Improved Likelihood Ratio Tests for Varying Dispersion Simplex Regression Models

被引:0
|
作者
Santos, Joas S. [1 ]
Cribari-Neto, Francisco [1 ]
机构
[1] Univ Fed Pernambuco, Ctr Ciencias Exatas & Nat, Dept Estat, BR-50670901 Recife, PE, Brazil
关键词
Bartlett correction; likelihood ratio test; Monte Carlo simulation; simplex regression; BETA-REGRESSION; MARGINAL MODELS; SCORE TESTS; INFERENCE;
D O I
10.1002/adts.202300315
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The simplex regression model is tailored to response variables that assume values in the standard unit interval, such as rates and proportions. The estimation of the parameters that index the model is performed by maximum likelihood, and test inferences are commonly reached using the likelihood ratio test. Such a test is based on an asymptotic approximation, and thus the resulting inferences may be misleading when the sample size is not large. In this paper, Bartlett-corrected likelihood ratio tests are obtained for varying dispersion simplex regressions. Monte Carlo simulations are performed to compare the finite sample behavior of the corrected tests to that of standard likelihood ratio. The numerical results show that one of the corrected tests displays excellent control of the type I error frequency. An empirical application is presented and discussed.
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页数:11
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