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Jfunctions

Numerical Routine J and Some Derivatives


Description

J00 represents the function J(x, y, v), where for real numbers x, y and v \in [0, 1],

J(x, y, v) = int_0^v exp((1 - t) x + t y) d t = (exp(x + v(y - x)) - exp(x))/(y - x).

The functions Jab give the respective derivatives J_{ab} for v = 1, i.e.

J_{ab}(x, y) = (partial ^ {a + b}) / (\partial x ^ a \partial y ^ b) J(x, y).

Specifically,

J_{10}(x, y) = (exp(y) - exp(x) - (y - x) exp(x))/((y - x) ^ 2);

J_{11}(x, y) = ((y - x)(exp(x) + exp(y)) + 2 (exp(y) - exp(x)))/((y - x) ^ 3);

J_{20}(x, y) = 2(exp(y) - exp(x) - (y - x) exp(x) - (y - x) ^ 2 exp(x)) / ((y - x) ^ 3).

Usage

J00(x, y, v)
J10(x, y)
J11(x, y)
J20(x, y)

Arguments

x

Vector of length d with real entries.

y

Vector of length d with real entries.

v

Number in [0, 1]^d.

Value

Value of the respective function.

Note

Taylor approximations are used if y-x is small. We refer to Duembgen et al (2011, Section 6) for details.

These functions are not intended to be invoked by the end user.

Author(s)

References

Duembgen, L, Huesler, A. and Rufibach, K. (2010) Active set and EM algorithms for log-concave densities based on complete and censored data. Technical report 61, IMSV, Univ. of Bern, available at http://arxiv.org/abs/0707.4643.

Duembgen, L. and Rufibach, K. (2011) logcondens: Computations Related to Univariate Log-Concave Density Estimation. Journal of Statistical Software, 39(6), 1–28. http://www.jstatsoft.org/v39/i06


logcondens

Estimate a Log-Concave Probability Density from Iid Observations

v2.1.5
GPL (>= 2)
Authors
Kaspar Rufibach <kaspar.rufibach@gmail.com> and Lutz Duembgen <duembgen@stat.unibe.ch>
Initial release
2016-07-11

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