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Two_new_algorithms_for_ecological_inference_based_on_linear_programming.pdf (635.41 kB)
Download fileImproving estimates accuracy of voter transitions. Two new algorithms for ecological inference based on linear programming
The
estimation of RxC ecological inference contingency tables from aggregate data
defines one of the most salient and challenging problems in the field of quantitative
social sciences. From
the mathematical programming framework, this paper suggests a new direction for
tackling this problem. For the first time in the literature, a procedure based
on linear programming is proposed to attain estimates of local contingency
tables. Based on this and the homogeneity hypothesis, we suggest two new
ecological inference algorithms. These two new algorithms represent an
important step forward in the ecological inference mathematical programming
literature. In addition to generating estimates for local ecological inference
contingency tables and amending the tendency to produce extreme transfer
probability estimates previously observed in other mathematical programming
procedures, they prove to be quite competitive and more accurate than the current
linear programming baseline algorithm. The new algorithms place the linear
programming approach once again in a prominent position in the ecological
inference toolkit. We use a unique dataset with almost 500 elections, where the
real transfer matrices are known, to assess their accuracy. Interested readers
can easily use these new algorithms with the aid of the R package lphom.
Funding
ECO2017-87245-R
AICO/2019/053
History
Declaration of conflicts of interest
Any conflict of interest to declareCorresponding author email
pavia@uv.esLead author country
SpainLead author job role
Higher Education ResearcherLead author institution
Universitat de ValenciaHuman Participants
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