Relevant bibliographies by topics / Iterative proportional fitting

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Iterative proportional fitting'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Iterative proportional fitting"

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Reffel, Fabian P. "Termination of the iterative proportional fitting procedure." Statistics & Probability Letters 92 (September 2014): 59–64. http://dx.doi.org/10.1016/j.spl.2014.05.002.

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Ruschendorf, Ludger. "Convergence of the Iterative Proportional Fitting Procedure." Annals of Statistics 23, no. 4 (1995): 1160–74. http://dx.doi.org/10.1214/aos/1176324703.

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Cramer, Erhard. "Conditional Iterative Proportional Fitting for Gaussian Distributions." Journal of Multivariate Analysis 65, no. 2 (1998): 261–76. http://dx.doi.org/10.1006/jmva.1998.1739.

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Johnston, R. J., and C. J. Pattie. "Entropy-Maximizing and the Iterative Proportional Fitting Procedure." Professional Geographer 45, no. 3 (1993): 317–22. http://dx.doi.org/10.1111/j.0033-0124.1993.00317.x.

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Kim, Sung-ho. "Iterative proportional fitting for nonhierarchical log-linear models." Communications in Statistics - Theory and Methods 26, no. 6 (1997): 1443–60. http://dx.doi.org/10.1080/03610929708831992.

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Kolenikov, Stanislav. "Calibrating Survey Data using Iterative Proportional Fitting (Raking)." Stata Journal: Promoting communications on statistics and Stata 14, no. 1 (2014): 22–59. http://dx.doi.org/10.1177/1536867x1401400104.

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Gietl, Christoph, and Fabian P. Reffel. "Accumulation points of the iterative proportional fitting procedure." Metrika 76, no. 6 (2012): 783–98. http://dx.doi.org/10.1007/s00184-012-0415-7.

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Wong, David W. S. "The Reliability of Using the Iterative Proportional Fitting Procedure∗." Professional Geographer 44, no. 3 (1992): 340–48. http://dx.doi.org/10.1111/j.0033-0124.1992.00340.x.

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Gange, Stephen J. "Generating Multivariate Categorical Variates Using the Iterative Proportional Fitting Algorithm." American Statistician 49, no. 2 (1995): 134. http://dx.doi.org/10.2307/2684626.

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Gange, Stephen J. "Generating Multivariate Categorical Variates Using the Iterative Proportional Fitting Algorithm." American Statistician 49, no. 2 (1995): 134–38. http://dx.doi.org/10.1080/00031305.1995.10476130.

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Dissertations / Theses on the topic "Iterative proportional fitting"

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Založnik, Maja. "Iterative proportional fitting : theoretical synthesis and practical limitations." Thesis, University of Liverpool, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569514.

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Iterative proportional fitting (IPF) is described formally and historically and its advantages and limitations are investigated through two practical simulation exercises using UK census micro data. The theoretical review is unique in being comprehensive and interdisciplinary. It is structured by progressing through three levels of understanding IPF: contingency table analysis in classic applications, analysis using log-linear models and finally the understanding IPF as a method for maximizing entropy. An elaborate methodological section develops the measures and technical tools for the analys
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Strohl, Brandon A. "Empirical Assessment of the Iterative Proportional Fitting Method for Estimating Bus Route Passenger Origin-Destination Flows." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1261583295.

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Chen, Aijing. "Bus Transit Passenger Origin-Destination Flow Estimation: Capturing Terminal Carry-Over Movements Using the Iterative Proportional Fitting Method." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1593675738643412.

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Adelani, Titus Olufemi. "An Evaluation of Traffic Matrix Estimation Techniques for Large-Scale IP Networks." 2010. http://hdl.handle.net/1993/3869.

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The information on the volume of traffic flowing between all possible origin and destination pairs in an IP network during a given period of time is generally referred to as traffic matrix (TM). This information, which is very important for various traffic engineering tasks, is very costly and difficult to obtain on large operational IP network, consequently it is often inferred from readily available link load measurements. In this thesis, we evaluated 5 TM estimation techniques, namely Tomogravity (TG), Entropy Maximization (EM), Quadratic Programming (QP), Linear Programming (LP) and Neu
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Books on the topic "Iterative proportional fitting"

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Saitō, Sanrō. Extensions of iterative proportional fitting procedure and I-projection modeling. Kyūshū Daigaku Shuppankai, 1998.

