending toward (if not already at) the point of actually designing statistics and research methods courses around this approach. For example, at BC, we find that the multivariate statistics course Sociology 703 uses the textbook Statistics and Data Analysis for Nursing Research. Are there truly no adequate textbooks that explain the same methods but in a context fit for students of sociology? More troublesome is how graduate level research & statistics courses across the sciences have increasingly become more classes on how to use software than courses on statistics. In other words, we are teaching future researchers how to use utilize software such as SPSS in order to perform statistical techniques they do not understand. The reason for this is not just because such software enables researchers to perform complicated analyses or construct sophisticated models with minimal understanding. Another major contributing factor is easily found. We need look no farther than e.g., the 3rd edition of The Linear Algebra a Beginning Graduate Student Ought to Know, as linear algebra is the foundation for basically all of modern statistics:

Linear algebra is a living, active branch of mathematical research which is central to almost all other areas of mathematics and which has important applications in all branches of the physical and social sciences and in engineering. However, in recent years the content of linear algebra courses required to complete an undergraduate degree in mathematics—and even more so in other areas—at all but the most dedicated universities, has been depleted to the extent that it falls far short of what is in fact needed for graduate study and research or for real-world application. This is true not only in the areas of theoretical work but also in the areas of computational matrix theory, which are becoming more and more important to the working researcher as personal computers become a common and powerful tool. Students are not only less able to formulate or even follow mathematical proofs, they are also less able to understand the underlying mathematics of the numerical algorithms they must use. The resulting knowledge gap has led to frustration and recrimination on the part of both students and faculty alike, with each silently—and sometimes not so silently—blaming the other for the resulting state of affairs. This book is written with the intention of bridging that gap. This book is designed for students who will be entering graduate mathematics programs. It is intended, therefore, to address the shortcomings of students whose mathematical experience and sophistication is beyond that of many researchers across multiple sciences. In particular, it is aimed at the deficiencies graduate mathematics students present in a subject that happens to be the backbone of multivariate statistics and data analysis. Graduate students in neuroscience, managerial science, nursing, economics, etc. do not in general have the mathematical experience nor competency that a mathematics major does. It is understandable, then, that faced with the difficulties of graduate students whose university-level mathematical education consists of nothing or little other than a single introductory statistics course, graduate programs have opted to rely more on teaching students just enough to associate a certain experimental design or question with some set of procedures in SPSS or built-in R functions. Understandable, though, doesn’t mean acceptable. The following bibliography is designed to both introduce new researchers to techniques and methods frequently not taught, to point out the deficiencies in the methods that are taught, and to provide sources to remedy both issues. Sect. 1: Elementary & Introductory Texts Agresti, A. (2012). Categorical Data Analysis (3rd ed.) (Wiley Series in Probability and Statistics). Wiley. Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). Springer. DasGupta, A. (2010). Fundamentals of Probability: A First Course (Springer Texts in Statistics). New York: Springer. Dekking, F.M., Kraaikamp, C., Lopuhaä. H.P., & Meester L. E. (Eds.). (2005). A Modern Introduction to Probability and Statistics: Understanding why and how. Springer.

Everitt, B., & Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R (Use R!). Springer. Foster, J. J., Barkus, E., & Yavorsky, C. (2005). Understanding and Using Advanced Statistics. Sage. Gentle, J. E. (2007). Matrix Algebra: Theory, Computations, and Applications in Statistics (Springer Texts in Statistics). Springer. Harville, D. A. (2008). Matrix Algebra From a Statistician's Perspective. Springer. [This book has a companion Exercises and Solutions text] Lynch, Scott M. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences). Springer, 2007. Schwarz, W. (2007). 40 Puzzles and Problems in Probability and Mathematical Statistics. Springer. Wickens, T. D. (1995). The Geometry of Multivariate Statistics. Psychology Press. Wilcox, R. R. (2009). Basic Statistics: Understanding Conventional Methods and Modern Insights. Oxford University Press. Wilcox, R. (2011). Modern statistics for the social and behavioral sciences: A practical introduction. CRC press. Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd Ed.). Academic Press. Young, G. A., & Smith, R. L. (2005). Essentials of statistical inference (Cambridge Series in Statistical and Probabilistic Mathematics Vol. 16). Cambridge University Press. Sect. 2: Thinking Critically with Statistical and Probabilistic Reasoning Aliseda, A. (2006). Abductive Reasoning: Logical Investigations into Discovery and Explanation (Synthese Library Vol. 330). Dordrecht: Springer. Alon, N., & Spencer, J. H. (2000). The Probabilistic Method. (2nd Ed.) (Wiley-Interscience Series in Discrete Mathematics and Optimization). Wiley. Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics). Springer. Chen, M. H., Müller, P., Sun, D., Ye, K., & Dey, D. K. (2010). Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger. Springer.

