Relevant bibliographies by topics / Weighted least squares

Academic literature on the topic 'Weighted least squares'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Weighted least squares"

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Bloch, Daniel A., and Lincoln E. Moses. "Nonoptimally Weighted Least Squares." American Statistician 42, no. 1 (February 1988): 50. http://dx.doi.org/10.2307/2685260.

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Bloch, Daniel A., and Lincoln E. Moses. "Nonoptimally Weighted Least Squares." American Statistician 42, no. 1 (February 1988): 50–53. http://dx.doi.org/10.1080/00031305.1988.10475521.

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Romano, Joseph P., and Michael Wolf. "Resurrecting weighted least squares." Journal of Econometrics 197, no. 1 (March 2017): 1–19. http://dx.doi.org/10.1016/j.jeconom.2016.10.003.

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Kiers, Henk A. L. "Weighted least squares fitting using ordinary least squares algorithms." Psychometrika 62, no. 2 (June 1997): 251–66. http://dx.doi.org/10.1007/bf02295279.

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Amiri-Simkooei, A., and S. Jazaeri. "Weighted total least squares formulated by standard least squares theory." Journal of Geodetic Science 2, no. 2 (January 1, 2012): 113–24. http://dx.doi.org/10.2478/v10156-011-0036-5.

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Weighted total least squares formulated by standard least squares theoryThis contribution presents a simple, attractive, and flexible formulation for the weighted total least squares (WTLS) problem. It is simple because it is based on the well-known standard least squares theory; it is attractive because it allows one to directly use the existing body of knowledge of the least squares theory; and it is flexible because it can be used to a broad field of applications in the error-invariable (EIV) models. Two empirical examples using real and simulated data are presented. The first example, a linear regression model, takes the covariance matrix of the coefficient matrix asQA=Qn⊗Qm, while the second example, a 2-D affine transformation, takes a general structure of the covariance matrixQA.The estimates for the unknown parameters along with their standard deviations of the estimates are obtained for the two examples. The results are shown to be identical to those obtained based on thenonlinearGauss-Helmert model (GHM). We aim to have an impartial evaluation of WTLS and GHM. We further explore the high potential capability of the presented formulation. One can simply obtain the covariance matrix of the WTLS estimates. In addition, one can generalize the orthogonal projectors of the standard least squares from which estimates for the residuals and observations (along with their covariance matrix), and the variance of the unit weight can directly be derived. Also, the constrained WTLS, variance component estimation for an EIV model, and the theory of reliability and data snooping can easily be established, which are in progress for future publications.
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Cohen, Albert, and Giovanni Migliorati. "Optimal weighted least-squares methods." SMAI journal of computational mathematics 3 (October 1, 2017): 181–203. http://dx.doi.org/10.5802/smai-jcm.24.

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Park, Sungwoo, and Dianne P. O’Leary. "Implicitly-weighted total least squares." Linear Algebra and its Applications 435, no. 3 (August 2011): 560–77. http://dx.doi.org/10.1016/j.laa.2010.06.020.

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Kraus, F. J., and M. F. Senning. "Parameter Weighted Least Squares Fitting." IFAC Proceedings Volumes 18, no. 11 (September 1985): 501–6. http://dx.doi.org/10.1016/s1474-6670(17)60174-5.

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Magnus, Jan R., Wendun Wang, and Xinyu Zhang. "Weighted-Average Least Squares Prediction." Econometric Reviews 35, no. 6 (October 20, 2014): 1040–74. http://dx.doi.org/10.1080/07474938.2014.977065.

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Chen, Jiahua, and Jun Shao. "Iterative Weighted Least Squares Estimators." Annals of Statistics 21, no. 2 (June 1993): 1071–92. http://dx.doi.org/10.1214/aos/1176349165.

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Dissertations / Theses on the topic "Weighted least squares"

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Wang, Zhen. "Semi-parametric Bayesian Models Extending Weighted Least Squares." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236786934.

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Wei, Fei. "Weighted least-squares finite element methods for PIV data assimilation." Thesis, Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/wei/WeiF0811.pdf.

