A nonparametric framework for inferring orders of categorical data from category-real pairs

  title={A nonparametric framework for inferring orders of categorical data from category-real pairs},
  author={Chainarong Amornbunchornvej and Navaporn Surasvadi and Anon Plangprasopchok and Suttipong Thajchayapong},
Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysis
The proposed framework provides not only how severe the issue of poverty is, but it also provides the causal relations among poverty factors, and knowing a confidence interval of degree of causal direction lets us know how strong a causal relation is.
Occupational Income Inequality of Thailand: A Case Study of Exploratory Data Analysis beyond Gini Coefficient
The results show that, in agricultural provinces, there are less issues in both types of inequality, while some non-agricultural provinces face an issue of occupational income inequality without any symptom of general income inequality (low Gini coefficients).
Population Structure of Nation-Wide Rice in Thailand
The population structure, which was inferred in this study, contains five populations s.t. each population has a unique ecological system, genetic pattern, as well as agronomic traits.


Identifying Linear Models in Multi-Resolution Population Data Using Minimum Description Length Principle to Predict Household Income
This work proposes a framework using regression analysis and Minimum Description Length to find a set of largest areas that have common indicators, which can be used to predict household incomes efficiently and demonstrates that the results of this framework performance is better than the baseline methods.
Moving beyond P values: data analysis with estimation graphics
The estimation graphic is described, a plot that displays an experimental dataset’s complete statistical information that avoids dichotomous thinking and introduces software that makes high-quality estimation graphics available to all.
Coordination Event Detection and Initiator Identification in Time Series Data
This work formalizes the Coordination Initiator Inference Problem and proposes a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods.
Better Bootstrap Confidence Intervals
Abstract We consider the problem of setting approximate confidence intervals for a single parameter θ in a multiparameter family. The standard approximate intervals based on maximum likelihood
Current Market Top Business Scopes Trend—A Concurrent Text and Time Series Active Learning Study of NASDAQ and NYSE Stocks from 2012 to 2017
A concurrent text and time series methodology is conducted to analyze the stocks in the New York Stock Exchange and the National Association of Securities Dealers Automated Quotations from 2012 to 2017 and finds evidence that artificial intelligence and blockchains gained increasing importance for companies during that period.
Income Inequality and Happiness
The negative link between income inequality and the happiness of lower-income respondents was explained not by lower household income, but by perceived unfairness and lack of trust.
Estimation statistics should replace significance testing
P is untrustworthy unless the statistical power is very high (above 90%), which offsets advantages of P such as its simplicity, and as researchers better appreciate the typically artificial nature of the null hypothesis and the limited capacity of P to support hypothesis testing, it is believed that P will become much less highly valued.
Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis
Preface. About this Book 1. Introduction to The New Statistics 2. From Null Hypothesis Significance Testing to Effect Sizes 3. Confidence Intervals 4. Confidence Intervals, Error Bars, and p Values
Partially Ordered Sets
Introduction to Probability
The articles [8], [9], [4], [7], [6], [2], [5], [1], and [3] provide the notation and terminology for this paper. For simplicity, we adopt the following convention: E denotes a non empty set, a