On Generalized Score Tests

1992 ◽  
Vol 46 (4) ◽  
pp. 327 ◽  
Author(s):  
Dennis D. Boos
Keyword(s):  
Score Tests ◽  
Generalized Score Tests

On Generalized Score Tests

1992 ◽  
Vol 46 (4) ◽  
pp. 327-333 ◽  
Author(s):  
Dennis D. Boos
Keyword(s):  
Score Tests ◽  
Generalized Score Tests

Corrected likelihood ratio and score tests for the beta distribution

2003 ◽  
Vol 73 (8) ◽  
pp. 585-596 ◽  
Author(s):  
André Luis Santiago Maia ◽  
Antô Nio Carlos Braga Junior ◽  
Gauss Cordeiro
Keyword(s):  
Likelihood Ratio ◽  
Beta Distribution ◽  
Score Tests

scholarly journals Small-Sample Corrections for Score Tests in Birnbaum–Saunders Regressions

2010 ◽  
Vol 40 (2) ◽  
pp. 232-243 ◽  
Author(s):  
Artur J. Lemonte ◽  
Silvia L. P. Ferrari
Keyword(s):  
Small Sample ◽  
Score Tests

Second order asymptotics for score tests in generalised linear models

Biometrika ◽  
1995 ◽  
Vol 82 (2) ◽  
pp. 426-432 ◽  
Author(s):  
FRANCISCO CRIBARI-NETO ◽  
SILVIA L. P. FERRARI
Keyword(s):  
Linear Models ◽  
Second Order ◽  
Generalised Linear Models ◽  
Score Tests ◽  
Second Order Asymptotics

scholarly journals Distributions You Can Count On …But What’s the Point?

Econometrics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 9 ◽  
Author(s):  
Brendan P. M. McCabe ◽  
Christopher L. Skeels
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Broad Class ◽  
Poisson Model ◽  
Econometric Analysis ◽  
Discrete Distributions ◽  
Optimal Test ◽  
Score Tests ◽  
Optimal Tests ◽  
Over Dispersion

The Poisson regression model remains an important tool in the econometric analysis of count data. In a pioneering contribution to the econometric analysis of such models, Lung-Fei Lee presented a specification test for a Poisson model against a broad class of discrete distributions sometimes called the Katz family. Two members of this alternative class are the binomial and negative binomial distributions, which are commonly used with count data to allow for under- and over-dispersion, respectively. In this paper we explore the structure of other distributions within the class and their suitability as alternatives to the Poisson model. Potential difficulties with the Katz likelihood leads us to investigate a class of point optimal tests of the Poisson assumption against the alternative of over-dispersion in both the regression and intercept only cases. In a simulation study, we compare score tests of ‘Poisson-ness’ with various point optimal tests, based on the Katz family, and conclude that it is possible to choose a point optimal test which is better in the intercept only case, although the nuisance parameters arising in the regression case are problematic. One possible cause is poor choice of the point at which to optimize. Consequently, we explore the use of Hellinger distance to aid this choice. Ultimately we conclude that score tests remain the most practical approach to testing for over-dispersion in this context.

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