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The ability to diagnose irregular flow patterns clinically in the left ventricle (LV) is currently very challenging. One potential approach for non-invasively measuring blood flow dynamics in the LV is particle image velocimetry (PIV) using microbubbles. To obtain local flow velocity vectors and velocity maps, PIV software calculates displacements of microbubbles over a given time interval, which is typically determined by the actual frame rate. In addition to the PIV, ultrasound images of the left ventricle can be used to determine the wall position as a function of time, and the inflow and outflow fluid velocity during the cardiac cycle. Despite the abundance of data, ultrasound and PIV alone are insufficient for calculating the flow properties of interest to clinicians. Specifically, the pressure gradient and total energy loss are of primary importance, but their calculation requires a full three-dimensional velocity field. Echo-PIV only provides 2D velocity data along a single plane within the LV. Further, numerous technical hurdles prevent three-dimensional ultrasound from having a sufficiently high frame rate (currently approximately 10 frames per second) for 3D PIV analysis. Beyond microbubble imaging in the left ventricle, there are a number of other settings where 2D velocity data is available using PIV, but a full 3D velocity field is desired. This thesis develops a novel methodology to assimilate two-dimensional PIV data into a three-dimensional Computational Fluid Dynamics simulation with moving domains. To illustrate and validate our approach, we tested the approach on three different problems: a flap displaced by a fluid jut; an expanding hemisphere; and an expanding half ellipsoid representing the left ventricle of the heart. To account for the changing shape of the domain in each problem, the CFD mesh was deformed using a pseudo-solid domain mapping technique at each time step. The incorporation of experimental PIV data can help to identify when the imposed boundary conditions are incorrect. This approach can also help to capture effects that are not modeled directly like the impacts of heart valves on the flow of blood into the left ventricle.
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Rosopa, Patrick. "A COMPARISON OF ORDINARY LEAST SQUARES, WEIGHTED LEAST SQUARES, AND OTHER PROCEDURES WHEN TESTING FOR THE EQUALITY OF REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2311.

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When testing for the equality of regression slopes based on ordinary least squares (OLS) estimation, extant research has shown that the standard F performs poorly when the critical assumption of homoscedasticity is violated, resulting in increased Type I error rates and reduced statistical power (Box, 1954; DeShon & Alexander, 1996; Wilcox, 1997). Overton (2001) recommended weighted least squares estimation, demonstrating that it outperformed OLS and performed comparably to various statistical approximations. However, Overton's method was limited to two groups. In this study, a generalization of Overton's method is described. Then, using a Monte Carlo simulation, its performance was compared to three alternative weight estimators and three other methods. The results suggest that the generalization provides power levels comparable to the other methods without sacrificing control of Type I error rates. Moreover, in contrast to the statistical approximations, the generalization (a) is computationally simple, (b) can be conducted in commonly available statistical software, and (c) permits post hoc analyses. Various unique findings are discussed. In addition, implications for theory and practice in psychology and future research directions are discussed.
Ph.D.
Department of Psychology
Sciences
Psychology
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Fang, Xing [Verfasser]. "Weighted total least squares solutions for applications in geodesy / Xing Fang." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2011. http://d-nb.info/1015446590/34.

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Viklands, Thomas. "Algorithms for the Weighted Orthogonal Procrustes Problem and other Least Squares Problems." Doctoral thesis, Umeå : Umeå universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-730.

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Cheng, Chao-heh. "Calculations for positioning with the Global Navigation Satellite System." Ohio : Ohio University, 1998. http://www.ohiolink.edu/etd/view.cgi?ohiou1176839268.

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Kamunge, Daniel. "A non-linear weighted least squares gas turbine diagnostic approach and multi-fuel performance simulation." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/5612.

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The gas turbine which has found numerous applications in Air, Land and Sea applications, as a propulsion system, electricity generator and prime mover, is subject to deterioration of its individual components. In the past, various methodologies have been developed to quantify this deterioration with varying degrees of success. No single method addresses all issues pertaining to gas turbine diagnostics and thus, room for improvement exists. The first part of this research investigates the feasibility of non-linear W eighted Least Squares as a gas turbine component deterioration quantification tool. Two new weighting schemes have been developed to address measurement noise. Four cases have been run to demonstrate the non-linear weighted least squares method, in conjunction with the new weighting schemes. Results demonstrate that the non-linear weighted least squares method effectively addresses measurement noise and quantifies gas path component faults with improved accuracy over its linear counterpart and over methods that do not address measurement noise. Since Gas turbine diagnostics is based on analysis of engine performance at given ambient and power setting conditions; accurate and reliable engine performance modelling and simulation models are essential for meaningful gas turbine diagnostics. The second part of this research therefore sought to develop a multi-fuel and multi-caloric simulation method with the view of improving simulation accuracy. The method developed is based on non-linear interpolation of fuel tables. Fuel tables for Jet-A, UK Natural gas, Kerosene and Diesel were produced. Six case studies were carried out and the results demonstrate that the method has significantly improved accuracy over linear interpolation based methods and methods that assume thermal perfection.
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Shulga, Yelena A. "Model-based calibration of a non-invasive blood glucose monitor." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/58.

