Dear all,
This is a reminder
Below details about the next RSS West Midlands Local Group Event.
Too many zeros? Not enough zeros? How to assess through inferential and graphical methods.
Where University of Wolverhampton, (City Campus), Wulfruna Street, Wolverhampton, WV1 1SE Room MA30
When 6.00pm. Light refreshment will be abailable from 5.30pm 9th May 2023
The event will consist of two 25 minute talks presented by Dr. Paul Wilson of the University of Wolverhampton and Prof. Jochen Einbeck of Durham
An intuitive test for Zero Inflation and Deflation:
While there do exist several statistical tests for detecting zero inflation and zero-deflation in count data regression models, these rely on asymptotical results and do not transparently distinguish between zero inflation and zero deflation. In this work, a novel non-asymptotic test is introduced which makes direct use of the fact that the distribution of the number of zeros under the null hypothesis of no zero modification can be described by a Poisson-binomial distribution. The computation of critical values from this distribution requires estimation of the mean parameter under the null hypothesis, for which a hybrid estimator involving a zero-truncated mean estimator is proposed. Power and nominal level attainment rates of the new test are studied, which turn out to be very competitive to those of the likelihood ratio test. Illustrative data examples are provided.
A graphical tool for assessing the suitability of a count regression model
Count data are usually fitted through Poisson models or simple two-parameter models such as the Negative Binomial distribution. Whilst a range of numeric methods and statistical tests do exist for assessing or comparing model fit in this context, diagrammatic methods are few. We present here a diagnostic plot, which we refer to as a `Quantile Band plot', that may be used to visually assess the suitability of a given count regression model. In the case of diagnosed model inadequacy, the plot has the unique feature of conveying precise information on the character of the violation, hence pointing the data analyst towards a potentially better model choice.
Best Regards,
Kristian Romano
Personal Website<https://warwick.ac.uk/fac/sci/statistics/staff/research_students/romano>
Pronouns: He/him
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