Courgeau, D. (2012). Probability and Social Science: Methodological Relationships between the two Approaches (Methodos Series Vol. 10). Springer Science & Business Media. Das, S. K. (2008). Foundations Of Decision-Making Agents: Logic, Probability and Modality. World Scientific. Evans, M. J., & Rosenthal, J. S. (2010). Probability And Statistics: The Science Of Uncertainty. W. H. Freeman & Co. Hacking, Ian. An introduction to probability and inductive logic. Cambridge University Press, 2001. Haenni, R., Romeijn, J-W., Wheeler, G., & Williamson, J. (2011). Probabilistic Logics and Probabilistic Networks (Synthese Library Vol. 350). Springer. Howson, C., & Urbach, P. (2006). Scientific Reasoning: The Bayesian Approach (3rd Ed.). Open Court Publishing. Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge University Press. Jeffrey, R. (2004). Subjective Probability: The Real Thing. Cambridge University Press. Morgan, S. L., & Winship, C. (2007). Counterfactuals and Causal Inference- Methods and Principles for Social Research (Analytical Methods for Social Research). Cambridge University Press. Morrison, M. (2000). Unifying Scientific Theories: Physical Concepts and Mathematical Structures. Cambridge University Press. Nickerson, R. (2011). Mathematical reasoning: Patterns, problems, conjectures, and proofs. Psychology Press. Press, S. J. (2009). Subjective and Objective Bayesian Statistics: Principles, Models, and Applications (Wiley Series in Probability and Statistics Vol. 590). Wiley. Perea, A. (2012). Epistemic game theory: reasoning and choice. Cambridge University Press. Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities (Monographs on Statistics and Applied Probability Vol. 42). Chapman & Hall. Sect. 3: Subject Specific Texts Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton university press.

Aschwanden, M. (2011). Self-Organized Criticality in Astrophysics: The Statistics of Nonlinear Processes in the Universe. Springer. Baecher, G. B., & Christian, J. T. (2005). Reliability and Statistics in Geotechnical Engineering. Wiley. Barceló, J. A. (2009). Computational Intelligence in Archaeology. Information Science Reference. Beran, J. (2004). Statistics in Musicology. CRC Press. Chandler, R., & Scott, M. (2011). Statistical Methods for Trend Detection and Analysis in the Environmental Sciences (Statistics in Practice). Wiley. Chattopadhyay, A. K., & Chattopadhyay, T. (2014). Statistical Methods for Astronomical Data Analysis (Springer Series in Astrostatistics Vol. 3). Springer. Drennan, R. D. (2009). Statistics for Archaeologists: A Common Sense Approach (Interdisciplinary Contributions to Archaeology). Springer. Deutsch, A., Brusch, L., Byrne, H., de Vries, G., & Herzel, H. (2007). Mathematical Modeling of Biological Systems, Vol. I: Cellular Biophysics, Regulatory Networks, Development, Biomedicine, and Data Analysis (Modeling and Simulation in Science, Engineering and Technology). Birkhäuser Diggle, P., & Ribeiro, P. J. (2007). Model-based Geostatistics (Springer Series in Statistics). Springer. Feeman, T. G. (2010). The Mathematics of Medical Imaging: A Beginner's Guide (SUMAT). Springer. Feigelson, E. D., & Babu, G. J. (2012). Modern Statistical Methods for Astronomy: With R Applications. Cambridge University Press. Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (Eds.). (2010). Handbook of Spatial Statistics (Chapman & Hall CRC Handbook of Modern Statistical Methods). CRC Press. Gregory, P. (2005). Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support. Cambridge University Press. Hsieh, W. W. (2009). Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Cambridge University Press. Lamm, E., & Unger, R. (2011). Biological Computation (Mathematical and Computational Biology Series). CRC Press.

