2 research outputs found
Identifying Linear Models in Multi-Resolution Population Data using Minimum Description Length Principle to Predict Household Income
One shirt size cannot fit everybody, while we cannot make a unique shirt that
fits perfectly for everyone because of resource limitation. This analogy is
true for the policy making. Policy makers cannot establish a single policy to
solve all problems for all regions because each region has its own unique
issue. In the other extreme, policy makers also cannot create a policy for each
small village due to the resource limitation. Would it be better if we can find
a set of largest regions such that the population of each region within this
set has common issues and we can establish a single policy for them? In this
work, we propose a framework using regression analysis and minimum description
length (MDL) to find a set of largest areas that have common indicators, which
can be used to predict household incomes efficiently. Given a set of household
features, and a multi-resolution partition that represents administrative
divisions, our framework reports a set C* of largest subdivisions that have a
common model for population-income prediction. We formalize a problem of
finding C* and propose the algorithm as a solution. We use both simulation
datasets as well as a real-world dataset of Thailand's population household
information to demonstrate our framework performance and application. The
results show that our framework performance is better than the baseline
methods. We show the results of our method can be used to find indicators of
income prediction for many areas in Thailand. By increasing these indicator
values, we expect people in these areas to gain more incomes. Hence, the policy
makers can plan to establish the policies by using these indicators in our
results as a guideline to solve low-income issues. Our framework can be used to
support policy makers to establish policies regarding any other dependent
variable beyond incomes in order to combat poverty and other issues.Comment: This is the accepted manuscript for publication in TKDD. The R
package is available at https://github.com/DarkEyes/MRRe
A nonparametric framework for inferring orders of categorical data from category-real ordered pairs
Given a dataset of careers and incomes, how large a difference of income
between any pair of careers would be? Given a dataset of travel time records,
how long do we need to spend more when choosing a public transportation mode
instead of to travel? In this paper, we propose a framework that is
able to infer orders of categories as well as magnitudes of difference of real
numbers between each pair of categories using Estimation statistics framework.
Not only reporting whether an order of categories exists, but our framework
also reports the magnitude of difference of each consecutive pairs of
categories in the order. In large dataset, our framework is scalable well
compared with the existing framework. The proposed framework has been applied
to two real-world case studies: 1) ordering careers by incomes based on
information of 350,000 households living in Khon Kaen province, Thailand, and
2) ordering sectors by closing prices based on 1060 companies' closing prices
of NASDAQ stock markets between years 2000 and 2016. The results of careers
ordering show income inequality among different careers. The stock market
results illustrate dynamics of sector domination that can change over time. Our
approach is able to be applied in any research area that has category-real
ordered pairs. Our proposed "Dominant-Distribution Network" provides a novel
approach to gain new insight of analyzing category orders. The software of this
framework is available for researchers or practitioners within R package:
EDOIF.Comment: The R package can be found at https://github.com/DarkEyes/EDOI