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Table 1. Standard binomial model.
"... In PAGE 5: ... The null hypothesis is defined as p H = q : . For all possible values of d, Table1 pres- ents the figures to compare our measure with the standard ones. To compute the Bayes Factor, we con-... ..."
Table 3. Negative Binomial Model for PDO Crash Frequency
"... In PAGE 8: ... This can potentially give rise to model specification errors. Modeling Results Non-injury Crashes Before and During Work Zone Period Model 1 ( Table3 ) provides information on the factors that influence the frequency of police-reported crashes in the pre-work zone period. The Rho-squared value, which provides a measure of the model fit, indicates a reasonable fit.... ..."
Table 6. Negative Binomial Model for KABC Crash Frequency
"... In PAGE 9: ... The exposure term is statistically non-significant. Model 4 ( Table6 ) shows the effects of the independent variables on injury-producing crashes in work zones. The goodness of fit statistic for the model is low implying that the explanatory variables are explaining relatively less variation in the data.... ..."
Table 5. Negative Binomial Model for KABC Crash Frequency in
"... In PAGE 9: ... 0.13. This indicates that the effect of work zone length is largely unchanged relative to the before work zone period, implying that reducing work zone length is not a critical consideration in the reduction of adverse work zone impacts. Injury Crashes in Before and During Work Zone Period Model 3 ( Table5 ) presents the effects of the independent variables on injury-producing crashes in the before work zone period. Summary statistics for the model are reasonable and the mean ADT is statistically significant (5% level) indicating that increased mean ADT results in higher injury-producing crash frequency.... ..."
Table 3: Estimates for global correlation in the Beta-Binomial model
2006
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Table 2. Binomial model results for different number of components
Table 2: Checkability of the binomial regression model
1998
"... In PAGE 8: ... The second stage assumes i i N ; 2 0 0 2 ; i = 1; :::; 20 with the illustrative informative priors, 2 IG(c; d) such that E( 2 ) = 10; V ar( 2 ) = 3; 2 IG(e; f) such that E( 2 ) = 1; V ar( 2 ) = 1 and N ??0 2 ; ?100 0 1 : Sampling based tting of this model is accomplished using Metropolis steps within a Gibbs sampler. Table2 summarizes the checkability of this model in terms of the I(d) and the interstage corre- lations using 1000 replications each providing 1000 pos- terior samples. We see that associations are weak, that d2j1 should be very e ective, d1 less so with the d2 apos;s o ering little promise.... ..."
Cited by 2
Table 2: Class implementing the hierarchical Binomial Beta-Binomial model for the LOH data.
2001
Cited by 2
Table 2: Class implementing the hierarchical Binomial Beta-Binomial model for the LOH data.
Table 1 -- MLE Results for the Single Period Binomial Logit Model
1998
"... In PAGE 3: ... Maximum likelihood estimation ( MLE ) of the binomial logit model yielded parameter estimates and statistics for separation likelihood. (See Table1 .) Interpreting the signs and significance levels of parameter estimates is similar to linear regression.... ..."
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