Landau, D. P., & Binder, K. (2009). A Guide to Monte Carlo Simulations in Statistical Physics (3rd ed.). Cambridge University Press. Loy, G. (2006). Musimathics: The Mathematical Foundations of Music (Vol. I). MIT Press. Loy, G. (2007). Musimathics: The Mathematical Foundations of Music (Vol. II). MIT Press. Mussardo, G. (2010). Statistical Field Theory: An Introduction to Exactly Solved Models in Statistical Physics (Oxford Graduate Texts). Oxford University Press. Oweiss, K. G. (Ed.). (2010). Statistical Signal Processing for Neuroscience and Neurotechnology. Academic Press. Paulson, D. S. (2008). Biostatistics and Microbiology: A Survival Manual. Springer. Pelletier, J. D. (2008). Quantitative Modeling of Earth Surface Processes. Cambridge University Press. Pham, H. (Ed.). (2006). Handbook of Engineering Statistics. Springer. Stumpf, M., Balding, D. J., & Girolami, M. (Eds.). (2011). Handbook of Statistical Systems Biology. Wiley. Wilks, D. S. (2011). Statistical Methods in the Atmospheric Sciences (3rd ed.) (International Geophysics Series Vol. 100). Academic Press. Wünschiers, R. (2013). Computational Biology: A Practical Introduction to BioData Processing and Analysis with Linux, MySQL, and R (2nd ed.). Springer. Yuryev, A. (Ed.). (2008). Pathway Analysis for Drug Discovery: Computational Infrastructure and Applications (Wiley Series on Technologies for the Pharmaceutical Industry).Wiley. Sect. 4: Some Important Studies on Critical Issues PLEASE review a good sample of studies found here: 402 Citations Questioning the Indiscriminate Use ofNull Hypothesis Significance Tests in Observational Studies (http://warnercnr.colostate.edu/~anderson/thompson1.html) Gigerenzer, G., Krauss, S., & Vitouch, O. (2004). The null ritual. In D. Kaplan (Ed.). (2004). The Sage handbook of quantitative methodology for the social sciences (pp. 391–408). Hubbard, R., & Lindsay, R. M. (2008). Why P values are not a useful measure of evidence in statistical significance testing. Theory & Psychology, 18(1), 69-88. Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method. American Psychologist, 56(1), 16.

Lambdin, C. (2012). Significance tests as sorcery: Science is empirical—significance tests are not. Theory & Psychology, 22(1), 67-90. McCloskey, D. N., & Ziliak, S. T. (2009). The Unreasonable Ineffectiveness of Fisherian" Tests" in Biology, and Especially in Medicine. Biological Theory, 4(1), 44. Sect. 5: History & Development of Statistics Dale, A. I. (1999). A History of Inverse Probability: From Thomas Bayes to Karl Pearson (2nd ed.) (Sources and Studies in the History of Mathematics and Physical Sciences). Springer. Godin, B. (2005). Measurement and Statistics on Science and Technology: 1920 to the Present (Routledge Studies in the History of Science, Technology, and Medicine). Routledge. Hald, A. (2003) A History of Probability and Statistics and Their Applications before 1750 (Wiley Series in Probability and Statistics). Wiley. Stigler, S. M. (1986). The History of Statistics: The Measurement of Uncertainty before 1900. Harvard University Press. Tabak, J. (2004). Probability And Statistics: The Science Of Uncertainty (History of Mathematics). Facts on File. Sect. 6: Statistical & Computational Software I have deliberately avoided including general introductions to software packages, one simple reason being that these abound and are too much alike for me to recommend those I think best written. Instead, I have tried to include sources that are specific to a subject or topic, or in general texts covering applications that general introductions (and giant reference books) lack. I have also deliberately not included any SPSS books (as I find SPSS to be a major factor in the current deficit in statistics education for many graduate students) and so too with Excel (which is just SPSS without the pre-loaded statistical methods). Borgo, M., Soranzo, A., & Grassi, M. (2012). MATLAB for Psychologists. Springer. Chekanov, S. V. (2010). Scientific data analysis using Jython Scripting and Java. Springer. Chen, D. G., & Peace, K. E. (2011). Clinical Trial Data Analysis Using R (Biostatistics Series). CRC Press. Chihara, L. M., & Hesterberg, T. C. (2012). Mathematical Statistics with Resampling and R. Wiley. Everitt, B. S., & Rabe-Hesketh, S. (2006). Handbook of Statistical Analyses Using Stata. CRC Press.