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Saitō, Saburō. Extensions of iterative proportional fitting procedure and I-projection modeling. Kyushu University Press, 1998.

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Book chapters on the topic "Iterative proportional fitting"

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Meyer, Carl-Heinz, and Holger Knublauch. "Parallel Iterative Proportional Fitting." In Operations Research Proceedings 1999. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58300-1_24.

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Anderson, Ben. "Estimating Small-Area Income Deprivation: An Iterative Proportional Fitting Approach." In Spatial Microsimulation: A Reference Guide for Users. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4623-7_4.

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Onuki, Yuji. "Extension of the Iterative Proportional Fitting Procedure and Its Evaluation Using Agent-Based Models." In Agent-Based Approaches in Economic and Social Complex Systems VII. Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54279-7_15.

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Cho, Sungjin, Tom Bellemans, Lieve Creemers, Luk Knapen, Davy Janssens, and Geert Wets. "Synthetic Population Techniques in Activity-Based Research." In Data Science and Simulation in Transportation Research. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4920-0.ch003.

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Activity-based approach, which aims to estimate an individual induced traffic demand derived from activities, has been applied for traffic demand forecast research. The activity-based approach normally uses two types of input data: daily activity-trip schedule and population data, as well as environment information. In general, it seems hard to use those data because of privacy protection and expense. Therefore, it is indispensable to find an alternative source to population data. A synthetic population technique provides a solution to this problem. Previous research has already developed a few techniques for generating a synthetic population (e.g. IPF [Iterative Proportional Fitting] and CO [Combinatorial Optimization]), and the synthetic population techniques have been applied for the activity-based research in transportation. However, using those techniques is not easy for non-expert researchers not only due to the fact that there are no explicit terminologies and concrete solutions to existing issues, but also every synthetic population technique uses different types of data. In this sense, this chapter provides a potential reader with a guideline for using the synthetic population techniques by introducing terminologies, related research, and giving an account for the working process to create a synthetic population for Flanders in Belgium, problematic issues, and solutions.
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Spataru, Amalia, Silvia Quarteroni, Lilian Arnaud, Paula van Dommelen, Ekaterina Koledova, and Quentin Le Masne. "High Engagement of Patients Monitored by a Digital Health Ecosystem Indicates Significant Improvements of Key r-hGH Treatment Metrics." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210295.

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The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes: adherence, persistence of use, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting (yes/no), and opting-in to receive injection reminders (yes/no), were used as a proxy for engagement. Patients were assigned to the low-engagement group (n=1,752) when all of their features had the low-engagement flag (>2h, no, no) and to the high-engagement group (n=1,081) when all of their features had the high-engagement flag (≤2h, yes, yes). The low-engagement group was down-sampled to 1,081 patients (subsample of n=37 for growth) using the iterative proportional fitting algorithm. Statistical tests were used to study the impact of engagement to the outcomes. The results show that all three outcomes were significantly improved by a factor varying from 1.8 up to 2.2 when the engagement level was high. These results should encourage the promotion of engagement and associated behaviors by both patients and healthcare professionals.
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Conference papers on the topic "Iterative proportional fitting"

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Cooke, Roger, and Margaret MacDonell. "Risk Newsboy: Approach for Addressing Uncertainty in Developing Action Levels and Cleanup Limits." In The 11th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2007. http://dx.doi.org/10.1115/icem2007-7324.

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Site cleanup decisions involve developing action levels and residual limits for key contaminants, to assure health protection during the cleanup period and into the long term. Uncertainty is inherent in the toxicity information used to define these levels, based on incomplete scientific knowledge regarding dose-response relationships across various hazards and exposures at environmentally relevant levels. This problem can be addressed by applying principles used to manage uncertainty in operations research, as illustrated by the newsboy dilemma. Each day a newsboy must balance the risk of buyi
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