Statistics seminar
Date and time: Tuesday, 29th November at 3:00 pm
Venue: Ramanujan Hall.
Speaker: Subrata Kundu, George Washington University (USA),
Title: Some remarks on generalizations of the likelihood function and the likelihood principle
Abstract: The sufficiency principle (SP), the weak conditionality principle (WCP), the likelihood function (LF), and the likelihood principle (LP) for a general statistical inference problem are discussed. It is argued that a general statistical problem can be regarded as a prediction problem by treating the quantity (z) of inferential interest as the realized but unobserved value of a random vector Z. The LF is defined as the density of the data given z and the unknown fixed parameters of the model, considered as a function of z and θ. The SP and WCP are modified such that they are equivalent to the LP based on the proposed LF.
(Joint work with Tapan K. Nayak)