Foulkes, A. S. (2009). Applied Statistical Genetics with R: For Population-based Association Studies (Use R!). Springer. Haneberg, W. C. (2004). Computational Geosciences with Mathematica. Springer. Hastings, K. J. (2010). Introduction to Probability with Mathematica (2nd ed.) (Textbooks in Mathematics). CRC Press. Jones, O., Maillardet, R., & Robinson, A. (2012). Introduction to Scientific Programming and Simulation Using R. CRC Press. Kay, S. M. (2006). Intuitive Probability and Random Processes using MATLAB. Springer. Li, Y., & Baron, J. (2012). Behavioral Research Data Analysis with R (Use R!). Springer. Martinez, W. L., & Martinez, A. R. (2002). Computational statistics handbook with MATLAB. CRC press. Martinez, W. L., & Martinez, A. R. (2005). Exploratory Data Analysis with MATLAB (Series in Computer Science and Data Analysis). CRC Press. Marin, J. M., & Robert, C. P. (2014). Bayesian Essentials with R (Springer Texts in Statistics). Springer. Mathur, S. K. (2010). Statistical Bioinformatics with R. Academic Press. Pitt-Francis, J., & Whiteley, J. (2012). Guide to Scientific Computing in C++ (Undergraduate Topics in Computer Science). Springer. Ruskeepää, H. (2009). Mathematica Navigator: Mathematics, Statistics and Graphics (3rd ed.). Academic Press. Suess, E. A., & Trumbo, B. E. (2010). Introduction to Probability Simulation and Gibbs Sampling with R (Use R!). Springer. Sumathi, S., & Paneerselvam, S. (2010). Computational Intelligence Paradigms: Theory & Applications using MATLAB. CRC Press. Torrence, B. F., & Torrence, E. A. (2009). The Student's Introduction to Mathematica®: A Handbook for Precalculus, Calculus, and Linear Algebra (2nd ed.). Cambridge University Press. Wallisch, P., Lusignan, M. E., Benayoun, M. D., Baker, T. I., Dickey, A. S., & Hatsopoulos, N. G. (2014). Matlab for Neuroscientists: An Introduction to Scientific Computing for MATLAB (2nd ed.). Academic Press.

Williams, G. (2011). Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!). Springer. Zhao, Y. (2012). R and Data Mining: Examples and Case Studies. Academic Press. Zieffler, A. S., Harring, J. R., & Long, J. D. (2011). Comparing Groups: Randomization and Bootstrap Methods Using R. Wiley. Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R. Springer. Sect. 7: Some Useful and Important Series of Monographs, Conference Proceedings, and Volumes *Series names are given followed by further identification information. Usually, this is merely the publisher, but can include e.g., the series that a particular series is published under (Lecture Notes in Computer Science (LNCS), for example, publishes hundreds of conference proceedings subseries) or other information to ensure the reader can easily look up the series in question.* Advances in Computational Intelligence. Communications in Computer and Information Science (CCIS). Springer. Advances in Knowledge Discovery and Data Mining. Lecture notes in Computer Science (Subseries Lecture Notes in Artificial Intellegence). Springer. ASA-SIAM Series on Statistics and Applied Probability (Society for Industrial and Applied Mathematics) Contributions to Statistics (Springer) Handbook of Statistics (Elsevier) International Conference on Independent Component Analysis (ICA) and Source Separation. Lecture Notes in Computer Science (LNCS). Sublibrary: SL 1 – Theoretical Computer Science and General Issues. (Springer). International Conference on Swarm Intelligence. LNCS. Sublibrary: SL 1 – Theoretical Computer Science and General Issues. (Springer). Lecture Notes in Statistics (Springer) Mathematics and Visualization (Springer) Methodology in the Social Sciences (Guiford Press) Quantitative Applications in the Social Sciences (Sage)

