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1

Clements, Michael P. On the limitations of comparing mean square forecast error. Oxford: Oxford University, Institute of Economics and Statistics, 1992.

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2

Baram, Yoram. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.

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3

Hoque, Asraul. The exact multiperiod mean-square forecast error for the first-order autoregressive model. London: London School of Economics, 1986.

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4

Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive modelwith an intercept. London: National Institute of Economic and Social Research, 1988.

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5

Mean justice: A town's terror, a prosecutor's power, a betrayal of innocence. New York: Simon & Schuster, 1999.

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6

Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive model with an intercept. London: International Centre for Economics and Related Disciplines, 1988.

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7

Davis, James Arthur. Peak-to-mean power control and error correction for OFDM transmission using Golay sequences and Reed-Muller codes. Palo Alto, CA: Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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8

Gaver, Donald Paul. Bayesian prediction of mean square errors with covariates. Monterey, Calif: Naval Postgraduate School, 1992.

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9

Um-- slips, stumbles, and verbal blunders, and what they mean. New York: Pantheon Books, 2007.

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10

Erard, Michael. Um: Slips, stumbles, and verbal blunders, and what they mean. New York, NY: Pantheon Books, 2008.

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11

Wegberg, Tanya A. Memory error, oder, Wie mein Vater über den Jordan ging. Reinbek bei Hamburg: Rowohlt Taschenbuch Verlag, 2009.

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12

Patterson, K. D. Revisions to the national income accounts: A multiplicative decomposition of mean square errors. Reading: University of Reading. Department of Economics, 1990.

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13

1944-, Trask R. L., ed. Say what you mean!: A troubleshooter's guide to English style and usage. Boston: David R. Godine, Publisher, 2005.

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14

Clark, Todd E. Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. Kansas City [Mo.]: Research Division, Federal Reserve Bank of Kansas City, 2004.

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15

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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16

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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17

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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18

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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19

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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20

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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21

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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22

Croley, Thomas E. Minimizing long-term wind set-up errors in estimated mean Erie and Superior lake levels. [Boulder, Colo.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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23

Hancock, Paul. Is that what you mean?: 100 common mistakes and how to correct them. Harlow: Pearson Education, 2001.

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24

Land where my fathers died. New York: Content Books, 2002.

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25

Willem W. S. Van Hees. A comparison of two estimates of standard error for a ratio-of-means estimator for a mapped-plot sample design in Southeast Alaska. Portland, OR: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2002.

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26

Hargreaves, Adam. Mr. Men: 12 days of Christmas. New York: Price Stern Sloan, 2011.

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27

United States. Congress. House. Committee on Ways and Means. Subcommittee on Health. Legislation to reduce medical errors: Hearing before the Subcommittee on Health of the Committee on Ways and Means, House of Representatives, One Hundred Seventh Congress, second session, September 10, 2002. Washington: U.S. G.P.O., 2003.

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28

Straka, Andy. Record of wrongs. Detroit: Gale/Cengage Learning, 2008.

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29

Crawford, Carl J. The truth. Pittsburgh, PA: SterlingHouse Publisher, Inc., 2010.

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30

United States. Congress. House. Committee on Ways and Means. Subcommittee on Health. Issues relating to medication errors: Hearing before the Subcommittee on Health of the Committee on Ways and Means, House of Representatives, One Hundred Third Congress, second session, September 20, 1994. Washington: U.S. G.P.O., 1994.

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31

United States. Congress. House. Committee on Ways and Means. Subcommittee on Health. Issues relating to medication errors: Hearing before the Subcommittee on Health of the Committee on Ways and Means, House of Representatives, One Hundred Third Congress, second session, September 20, 1994. Washington: U.S. G.P.O., 1994.

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32

Means, United States Congress House Committee on Ways and. H.R. 3570. For Correction of Error in Revised Statutes.: Imported Knit Goods: hearings before the United States House Committee on Ways and Means, Forty-Seventh Congress, first session, on May 16, 1882. Washington: U.S. G.P.O., 1985.