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This project was dedicated to the problem of improving a non-invasive blood glucose monitor being developed by the VivaScan Corporation. The company has made some progress in the non-invasive blood glucose device development and approached WPI for a statistical assistance in the improvement of their model in order to predict the glucose level more accurately. The main goal of this project was to improve the ability of the non-invasive blood glucose monitor to predict the glucose values more precisely. The goal was achieved by finding and implementing the best regression model. The methods included ordinary least squared regression, partial least squares regression, robust regression method, weighted least squares regression, local regression, and ridge regression. VivaScan calibration data for seven patients were analyzed in this project. For each of these patients, the individual regression models were built and compared based on the two factors that evaluate the model prediction ability. It was determined that partial least squares and ridge regressions are two best methods among the others that were considered in this work. Using these two methods gave better glucose prediction. The additional problem of data reduction to minimize the data collection time was also considered in this work.
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Oxby, Paul W. "Multivariate weighted least squares as a preferable alternative to the determinant criterion for multiresponse parameter estimation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22225.pdf.

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Kim, Wonho. "An improved bus signal priority system for networks with nearside bus stops." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1460.

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Bus Signal Priority (BSP), which has been deployed in many cities around the world, is a traffic signal enhancement strategy that facilitates efficient movement of buses through signalized intersections. Most BSP systems do not work well in transit networks with nearside bus stop because of the uncertainty in dwell time. Unfortunately, most bus stops on arterial roadways are of this type in the U.S. This dissertation showed that dwell time at nearside bus stops could be modeled using weighted least squares regression. More importantly, the prediction intervals associated with the estimate dwell time were calculated. These prediction intervals were subsequently used in the improved BSP algorithm that attempted to reduce the negative effects of nearside bus stops on BSP operations. The improved BSP algorithm was tested on urban arterial section of Bellaire Boulevard in Houston, Texas. VISSIM, a micro simulation model was used to evaluate the performance of the BSP operations. Prior to evaluating the algorithm, the parameters of the micro simulation model were calibrated using an automated Genetic Algorithm based methodology in order to make the model accurately represent the traffic conditions observed in the field. It was shown that the improved BSP algorithm significantly improved the bus operations in terms of bus delay. In addition, it was found that the delay to other vehicles on the network was not statistically different from other BSP algorithms currently being deployed. It is hypothesized that the new approach would be particularly useful in North America where there are many transit systems that utilize nearside bus stops in their networks.
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Books on the topic "Weighted least squares"

1

Hough, Patricia D. Complete orthogonal decomposition for weighted least squares. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1994.

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Hough, Patricia D. Stable and efficient solution of weighted least-squares problems with applications in interior point methods. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1996.

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Hesselager, Ole. On the asymptotic distribution of weighted least squares estimators. Copenhagen: Laboratory of Actuarial Mathematics, University of Copenhagen, 1986.

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Koenker, Roger. A note on Amemiya's form of the weighted least squares estimator. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1990.

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Tobia, John. A time-varying analysis of the exponentially data weighted recursive least squares (EDW-RLS) algorithm. Ottawa: National Library of Canada, 1992.

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

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Schwenzfeger, K. J. Comparison of ERS-1 scatterometer Monte Carlo performance simulations using a weighted nonlinear least-squares and a maximum likelihood estimation method. Neubiberg: Hochschule der Bundeswehr München, 1985.

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Supremum and Stability of Weighted Pseudoinverses and Weighted Least Squares Problems: Analysis and Computations. Nova Science Publishers, 2001.

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Strutz, Tilo. Data Fitting and Uncertainty: A practical introduction to weighted least squares and beyond. Springer Vieweg, 2015.

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Hipolito, Alexander L. A weighted least squares approch to direction finding in mathematical programming. 1993.