Sage “Major Works”. Titles: SAGE Quantitative Research Methods (4 Vols.) SAGE Secondary Data Analysis (4 Vols.) SAGE Qualitative Research Methods (4 Vols.) Selected Works in Probability and Statistics (Springer) Springer Proceedings in Mathematics & Statistics Springer Series in Statistics (Springer) Springer Texts in Statistics (Springer) Statistics and Computing (Springer) Statistics: Textbooks and Monographs (Dekker) Studies in Classification, Data Analysis, and Knowledge Organization (Springer) Studies in Theoretical and Applied Statistics (Springer) Use R! (Springer) Wiley Series in Probability and Statistics (Wiley) Section 8: Sample of Texts: Not Your Grandmother’s Statistics Aggarwal, C. C. (2013). Outlier Analysis. Springer. Abraham, A., Hassanien, A. E., & Snášel, V. (2009). Computational Social Network Analysis: Trends, Tools and Research Advances (Computer Communications and Networks). Springer. Bandyopadhyay, S., & Saha, S. (2012). Unsupervised classification: similarity measures, classical and metaheuristic approaches, and applications. Springer. Baragona, R., Battaglia, F., & Poli, I. (2011). Evolutionary Statistical Procedures: An Evolutionary Computation Approach to Statistical Procedures Designs and Applications (Statistics and Computing). Springer. Borg, I., & Groenen, P. J. (2005). Modern Multidimensional Scaling: Theory and Applications (Springer Series in Statistics). Springer. Buckley, J. J. (2006). Fuzzy Probability and Statistics (Studies in Fuzziness and Soft Computing Vol. 196). Springer.

Caldarelli, G., & Vespignani, A. (Eds.) (2007). Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science (Complex Systems and Interdisciplinary Science Vol. 2). World Scientific. De Jong, K. A. (2006). Evolutionary Computation: A Unified Approach. MIT press. Di Ciaccio, A., Coli, M., & Ibanez, J. M. A. (Eds.). (2012). Advanced statistical methods for the analysis of large data-sets (Studies in Theoretical and Applied Statistics: Selected Papers of the Statistical Societies). Springer. Du, K. L., & Swamy, M. N. S. (2014). Neural Networks and Statistical Learning. Springer. Ferraty, F. (2011). Recent Advances in Functional Data Analysis and Related Topics (Contributions to Statistics). Springer. Fox, J. P. (2010). Bayesian item response modeling: Theory and applications (Statistics for Social and Behavioral Sciences). Springer. Fuller, W. A. (2011). Sampling Statistics (Wiley Series in Survey Methodology). Wiley. Gan, G., Ma, C., & Wu, J. (2007). Data clustering: theory, algorithms, and applications (ASASIAM Series on Statistics and Applied Probability Vol. 20). Siam. Gibilisco, P., Rogantin, R. E., & Wynn, H. (Eds.). (2010). Algebraic and geometric methods in statistics. Cambridge University Press. Grafarend, E. W. (2006). Linear and Nonlinear Models: Fixed Effects, Random Effects, and Mixed Models. Walter de Gruyter. Grigoletto, M., Lisi, F., & Petrone, S. (2013). Complex Models and Computational Methods in Statistics (Contributions to Statistics). Springer. Gut, A. (2005). Probability: a graduate course (Springer Series in Statistics). Springer. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.) (Springer Series in Statistics). Springer. Heyer, H. (2009). Structural Aspects in the Theory of Probability (2nd Enlarged ed.) (Series on Multivariate Analysis, Vol. 8). World Scientific. Hilbe, J. (2011). Negative Binomial Regression (2nd Ed.). Cambridge University Press.

