Using geometry to select one dimensional exponential families that are monotone likelihood ratio in the sample space, are weakly unimodal and can be parametrized by a measure of central tendency
Author
Vos, Paul; Anaya-Izquierdo, Karim
Abstract
One dimensional exponential families on finite sample spaces are studied using the geometry of the simplex Δn°-1 and that of a transformation Vn-1 of its interior. This transformation is the natural parameter space associated with the family of multinomial distributions. The space Vn-1 is partitioned into cones that are used to find one dimensional families with desirable properties for modeling and inference. These properties include the availability of uniformly most powerful tests and estimators that exhibit optimal properties in terms of variability and unbiasedness.
Date
2014-07-18
Citation:
APA:
Vos, Paul, & Anaya-Izquierdo, Karim. (July 2014).
Using geometry to select one dimensional exponential families that are monotone likelihood ratio in the sample space, are weakly unimodal and can be parametrized by a measure of central tendency.
,
(),
-
. Retrieved from
http://hdl.handle.net/10342/8311
MLA:
Vos, Paul, and Anaya-Izquierdo, Karim.
"Using geometry to select one dimensional exponential families that are monotone likelihood ratio in the sample space, are weakly unimodal and can be parametrized by a measure of central tendency". .
. (),
July 2014.
September 27, 2023.
http://hdl.handle.net/10342/8311.
Chicago:
Vos, Paul and Anaya-Izquierdo, Karim,
"Using geometry to select one dimensional exponential families that are monotone likelihood ratio in the sample space, are weakly unimodal and can be parametrized by a measure of central tendency," , no.
(July 2014),
http://hdl.handle.net/10342/8311 (accessed
September 27, 2023).
AMA:
Vos, Paul, Anaya-Izquierdo, Karim.
Using geometry to select one dimensional exponential families that are monotone likelihood ratio in the sample space, are weakly unimodal and can be parametrized by a measure of central tendency. .
July 2014;
():
.
http://hdl.handle.net/10342/8311. Accessed
September 27, 2023.
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