Search results for "Quasi-likelihood"
showing 4 items of 4 documents
Handling Underdispersion in Calibrating Safety Performance Function at Urban, Four-Leg, Signalized Intersections
2011
Poisson basic assumption of equidispersion is often too much restrictive for crash count data; in fact this type of data has been found to often exhibit overdispersion. Underdispersion has been less commonly observed, and this is the reason why it has been less convenient to model directly than overdispersion. Overdispersion and underdispersion are not the only issues that can be a potential source of error in specifying statistical models and that can lead to biased crash-frequency predictions; these issues can derive from data properties (temporal and spatial correlation, time-varying explanatory variables, etc.) or from methodological approach (omitted variables, functional form selectio…
A Quasi-likelihood Markov model for the hardenability of steel
1992
To evaluate the hardenability of steel the Jominy test can be used. A steel bar of fixed dimensions is heated up to about 850 °C and then quenched from one end by water spray. Its hardness y is then measured at increasing distances d from this quenched end giving observations (d, y(d)), which form the Jominy curve. For each Jominy curve these observations can be fitted by descending curve μ = μ(d; η) with shape parameters η = (η 1,..., η p ) T which are assumed to vary along the alloying elements (C, Mn, Cr etc.).
On Rao Score and Pearson X2 Statistics in Generalized Linear Models
2005
The identity of the Rao score and PearsonX 2 statistics is well known in the areas where the latter was first introduced: goodness-of-fit in contingency tables and binary responses. We show in this paper that the same identity holds when the two statistics are used for testing goodness-of-fit of Generalized Linear Models. We also highlight the connections that exist between the two statistics when they are used for the comparison of nested models. Finally, we discuss some merits of these unifying results.
Overdispersion tests in count-data analysis.
2008
Count data are commonly assumed to have a Poisson distribution, especially when there is no diagnostic procedure for checking this assumption. However, count data rarely fit the restrictive assumptions of the Poisson distribution. The violation of much of such assumptions commonly results in overdispersion, which invalidates the Poisson distribution. Undetected overdispersion may entail important misleading inferences, so its detection is essential. In this study, different overdispersion diagnostic tests are evaluated through two simulation studies. In Exp. 1, the nominal error rate is compared under different sample sizes and Λ conditions. Analysis shows a remarkable performance of the χ…