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Activity Number: 194 - Contributed Poster Presentations: SSC
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract #330431
Title: Applications of Directional Inference
Author(s): Andrew McCormack* and Nancy Reid and Sri-Amirthan Theivendran and Nicola Sartori
Companies: and University of Toronto and University of Toronto and University of Padua
Keywords: Hypothesis Testing; p-value; Exponential Family; Saddlepoint Approximation; Tangent Exponential Model

Inference for vector parameters is often based on the likelihood ratio statistic, with p-values computed using the Chi-squared approximation. Davison et al (2014) and Fraser et al (2016) proposed a directional test and showed how p-values for this test could be accurately computed. The calculations are particularly simple for linear hypotheses in exponential families. In some common situations the directional approach yields p-values that are equivalent to those obtained from well known test statistics. For example, the directional test of a linear constraint on the regression vector in normal theory linear regression is equivalent to the usual F-test, thus giving a new view on this classical test. We examine the performance of directional inference in several examples. Simulations are presented showing that directional p-values are uniformly distributed under the null hypothesis, and can be more accurate than usual first order approximations.

Authors who are presenting talks have a * after their name.

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