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Books on the topic 'Least Square Error'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Gauss, Carl Friedrich. Theory of the combination of observations least subject to error: Part one, part two, supplement = Theoria combinationis observationum erroribus minimus obnoxiae : pars prior, pars posterior, supplementum. Society for Industrial and Applied Mathematics, 1995.

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2

van, Huffel Sabine, and International Workshop on Total Least Squares and Errors-in-Variables Modeling (2nd : 1996 : Leuven, Belgium), eds. Recent advances in total least squares techniques and errors-in-variables modeling. Society for Industrial and Applied Mathematics, 1997.

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3

Bevington, Philip R. Data reduction and error analysis for thephysical sciences. 2nd ed. McGraw-Hill, 1994.

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4

Van Huffel, Sabine, and Philippe Lemmerling, eds. Total Least Squares and Errors-in-Variables Modeling. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-3552-0.

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5

D, Ghilani Charles, ed. Adjustment computations: Statistics and least squares in surveying and GIS. 3rd ed. John Wiley & Sons, 1997.

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6

Blais, J. A. R. Least-squares block adjustment of stereoscopic models and error analysis. University of Calgary, Division of Surveying Engineering, 1985.

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7

Rogers, Janet. Economic data: Handle with care. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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8

Center, Ames Research, ed. Round-off error propogation in four generally applicable, recursive, least-squares-estimation schemes. National Aeronautics and Space Administration, Ames Research Center, 1988.

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9

Huffel, Sabine. Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications. Springer Netherlands, 2002.

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10

name, No. Total least squares and errors-in-variables modeling: Analysis, algorithms and applications. Kluwer Academic, 2001.

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11

van, Huffel Sabine, and Lemmerling Philippe, eds. Total least squares and errors-in-variables modeling: Analysis, algorithms and applications. Kluwer Academic, 2002.

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12

Keith, Robinson D., ed. Data reduction and error analysis for the physical sciences. 2nd ed. McGraw-Hill, 1992.

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13

Keith, Robinson D., ed. Data reduction and error analysis for the physical sciences. 3rd ed. McGraw-Hill, 2003.

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14

Toutenburg, Helge. MSE-comparisons between restricted least squares, mixed, and weighted mixed estimators with special emphasize [i.e. emphasis] to nested restrictions. Akademie der Wissenschaften der DDR, Karl-Weierstrass-Institut für Mathematik, 1988.

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15

Ahmed, Mahbub I. Performance analysis of least square error [omega] filter for image reconstruction from projection. 1990.

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16

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

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17

Gauss, Carl Friedrich. Theory of the Combination of Observations Least Subject to Errors: Part One, Part Two, Supplement (Classics in Applied Mathematics). Society for Industrial Mathematics, 1987.

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18

(Editor), S. van Huffel, and P. Lemmerling (Editor), eds. Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications. Springer, 2002.

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19

Weld, Le Roy Dougherty. Theory of Errors and Least Squares. Creative Media Partners, LLC, 2018.

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20

Theory of Errors and Method of Least Squares. Creative Media Partners, LLC, 2022.

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21

Johnson, William. Theory of Errors and Method of Least Squares. Independently Published, 2018.

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22

Johnson, William Woolsey. Theory of Errors: And Method of Least Squares. Independently Published, 2019.

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23

The Theory Of Errors And Method Of Least Squares. Kessinger Publishing, LLC, 2007.

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24

Round-off error propogation in four generally applicable, recursive, least-squares-estimation schemes. National Aeronautics and Space Administration, Ames Research Center, 1988.

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25

Round-off error propogation in four generally applicable, recursive, least-squares-estimation schemes. National Aeronautics and Space Administration, Ames Research Center, 1988.

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26

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 desig

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27

Keith, Robinson D., ed. Data Reduction and Error Analysis for the Physical Sciences. 3rd ed. McGraw, 2002.

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28

Belgium) International Workshop on Total Least Squares and Errors-in-Variables Modeling (2nd : 1996 : Leuven. Recent Advances in Total Least Squares Techniques & Errors in Variables Modeling (Siam Proceedings in Applied Mathematics Ser. ; Vol. Pr 93)). Soc for Industrial & Applied Math, 1997.

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