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Book chapters on the topic "Weighted least squares"

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Sheather, Simon J. "Weighted Least Squares." In Springer Texts in Statistics, 115–23. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-09608-7_4.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Weighted Least Squares." In Machine Learning in Medicine, 95–103. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7869-6_10.

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Taskinen, S., and K. Nordhausen. "Iterative Weighted Least Squares." In Encyclopedia of Mathematical Geosciences, 1–4. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_169-1.

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Sterchi, Martin, and Michael Wolf. "Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence." In Robustness in Econometrics, 135–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50742-2_9.

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Markovsky, Ivan, and Sabine Van Huffel. "On Weighted Structured Total Least Squares." In Large-Scale Scientific Computing, 695–702. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11666806_80.

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Fang, X., and H. Kutterer. "On the Weighted Total Least Squares Solutions." In International Association of Geodesy Symposia, 45–50. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10828-5_7.

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Müller, Hans-Georg. "Kernel and Local Weighted Least Squares Methods." In Lecture Notes in Statistics, 26–46. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3926-0_4.

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White, Halbert, and Maxwell Stinchcombe. "Adaptive Efficient Weighted Least Squares With Dependent Observations." In Directions in Robust Statistics and Diagnostics, 337–63. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-4444-8_19.

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Baryamureeba, Venansius. "On Solving Large-Scale Weighted Least Squares Problems." In Lecture Notes in Computer Science, 59–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45262-1_8.

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Mašíček, L. "Consistency of the Least Weighted Squares Regression Estimator." In Theory and Applications of Recent Robust Methods, 183–94. Basel: Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_17.

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Conference papers on the topic "Weighted least squares"

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Fiori, S., and F. Piazza. "Weighted least-squares blind deconvolution." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.760640.

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Ker, Andrew D. "Optimally weighted least-squares steganalysis." In Electronic Imaging 2007. SPIE, 2007. http://dx.doi.org/10.1117/12.704606.

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Garzelli, Andrea, Luciano Alparone, Luca Capobianco, and Filippo Nencini. "Fast weighted least squares pan-sharpening." In SPIE Europe Remote Sensing, edited by Lorenzo Bruzzone, Claudia Notarnicola, and Francesco Posa. SPIE, 2009. http://dx.doi.org/10.1117/12.830417.

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Kaplan, Nur Huseyin, and Isin Erer. "Fast Weighted Least Squares Based Pansharpening." In 2019 9th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 2019. http://dx.doi.org/10.1109/rast.2019.8767865.

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Aleixo, Rafael, and Hugo Lara Urdaneta. "Least-Squares Delayed Weighted Gradient Method." In CNMAC 2022- XLI Congresso Nacional de Matemática Aplicada e Computacional. SBMAC, 2022. http://dx.doi.org/10.5540/03.2022.009.01.0267.

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He, Zifen, and Yinhui Zhang. "Weighted Least-squares Color Digital Halftoning." In 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isrme-15.2015.21.

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Liu, Yonghuai, Hong Zhou, Xuan Su, Mingxin Ni, and Robert J. Lloyd. "Transforming Least Squares to Weighted Least Squares for Accurate Range Image Registration." In Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06). IEEE, 2006. http://dx.doi.org/10.1109/3dpvt.2006.142.

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Iatrou, Maria, Bruno De Man, Kedar Khare, and Thomas M. Benson. "A 3D study comparing filtered backprojection, weighted least squares, and penalized weighted least squares for CT reconstruction." In 2007 IEEE Nuclear Science Symposium Conference Record. IEEE, 2007. http://dx.doi.org/10.1109/nssmic.2007.4436689.

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Sacchi, Mauricio D., and Tadeusz J. Ulrych. "Model re‐weighted least‐squares Radon operators." In SEG Technical Program Expanded Abstracts 1995. Society of Exploration Geophysicists, 1995. http://dx.doi.org/10.1190/1.1887410.

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Zhou, CanLin, Shuchun Si, XiaoLei Li, Zhenkun Lei, and YanJie Li. "Robust weighted least-squares phase-unwrapping algorithm." In Eleventh International Conference on Information Optics and Photonics (CIOP 2019), edited by Hannan Wang. SPIE, 2019. http://dx.doi.org/10.1117/12.2547663.

