Active information, missing data and prevalence estimation

  title={Active information, missing data and prevalence estimation},
  author={Ola H{\"o}ssjer and Daniel Andr'es D'iaz-Pach'on and Chen Zhao and J. Sunil Rao},
The topic of this paper is prevalence estimation from the perspective of active information. Prevalence among tested individuals has an upward bias under the assumption that individuals’ willingness to be tested for the disease increases with the strength of their symptoms. Active information due to testing bias quantifies the degree at which the willingness to be tested correlates with infection status. Interpreting incomplete testing as a missing data problem, the missingness mechanism… 

Figures and Tables from this paper

Assessing, testing and estimating the amount of fine-tuning by means of active information

A general framework is introduced to estimate how much external information has been infused into a search algorithm, the so-called active information. This is rephrased as a test of fine-tuning,



A simple correction for COVID-19 sampling bias

Statistical Analysis With Missing Data

  • N. Lazar
  • Computer Science
  • 2003
Generalized Estimating Equations is a good introductory book for analyzing continuous and discrete correlated data using GEE methods and provides good guidance for analyzing correlated data in biomedical studies and survey studies.

Active information requirements for fixation on the Wright-Fisher model of population genetics

This paper illustrates active information in population genetics through the Wright-Fisher model, a non-neutral model that includes other sources of frequency variation including selection and mutation.

Conservation of Information in Search: Measuring the Cost of Success

  • W. DembskiR. Marks
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
  • 2009
This paper proposes three measures to characterize the information required for successful search and develops a methodology based on these information measures to gauge the effectiveness with which problem-specific information facilitates successful search.

Prior Probabilities

  • E. Jaynes
  • Mathematics
    Encyclopedia of Machine Learning
  • 1968
It is shown that in many problems, including some of the most important in practice, this ambiguity can be removed by applying methods of group theoretical reasoning which have long been used in theoretical physics.

Categorical Data Analysis

Modeling and inferential procedures (estimation and precision assessment) are discussed for a single proportion, thereafter in the context of cross-classified data, i.e., contingency tables, is discussed, with particular emphasis on testing the null hypothesis of no association (independence) between the row and column classification.

The Gopher's Gambit: Survival Advantages of Artifact-based Intention Perception

It is found that gophers possessing the ability to perceive intention have significantly better survival outcomes than those without intention perception in most of the cases evaluated.

Mode hunting through active information

An algorithm called active information mode hunting (AIMH) is developed that, when applied to the whole space, will say whether there are any modes present and where they are and it is shown AIMH is consistent and helps to overcome issues with the curse of dimensionality.

Bernoulli's principle of insufficient reason and conservation of information in computer search

  • W. DembskiR. Marks
  • Computer Science
    2009 IEEE International Conference on Systems, Man and Cybernetics
  • 2009
This discussion leads to resolution of the seeming conflict between COI and the observation that some search algorithms perform well on a large class of problems.

Generalized active information: extensions to unbounded domains

D Disequilibrium from maximum entropy, measured as active information, can be evaluated from baselines with unbounded support, and this is the purpose of this paper.