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effect models
Both fixed and random effect models were conducted for this analysis.
      
Both cross-sectional and fixed-effect models, however, are obtained by imposing strong a priori restrictions on the model parameters.
      
Complier Average Causal Effect models are an appropriate way to adjust for contamination if it can be measured.
      
Discrete choice, fixed-effect and random-effect models are adopted to establish a relationship between CVC investment and patenting.
      
Direct effect models assume that social support and stressors act independent of one another on strains.
      
Estimation procedure is carried out by using Fixed Effect and Random Effect Models in panel data analysis.
      
Fixed effect models were used to obtain summary statistics for overall efficacy.
      
FEV1 decline according to genotype was analyzed using linear mixed effect models with adjustment for confounders.
      
For univariate tests, mixed-effect models were used to account for the hierarchical and longitudinal sampling design.
      
For GSTT1, all the studies were homogenous and both the fixed-effect and random-effect models generated the same result.
      
Gather experimental evidence on cause-effect interactions to build cause and effect models.
      
Heteroscedasticity can also be corrected with random effect models which assume that differences between units are randomly distributed.
      
In linear fixed-effect models noisy estimates of the fixed effects do not bias estimates of the slope coefficients.
      
In general, studies based on fixed-effect models, produce much higher convergence rates than those obtained using cross-country regressions.
      
In addition there are drug effect models which simulate the ef fects of about 70 intravenous medications on the car diovascular or pulmonary system.
      
In particular, the resulted efficiency rankings obtained from random effect and fixed effect models are analyzed.
      
In the absence of adequate instrument and fixed-effect models, estimating reduced-form equations has proved to be a viable solution.
      
In addition, we use fixed-effect models to account for explanatory factors at the State level.
      
Mixed-effect models for predicting microbial interactions in the vaginal ecosystem.
      
Mixed effect models could be used to estimate yearly variogram parameters in a multi-annual modelling.
      
 

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