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1

Harrison, R. S. Managing the estimating function. Ascot: Chartered Institute of Building, 1987.

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2

Langmaid, John. Estimating: Getting value from function. Bracknell: BSRIA, 2003.

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3

Drummond, Ian. Estimating with Mk II function point analysis. London: HMSO, 1992.

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4

Wildman, Valerie Jean. Estimating effective area surveyed with the cumulative distribution function. Corvallis, Or: Dept. of Statistics, Oregon State University, 1985.

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5

Software sizing and estimating: Mk II FPA (function point analysis). Chichester, West Sussex, England: Wiley, 1991.

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6

Canright, David. Estimating the spatial extent of attractors of iterated function systems. Monterey, Calif: Naval Postgraduate School, 1993.

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7

Cuenco, Michael L. Framework for estimating salmon survival as a function of habitat condition. Portland, Or: Columbia River Inter-Tribal Fish Commission, 1996.

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8

Treble, Stephen. Sizing and estimating software in practice: Making MKII function points work. London: McGraw-Hill, 1995.

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9

1962-, Douglas Neil, ed. Sizing and estimating software in practice: Making MKII function points work. London: McGraw-Hill, 1995.

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10

Attfield, C. L. F. Estimating the UK demand for money function: A test of two approaches. Bristol: University of Bristol, Department of Economics, 1995.

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11

Chen, Youyi. Extended quasi-likelihoods and optimal estimating functions. Toronto: University of Toronto, Dept. of Statistics, 1991.

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12

Toivanen, Otto. Oligopolistic services and cost function estimation. Coventry: University of Warwick Department of Economics, 1994.

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13

Toivanen, Otto. Oligopolistic services and cost function estimation. Coventry: Warwick University, Department of Economics, 1994.

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14

R, Thompson James. Nonparametric function estimation, modeling, and simulation. Philadelphia: Society for Industrial and Applied Mathematics, 1990.

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15

Tree structured function estimation with Haar wavelets. Hamburg: Verlag Dr. Kovač, 1999.

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16

Pollock, D. S. G. On the criterion function for ARMA estimation. London: Queen Mary and Westfield College. Department of Economics, 1991.

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17

Evans, Michael. An example concerning the likelihood function. Toronto: University of Toronto, Dept. of statistics, 1988.

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18

Tibshirani, Robert. Slide functions for projection pursuit regression and neural networks. Toronto: University of Toronto, Department of Statistics, 1992.

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19

Laibson, David I. Estimating discount functions with consumption choices over the lifecycle. Cambridge, Mass: National Bureau of Economic Research, 2007.

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20

Levinsohn, James Alan. Estimating production functions using inputs to control for unobservables. Cambridge, MA: National Bureau of Economic Research, 2000.

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21

Laibson, David. Estimating discount functions with consumption choices over the lifecycle. Cambridge, MA: National Bureau of Economic Research, 2007.

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22

Frain, John. Estimating investment functions for a small-scale econometric model. Dublin: Economic Analysis, Research and Publications Department, Central Bank of Ireland, 1996.

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23

1954-, Marron James Stephen, American Mathematical Society, Institute of Mathematical Statistics, and Society for Industrial and Applied Mathematics., eds. Function estimates: Proceedings of a conference held July 28-August 3, 1985. Providence, R.I: American Mathematical Society, 1986.

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24

Hillegers, L. T. M. E. The estimation of parameters in functional relationship models. [Maastricht: L.T.M.E. Hillegers, 1986.

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25

G, Roussas George, North Atlantic Treaty Organization. Scientific Affairs Division., and Advanced Study Institute on Nonparametric Functional Estimation and Related Topics (1990 : Island of Spetsai, Greece), eds. Nonparametric functional estimation and related topics. Dordrecht: Kluwer Academic Publishers, 1991.

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26

Newey, Whitney K. Undersmoothing and bias corrected functional estimation. Cambridge, Mass: Massachusetts Institute of Technology, 1998.

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27

Dodd, John M. FTEQ: The functional time estimation questionnaire. Novato, Calif: Academic Therapy Publications, 1990.