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33

United States. Congress. Senate. Committee on Commerce, Science, and Transportation. Subcommittee on Consumer Protection, Product Safety, and Insurance. Credit reports: What accuracy and errors mean for consumers : hearing before the Subcommittee on Consumer Protection, Product Safety, and Insurance of the Committee on Commerce, Science, and Transportation, United States Senate, One Hundred Thirteenth Congress, first session, May 7, 2013. Washington: U.S. Government Printing Office, 2013.

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34

Burgh, James. Political disquisitions: Or, An enquiry into public errors, defects, and abuses, illustrated by and established upon facts and remarks, extracted from a variety of authors, ancient and modern, calculated to draw the timely attention of government and people to a due consideration of the necessity and the means of reforming those errors, defects and abuses of restoring the Constitution and saving the state. Philadelphia: R. Bell, 1989.

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35

You just don't understand: Women and men in conversation. London: Virago, 1991.

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36

Tannen, Deborah. You just don't understand: Women and men in conversation. New York, NY: Morrow, 1990.

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37

You just don't understand: Women and men in conversation. New York: Morrow, 1990.

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38

Humes, Edward. Mean Justice. Tandem Library, 1999.

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39

Humes, Edward. Mean Justice. Turtleback Books Distributed by Demco Media, 2003.

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40

Humes, Edward. Mean Justice. Pocket, 2003.

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41

G, Kalit, and Ames Research Center, eds. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.

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42

G, Kalit, and Ames Research Center, eds. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.

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43

Siripanich, Pachitjanut Dasnanjali. Estimating root mean squared error in the one-way random model. 1987.

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44

O'Donnell, Eileen M. A mean squared error of prediction approach to the analysis of the combined array. 1994.

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45

Cortes-Colon, William. An analysis of the BOOTSTRAP method for estimating the mean squared error of statistical estimators. 1986.

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46

Humes, Edward. Mean Justice: A Town's Terror, A Prosecutor's Power, A Betrayal of Innocence. Simon & Schuster, 2012.

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47

Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.

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This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.
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48

Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 1. Least-squares Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0018.

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This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental designs, such as the use of a control population versus a divergence-selection approach. It concludes by discussing another linear model used mainly by plant breeders, generation-means analysis (GMA), wherein remnant seed for several generations of response are crossed and then grown in a common garden. Such an analysis can provide insight into the genetic nature of any response.
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49

Walsh, Bruce, and Michael Lynch. Theorems of Natural Selection: Results of Price, Fisher, and Robertson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0006.

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This chapter reviews a number of “theorems” of natural selection. These include exact results (true mathematical theorems): the Robertson-Price identity, Price's general expression for any form of selection response, and the Fisher-Price-Ewens version of Fisher's fundamental theorem. Their generality comes as the cost of usually being very difficult to apply. An important exception is the Robertson-Price identity, which expresses the within-generation change in the mean of a trait as its covariance with relative fitness. This chapter also examines three classic approximations: Fisher's fundamental theorem for the behavior of mean population fitness, and Robertson's secondary theorem and the breeder's equation for the expected response in a trait under selection, showing both how these results are connected and the error given by the various approximations. Finally, the chapter examines the connection between the additive variance of a trait and its correlation with fitness.
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50

Nothaft, C. Philipp E. Scandalous Error. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799559.001.0001.

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The Gregorian calendar reform of 1582, which provides the basis for the present-day Western civil calendar, has often been portrayed as a triumph of early modern scientific culture and an expression of papal ambition in the wake of the Counter-Reformation. Much less attention has been paid to our calendar’s intellectual and material roots in the European Middle Ages, when the reckoning of time by means of calendrical cycles was a topic of central importance to education and learned culture. For centuries prior to the Gregorian reform, astronomers, mathematicians, theologians, and even Church councils had been debating the necessity of improving or emending the existing ecclesiastical calendar, which throughout the Middle Ages kept growing out of sync with the astronomical phenomena at an alarming pace. Scandalous Error uses a broad base of sources, many of them unpublished or previously unknown, to paint the first full-scale survey of the medieval debate surrounding the calendar and its astronomical underpinnings.
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