Chebyshev’s inequality is one of the most common inequalities used in probability theory to bound the tail probabilities of a random variable X having finite variance σ2 = { Var}X. It states that
where μ = eX denotes the mean of X. Of course, the given bound is of use only if t is bigger than the standard deviation σ. Instead of proving (1) we will give a proof of the more general Markov’s inequality which states that for any nondecreasing function g : [0, ∞) → [0, ∞) and any nonnegative random variable Y
Indeed, choosing Y = | X − μ | and g(x) = x 2 gives (1). The proof of Markov’s inequality is very easy: For any t > 0,
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References and Further Reading
Chebyshev P (1867) Des valeurs moyennes. Liouville’s J Math Pure Appl 12:177–184
Feller W (1971) An introduction to probability theory and its applications, vol II, 2nd edn. Wiley, New York
Monhor D (2007) A Chebyshev inequality for multivariate normal distribution. Prob Eng Inform Sci 21(2):289–300
Olkin I, Pratt JW (1958) A multivariate Tchebycheff inequality. Ann Math Stat 29:226–234
Sellke TM, Sellke SH (1997) Chebyshev inequalities for unimodal distributions. Am Stat 51(1):34–40
Vysočanskiĭ DF, Petunĭn JĪ (1979) Proof of the 3σ rule for unimodal distributions. Teor Veroyatnost i Mat Stat 21:23–35
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Alsmeyer, G. (2011). Chebyshev’s Inequality. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_167
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DOI: https://doi.org/10.1007/978-3-642-04898-2_167
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