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Maximum likelihood estimation procedures for parameters of generalized gravity model.

机译：广义重力模型参数的最大似然估计程序。

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This research is aimed at developing algorithms for obtaining Maximum Likelihood (ML) estimates of Generalized Gravity Model parameters, which are computationally more efficient than the ones currently available. The model considered is a very general version of the gravity model, which was first presented by Sen and Soot (1981) and which has been given a sound theoretical grounding by Smith (1987).;The proposed algorithms dramatically improve the computational performance of obtaining ML estimates of gravity model. These improvements should assure the relevance of these estimation procedures in the application of the gravity models in transportation planning efforts.;The key modification that dramatically improves performance is a method known as the Linearized DSF (LDSF) Procedure. Specifically five alternative ML estimation procedures are proposed for the general case of more than one measure of separation. The alternative procedures presented are: A Modified Scoring Procedure, three Modified Gradient Search Procedures which I shall call Procedures Ia, Ib and Procedure II, and application of the Generalized Linear Models (GLIM).

机译：这项研究旨在开发用于获得广义重力模型参数的最大似然（ML）估计的算法，该算法在计算上比当前可用的算法效率更高。所考虑的模型是重力模型的非常通用的版本，该模型最初由Sen和Soot（1981）提出，并已由Smith（1987）给出了合理的理论基础。所提出的算法极大地提高了重力模型的计算性能。 ML重力模型的估计。这些改进应确保将这些估计程序与重力模型在运输计划工作中的应用相关。显着提高性能的关键修改是称为线性化DSF（LDSF）程序的方法。针对不止一种分离度量的一般情况，特别提出了五种替代的ML估计程序。提出的替代程序包括：改进的评分程序，三个改进的梯度搜索程序（我将它们称为过程Ia，Ib和过程II），以及广义线性模型（GLIM）的应用。

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作者
Yun, Seongsoon.;
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作者单位

University of Illinois at Chicago.;

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授予单位 University of Illinois at Chicago.;
学科 Transportation.;Urban and Regional Planning.;Operations Research.
学位 Ph.D.
年度 1992
页码 108 p.
总页数 108
原文格式 PDF
正文语种 eng
中图分类遥感技术;
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Maximum likelihood estimation procedures for parameters of generalized gravity model.

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