6533b7d8fe1ef96bd126b839
RESEARCH PRODUCT
A True Extension of the Markov Inequality to Negative Random Variables
Louis De Mesnardsubject
Chebyshev's inequalityLaw of large numbersComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONMarkov's inequalityMathematicsofComputing_NUMERICALANALYSISApplied mathematicsExtension (predicate logic)Random variableUpper and lower boundsMathematicsVariable (mathematics)description
The Markov inequality is a classical nice result in statistics that serves to demonstrate other important results as the Chebyshev inequality and the weak law of large numbers, and that has useful applications in the real world, when the random variable is unspecified, to know an upper bound for the probability that an variable differs from its expectation. However, the Markov inequality has one main flaw: its validity is limited to nonnegative random variables. In the very short note, we propose an extension of the Markov inequality to any non specified random variable. This result is completely new.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 | SSRN Electronic Journal |