Abstract
We develop hypothesis testing for active information — the averaged quantity in the Kullback–Leibler divergence. To our knowledge, this is the first paper to derive exact probabilities of type-I errors for hypothesis testing in the area.
- Title
- Hypothesis testing with active information
- Creators
- Daniel Andrés Díaz–Pachón - University of Miami, UMMG Department of Public Health Sciences ResearchJuan Pablo Sáenz - Department of Industrial Engineering - University of Miami, McArthur Engineering Building, 1251 Memorial Drive, Coral Gables FL, 33146, United States of AmericaJ. Sunil Rao - Division of Biostatistics - University of Miami, Don Soffer Clinical Research Center, 1120 NW 14th St, Miami FL, 33136, United States of America
- Publication Details
- Statistics & probability letters, Vol.161, p.108742
- Publisher
- Elsevier B.V
- Academic Unit
- Miller School of Medicine; UMMG Department of Public Health Sciences Research
- Language
- English
- Resource Type
- Journal article
- Record Identifier
- 991031577115702976