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1

Berthold, M. Guide to intelligent data analysis: How to intelligently make sense of real data. London: Springer, 2010.

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2

Hailperin, Max. Load balancing using time series analysis for soft real time systems with statistically periodic loads. Stanford, Calif: Dept. of Computer Science, Stanford University, 1993.

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3

Data driven statistical methods. London: Chapman & Hall, 1998.

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4

Kiselev, I︠U︡. V. (I︠U︡riĭ Vasilʹevich), ed. Statistical methods of geophysical data processing. Singapore: World Scientific, 2010.

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5

Multivariate statistical simulation. New York: Wiley, 1987.

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6

McCuen, Richard H. Microcomputer applications in statistical hydrology. Englewood Cliffs, N.J: Prentice Hall, 1993.

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7

Fran, Rizzardi, ed. BLSS, the Berkeley interactive statistical system. New York: Norton, 1988.

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8

Hale, Robert L. MYSTAT: Statistical applications. Cambridge, MA: Course Technology, 1992.

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9

1947-, Carson John Hargadine, ed. Multiple processor systems for real-time applications. Englewood Cliffs, N.J: Prentice-Hall, 1985.

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10

Froeschl, Karl. Metadata management in statistical information processing: A unified framework for metadata-based processing of statistical data aggregates. Wien: Springer, 1997.

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11

Goyal, M. Computer-based numerical & statistical techniques. Hingham, Mass: Infinity Science Press, 2007.

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12

Design and analysis of distributed real-time systems. New York, NY: Intertext Publications, 1985.

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13

Härdle, W. XploRe: An interactive statistical computing environment. New York: Springer-Verlag, 1995.

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14

Lesik, Sally A. Applied statistical inference with MINITAB. Boca Raton: Chapman & Hall/CRC, 2010.

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15

Lesik, Sally. Applied statistical inference with MINITAB. Boca Raton: Chapman & Hall/CRC, 2010.

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16

Lesik, Sally A. Applied statistical inference with MINITAB. Boca Raton: Chapman & Hall/CRC, 2010.

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17

Arnold, Barry C. Conditional specification of statistical models. New York: Springer, 1999.

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18

Andersen, Erling B. The statistical analysis of categorical data. 3rd ed. Berlin: Springer-Verlag, 1994.

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19

Andersen, Erling B. The statistical analysis of categorical data. 2nd ed. Berlin: Springer-Verlag, 1991.

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20

1917-, Matsushita Kameo, ed. Statistical theory and data analysis: Proceedings of the Pacific Area Statistical Conference. Amsterdam: North-Holland, 1985.

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21

Pacific Area Statistical Conference (1st 1982 Tokyo, Japan). Statistical theory and data analysis: Proceedings of the Pacific Area Statistical Conference. Amsterdam: North Holland, 1985.

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22

M, Krishna C., ed. Readings in real-time systems. Los Alamitos, Calif: IEEE Computer Society Press, 1993.

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23

Lee, Y. H. Readings in real-time systems. Los Alamitos, Calif: IEEE Computer Society Press, 1993.

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24

Armstrong, Philip N. Data rearrangement and real-time computation. Santa Monica, CA: Rand Corp., 1993.

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25

W, Anderson T. Minitab guide to The statistical analysis of data. Palo Alto, CA: Scientific Press, 1986.

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26

Complex data modeling and computationally intensive statistical methods. Milan: Springer-Verlag Italia, 2010.

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27

Felinger, Attila. Data analysis and signal processing in chromatography. Amsterdam [Netherlands]: Elsevier, 1998.

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28

ADA in distributed real-time systems. New York: Intertext Publications, 1990.

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29

Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. Taylor & Francis Group, 2017.

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30

Winson-Geideman, Kimberly, Andy Krause, Clifford A. Lipscomb, and Nick Evangelopoulos. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. Taylor & Francis Group, 2017.

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31

Analyzing Compositional Data With R. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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32

Boobier, Tony. Analytics for Insurance: The Real Business of Big Data. Wiley & Sons, Limited, John, 2016.

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33

Boobier, Tony. Analytics for Insurance: The Real Business of Big Data. Wiley & Sons, Incorporated, John, 2016.

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34

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Capstone: Application of NLTS to Real-World Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0010.

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This chapter investigates the use of Extreme Value Statistics (EVS) to probabilistically model extreme events separated as unstructured noise in signal processing. We apply a version of EVS that computes the likelihood of extreme discrepancies exceeding a selected threshold value within a given time interval. In theory, exceedances follow a Generalized Pareto (GP) distribution, and we run diagnostics to determine how well the data actually fit this distribution. If we find a reasonable fit, we can invert the GP distribution to solve for quantiles providing a useful noise diagnostic: return level plots. Return level plots show the return periods expected before particular extreme noise levels return levels are realized.
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35

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Data Preprocessing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0006.

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Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test this null hypothesis using surrogate data testing. The combination of a noticeably regular shadow attractor, along with strong statistical rejection of fortuitous regularity, increases the probability that observed data are generated by deterministic real-world dynamics.
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36

Sprent, Peter. Data Driven Statistical Methods. CRC Press LLC, 2019.

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37

Sprent, Peter. Data Driven Statistical Methods. CRC Press LLC, 2019.

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38

Sprent, Peter. Data Driven Statistical Methods. CRC Press LLC, 2019.

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39

Sprent, Peter. Data Driven Statistical Methods. CRC Press LLC, 2019.

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40

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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41

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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42

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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43

Weerahandi, Samaradasa. Exact Statistical Methods for Data Analysis. Springer, 2014.

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44

Exact Statistical Methods for Data Analysis. Springer, 2003.

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45

1943-, Crowder M. J., ed. Statistical analysis of reliability data. London: Chapman & Hall, 1991.

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46

1943-, Crowder M. J., ed. Statistical analysis of reliability data. London: Chapman & Hall, 1994.

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47

Bahram, Javidi, and Horner Joseph L, eds. Real-time optical information processing. Boston: Academic Press, 1994.

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48

A, Aitkin Murray, ed. Statistical modelling in GLIM. Oxford [Oxfordshire]: Clarendon Press, 1989.

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49

Andrews, D. F., and J. E. Stafford. Symbolic Computation for Statistical Inference (Oxford Statistical Science Series). Oxford University Press, USA, 2000.

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50

Statistical Data Analysis Using Your Personal Computer. Sage Publications, Inc, 2001.

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