Huber, C., Limnios, N., Mesbah, M., & Nikulin, M. (Eds.). (2010). Mathematical Methods in Survival Analysis, Reliability and Quality of Life (Applied Stochastic Methods Series). Wiley. Izenman, A. J. (2009). Modern multivariate statistical techniques: regression, classification, and manifold learning (Springer Texts in Statistics). Springer. Kogan, J., Nicholas, C., & Teboulle, M. (2006). Grouping Multidimensional Data: Recent Advances in Clustering. Springer. Krzanowski, W. J., & Hand, D. J. (2009). ROC Curves for Continuous Data (Monographs on Statistics and Applied Probability Vol. 111). CRC Press. Kulkarni, S., & Harman, G. (2011). An elementary introduction to statistical learning theory (Wiley Series in Probability and Statistics). Wiley. Li, F., & Klette, R. (2011). Euclidean Shortest Paths: Exact or Approximate Algorithms. Springer. Liu, H., & Motoda, H. (Eds.). (2007). Computational Methods of Feature Selection. CRC Press. Lomax, R. G., & Schumacker, R. E. (2010). A beginner's guide to structural equation modeling. (3rd ed.) Routledge. Mantovan, P. (Ed.). (2010). Complex Data Modeling and Computationally Intensive Statistical Methods (Contributions to Statistics). Springer. Mielke, P. W., & Berry, K. J. (2007). Permutation methods: a distance function approach (2nd ed.) (Springer Series in Statistics). Springer. Nishisato, S. (2006). Multidimensional nonlinear descriptive analysis. CRC Press. Palumbo, C. N. L. F., & Greenacre, M. J. (Eds.). (2010). Data Analysis and Classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the Società Italiana di Statistica (Studies in Classification, Data Analysis, and Knowledge Organization). Springer. Piccolo, D., Verde, R., & Vichi, M. (Eds.). (2011). Classification and Multivariate Analysis for Complex Data Structures (Studies in Classification, Data Analysis, and Knowledge Organization). Springer. Press, S. J. (2003). Subjective and objective Bayesian statistics: principles, models, and applications (2nd ed.). Wiley. Riesen, K., & Bunke, H. (2010). Graph classification and clustering based on vector space embedding (Series in Machine Perception and Artificial Intelligence, Vol. 77). World Scientific.

Robert, C. P. (2007). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics). Springer. Seber, G. A. F., & Wild, C. J. (2003). Nonlinear regression. Wiley. Silvapulle, M. J., & Sen, P. K. (2005). Constrained Statistical Inference: Order, Inequality, and Shape Constraints (Wiley Series in Probability and Statistics). Wiley. Stauffer, H. B. (2007). Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists. Wiley. Viertl, R. (2011). Statistical methods for fuzzy data. Wiley. Von Eye, A. (2002). Configural Frequency Analysis: Methods, Models, and Applications. LEA. Webb, A. R. (2002). Statistical pattern recognition (2nd ed.). Wiley. Zezula, P., Amato, G., Dohnal, V., & Batko, M. (2006). Similarity Search: The Metric Space Approach (Advances in Database Systems). Springer. Zielesny, A. (2011). From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence (Intelligent Systems Reference Library Vol. 18). Springer.                                                              i

 For many it is also a science or even THE science. For example, in his 1997 monograph Mathematics as a Science  of Patterns, Resnik calls mathematics the “queen of the sciences” but does not cite Gauss (whence comes the  quote, albeit in the form “Die Mathematik ist die Königin der Wissenschaften…”). Apparently, it is no longer a  quote but a proverb or aphorism/apothegm and hence requires no citation.  ii  Jasmin, L., & Ohara, P. T. (2004). Anatomical identification of neurons responsive to nociceptive stimuli. In Z.  D. Luo (Ed.) Pain Research (pp. 167‐188). Humana Press.  iii  Zikmund, W., Babin, B., Carr, J., & Griffin, M. (2009). Business Research Methods (8th Ed.). Cengage Learning.  iv  Mielke, P. W., & Berry, K. J. (2007). Permutation Methods: A Distance Function Approach (2nd Ed.). Springer.  v  See section 4  vi  Mielke & Berry, 2007.  vii  Fisher, R. A. (1922). On the interpretation of χ2 from contingency tables, and the calculation of P. Journal of the  Royal Statistical Society, 87‐94.; Turing, A.M. (1936), "On computable numbers, with an application to the  Entcheidungproblem", Proc. Lond. Math. Soc. II Ser., Vol. 42, pp. 230‐65.