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In probability theory, Chernoff's distribution, named after Herman Chernoff, is the probability distribution of the random variable

${\displaystyle Z={\underset {s\in \mathbf {R} }{\operatorname {argmax} }}\ (W(s)-s^{2}),}$

where W is a "two-sided" Wiener process (or two-sided "Brownian motion") satisfying W(0) = 0. If

${\displaystyle V(a,c)={\underset {s\in \mathbf {R} }{\operatorname {argmax} }}\ (W(s)-c(s-a)^{2}),}$

then V(0, c) has density

${\displaystyle f_{c}(t)={\frac {1}{2}}g_{c}(t)g_{c}(-t)}$

where g_c has Fourier transform given by

${\displaystyle {\hat {g}}_{c}(s)={\frac {(2/c)^{1/3}}{\operatorname {Ai} (i(2c^{2})^{-1/3}s)}},\ \ \ s\in \mathbf {R} }$

and where Ai is the Airy function. Thus f_c is symmetric about 0 and the density ƒ_Z = ƒ₁. Groeneboom (1989) shows that

${\displaystyle f_{Z}(z)\sim {\frac {1}{2}}{\frac {4^{4/3}|z|}{\operatorname {Ai} '({\tilde {a}}_{1})}}\exp \left(-{\frac {2}{3}}|z|^{3}+2^{1/3}{\tilde {a}}_{1}|z|\right){\text{ as }}z\rightarrow \infty }$

where ${\displaystyle {\tilde {a}}_{1}\approx -2.3381}$ is the largest zero of the Airy function Ai and where ${\displaystyle \operatorname {Ai} '({\tilde {a}}_{1})\approx 0.7022}$. In the same paper, Groeneboom also gives an analysis of the process ${\displaystyle \{V(a,1):a\in \mathbf {R} \}}$. The connection with the statistical problem of estimating a monotone density is discussed in Groeneboom (1985). Chernoff's distribution is now known to appear in a wide range of monotone problems including isotonic regression.

The Chernoff distribution should not be confused with the Chernoff geometric distribution (called the Chernoff point in information geometry) induced by the Chernoff information.

History

Groeneboom, Lalley and Temme state that the first investigation of this distribution was probably by Chernoff in 1964, who studied the behavior of a certain estimator of a mode. In his paper, Chernoff characterized the distribution through an analytic representation through the heat equation with suitable boundary conditions. Initial attempts at approximating Chernoff's distribution via solving the heat equation, however, did not achieve satisfactory precision due to the nature of the boundary conditions. The computation of the distribution is addressed, for example, in Groeneboom and Wellner (2001).

The connection of Chernoff's distribution with Airy functions was also found independently by Daniels and Skyrme and Temme, as cited in Groeneboom, Lalley and Temme. These two papers, along with Groeneboom (1989), were all written in 1984.

References

1. Groeneboom, Piet (1989). "Brownian motion with a parabolic drift and Airy functions". Probability Theory and Related Fields. 81: 79–109. doi:10.1007/BF00343738. MR 0981568. S2CID 119980629.
2. Groeneboom, Piet (1985). Le Cam, L.E.; Olshen, R. A. (eds.). Estimating a monotone density. Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer, vol. II. pp. 539–555.
3. Groeneboom, Piet; Jongbloed, Geurt (2018). "Some Developments in the Theory of Shape Constrained Inference". Statistical Science. 33 (4): 473–492. doi:10.1214/18-STS657. S2CID 13672538.
4. Nielsen, Frank (2022). "Revisiting Chernoff Information with Likelihood Ratio Exponential Families". Entropy. 24 (10). MDPI: 1400. doi:10.3390/e24101400. PMC 9601539. PMID 37420420.
5. ^ Groeneboom, Piet; Lalley, Steven; Temme, Nico (2015). "Chernoff's distribution and differential equations of parabolic and Airy type". Journal of Mathematical Analysis and Applications. 423 (2): 1804–1824. arXiv:1305.6053. doi:10.1016/j.jmaa.2014.10.051. MR 3278229. S2CID 119173815.
6. Chernoff, Herman (1964). "Estimation of the mode". Annals of the Institute of Statistical Mathematics. 16: 31–41. doi:10.1007/BF02868560. MR 0172382. S2CID 121030566.
7. Groeneboom, Piet; Wellner, Jon A. (2001). "Computing Chernoff's Distribution". Journal of Computational and Graphical Statistics. 10 (2): 388–400. CiteSeerX 10.1.1.369.863. doi:10.1198/10618600152627997. MR 1939706. S2CID 6573960.
8. Daniels, H.E.; Skyrme, T.H.R. (1985). "The maximum of a random walk whose mean path has a maximum". Advances in Applied Probability. 17 (1): 85–99. doi:10.2307/1427054. JSTOR 1427054. MR 0778595. S2CID 124603511.
9. Temme, N.M. (1985). "A convolution integral equation solved by Laplace transformations" (PDF). Journal of Computational and Applied Mathematics. 12–13: 609–613. doi:10.1016/0377-0427(85)90052-4. MR 0793989. S2CID 120496241.

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