Estimating cell probabilities in contingency tables with constraints on marginals/conditionals by geometric programming with applications

DOI10.1007/s00180-014-0525-yzbMath1342.65067OpenAlexW2094346359MaRDI QIDQ736994

Johan Lim, Xinlei Wang, Kyu S. Hahn, Seung-Jean Kim

Publication date: 5 August 2016

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-014-0525-y

zbMATH Keywords

monomialstochastic orderingmultinomial distributionMatlabposynomialjudgement post-stratificationknown marginalsordered conditionalsordered marginals

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Point estimation (62F10) Contingency tables (62H17)

Uses Software

• Matlab
• SDaA

Cites Work

• Constrained estimation using judgment post-stratification
• Maximum likelihood estimation of ordered multinomial probabilities by geometric programming
• A tutorial on geometric programming
• Maximum likelihood estimates with order restrictions on probabilities and odds ratios: A geometric programming approach
• Ranked set sampling. Theory and applications
• ROC convex hull and nonparametric maximum likelihood estimation
• Isotonized CDF Estimation from Judgment Poststratification Data with Empty Strata
• Concomitants of Multivariate Order Statistics With Application to Judgment Poststratification
• Judgement Post‐Stratification with Imprecise Rankings
• Maximum likelihood estimates for multinomial probabilities via geometric programming
• Maximum Likelihood Estimation for the Multinomial Distribution Using Geometric Progamming
• Nonparametric Tests for Perfect Judgment Rankings
• Digital Circuit Optimization via Geometric Programming
• Contingency tables with given marginals
• Maximum likelihood estimation of ordered multinomial parameters
• On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known
• An Iterative Method of Adjusting Sample Frequency Tables When Expected Marginal Totals are Known
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