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Reports on the topic "Weighted least squares"

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Daubechies, Ingrid, Ronald DeVore, Massimo Fornasier, and C. S. Gunturk. Iteratively Re-weighted Least Squares Minimization for Sparse Recovery. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada528510.

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Carroll, Raymond J., and Daren B. Cline. An Asymptotic Theory for Weighted Least Squares with Weights Estimated by Replication. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada198000.

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McKeague, Ian W. Asymptotic Theory for Weighted Least Squares Estimators in Aalen's Additive Risk Model. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada191085.

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Banks, H. T., Zachary R. Kenz, and W. C. Thompson. An Extension of RSS-based Model Comparison Tests for Weighted Least Squares. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada568205.

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Chiu, Shean-Tsong. Weighted Least Squares Estimators on the Frequency Domain for the Parameters of a Time Series. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada455201.

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Nuttall, Albert H. Weighted Least Squares Fit of a Real Tone to Discrete Data, by Means of an Efficient Fast Fourier Transform Search. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada185910.

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Carroll, Raymond J., Marie Davidian, and David Ruppert. Estimating Weights in Heteroscedastic Regression Models by Applying Least Squares to Squared or Absolute Residuals. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada168487.

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Over, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong, and Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, June 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7696531.bard.

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To reduce financial risk and N losses to the environment, fertilization methods are needed that improve NUE and increase the quality of wheat. In the literature, ample attention is given to grid-based and zone-based soil testing to determine the soil N available early in the growing season. Plus, information is available on in-season N topdressing applications as a means of improving GPC. However, the vast majority of research has focused on wheat that is grown under N limiting conditions in sub-humid regions and irrigated fields. Less attention has been given to wheat in dryland that is water limited. The objectives of this study were to: (1) determine accuracy in determining GPC of HRSW in Israel and SWWW in Oregon using on-combine optical sensors under field conditions; (2) develop a quantitative relationship between image spectral reflectance and effective crop physiological parameters; (3) develop an operational precision N management procedure that combines variable-rate N recommendations at planting as derived from maps of grain yield, GPC, and test weight; and at mid-season as derived from quantitative relationships, remote sensing, and the DSS; and (4) address the economic and technology-transfer aspects of producers’ needs. Results from the research suggest that optical sensing and the DSS can be used for estimating the N status of dryland wheat and deciding whether additional N is needed to improve GPC. Significant findings include: 1. In-line NIR reflectance spectroscopy can be used to rapidly and accurately (SEP <5.0 mg g⁻¹) measure GPC of a grain stream conveyed by an auger. 2. On-combine NIR spectroscopy can be used to accurately estimate (R² < 0.88) grain test weight across fields. 3. Precision N management based on N removal increases GPC, grain yield, and profitability in rainfed wheat. 4. Hyperspectral SI and partial least squares (PLS) models have excellent potential for estimation of biomass, and water and N contents of wheat. 5. A novel heading index can be used to monitor spike emergence of wheat with classification accuracy between 53 and 83%. 6. Index MCARI/MTVI2 promises to improve remote sensing of wheat N status where water- not soil N fertility, is the main driver of plant growth. Important features include: (a) computable from commercial aerospace imagery that include the red edge waveband, (b) sensitive to Chl and resistant to variation in crop biomass, and (c) accommodates variation in soil reflectance. Findings #1 and #2 above enable growers to further implement an efficient, low cost PNM approach using commercially available on-combine optical sensors. Finding #3 suggests that profit opportunities may exist from PNM based on information from on-combine sensing and aerospace remote sensing. Finding #4, with its emphasis on data retrieval and accuracy, enhances the potential usefulness of a DSS as a tool for field crop management. Finding #5 enables land managers to use a DSS to ascertain at mid-season whether a wheat crop should be harvested for grain or forage. Finding #6a expands potential commercial opportunities of MS imagery and thus has special importance to a majority of aerospace imaging firms specializing in the acquisition and utilization of these data. Finding #6b on index MCARI/MVTI2 has great potential to expand use of ground-based sensing and in-season N management to millions of hectares of land in semiarid environments where water- not N, is the main determinant of grain yield. Finding #6c demonstrates that MCARI/MTVI2 may alleviate the requirement of multiple N-rich reference strips to account for soil differences within farm fields. This simplicity will be less demanding of grower resources, promising substantially greater acceptance of sensing technologies for in-season N management.
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