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28

Nagelkerke, Nico J. D. Maximum Likelihood Estimation of Functional Relationships. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2858-5.

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29

Roussas, George, ed. Nonparametric Functional Estimation and Related Topics. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3222-0.

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30

Nagelkerke, Nico J. D. Maximum likelihood estimation of functional relationships. Berlin: Springer-Verlag, 1992.

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31

Chernozhukov, Victor. Improving point and interval estimates of monotone functions by rearrangement. Cambridge, MA: Massachusetts Institute of Technology, Dept. of Economics, 2008.

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32

Gilʹ, M. I. Norm estimations for operator-valued functions and applications. New York: M. Dekker, 1995.

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33

Norm estimations for operator-valued functions and applications. New York: M. Dekker, 1995.

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34

1950-, Vályi István, ed. Ellipsoidal calculus for estimation and control. Laxenburg, Austria: IIASA, 1997.

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35

Dotson, Kelly J. Development of confidence limits by pivotal functions for estimating software reliability. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Office, 1987.

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36

J, Wales Terence, ed. Demand system specification and estimation. New York: Oxford University Press, 1992.

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37

Knight, John L. Estimation of stationary stochastic processes via the empirical characteristic function. Cambridge: Department of Applied Economics, University of Cambridge, 1994.

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38

Kolstad, Charles D. Dynamic specification error in cost function and factor demand estimation. Champaign: University of Illinois at Urbana-Champaign, 1992.

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39

Dahlbom, Ulla. Estimation of regression functions with certain monotonicity and concavity/convexity restrictions. Göteborg: Göteborg University, 1994.

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40

Hanfelt, John. General Estimating Function Theory. Taylor & Francis Group, 2020.

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41

Hanfelt, John. General Estimating Function Theory. Taylor & Francis Group, 2020.

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42

Hanfelt, John. General Estimating Function Theory. Taylor & Francis Group, 2020.

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43

Hanfelt, John. General Estimating Function Theory. Taylor & Francis Group, 2020.

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44

Hanfelt, John. General Estimating Function Theory. Chapman & Hall/CRC, 2009.

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45

Central Computer and Telecommunications Agency., ed. Estimating with Mk [2] function point analysis. [London]: Central Computer and Telecommunications Agency, 1991.

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46

Speeckaert, Marijn, and Jopis Delanghe. Assessment of renal function. Edited by Christopher G. Winearls. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0007.

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Abstract:
Glomerular filtration rate (GFR) can be measured as the clearance of exogenous or endogenous filtration markers. Practical formulas permit estimation of creatinine clearance or GFR without timed urine collections in many stable patients with CKD. Standardization of serum creatinine is important for all of these estimation methods and implementing traceability of the assays to the new global SRM 967 standard has led to changes in clinical decision-making criteria. Calibration to an IDMS reference produces a lowering of serum creatinine values by 10–30% for most methods. Serum creatinine concentration depends on age, gender and muscle mass. Cystatin C is an alternative marker of GFR, but estimation is more expensive and it is not clear that it has a useful place in routine practice. The MDRD Study equation was validated in the framework of the Modification of Diet in Renal Diseases study. It is superior to the Cockcroft and Gault formula for estimating Creatinine Clearance in most people. In 2009, the CKD Epidemiology Collaboration (CKD-EPI) formula was introduced, which provides a more accurate estimation for patients with GFR values between 60 and 90 mL/min. In children, the Schwartz formula is frequently used. Some urinary markers of kidney disease are also discussed.
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47

Epstein, Natan P., and Corrado Macchiarelli. Estimating Poland's Potential Output: A Production Function Approach. International Monetary Fund, 2010.

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48

Epstein, Natan P., and Corrado Macchiarelli. Estimating Poland's Potential Output: A Production Function Approach. International Monetary Fund, 2010.

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49

Epstein, Natan P., and Corrado Macchiarelli. Estimating Poland's Potential Output: A Production Function Approach. International Monetary Fund, 2010.

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50

Biddle, Jeff E. Progress Through Regression: Estimating the Production Function, 1927-1965. University of Cambridge ESOL Examinations, 2020.

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