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In last passage percolation (LPP) models, a random environment in the two-dimensional integer lattice consisting of independent and identically distributed weights is considered.

Results: The heterogeneity of trial designs and the use of different comparators necessitated a Bayesian hierarchical meta-analysis with three models: (a) random trial effect and fixed treatment effects, (b) random trial and treatment effects, (c) random trial and treatment effects with trial variance unknown.

For these three models, a random effect was added depending on the genotype.

Because there we only 2 to 3 time-points in the models, a random slope was not estimated.

> -wrap-foot> In all models, a random factor of nest of origin explained significantly the variance of digit ratios (likelihood ratio test: in all cases χ > 11.7, p < 0.0003) We found no association between digit ratios and reproductive traits/quality (laying date or clutch size in females, arrival date in males) in either females or males (Table  4).

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Besides the employed SURE, OLS and logit regression models, a random-effects or fixed-effects panel data model would also be appropriate.

To assess the reliability of our results, we conducted sensitivity analyses (Table 2), comparing the pooled estimates produced using two types of models (a random-effects model versus a fixed-effects model).

Integrated with wavelet transform, the proposed approach may be used to model a random process with non-stationary frequency content.

As a result, we adopt the channel erasure probabilities uniformly at random to model a random distribution of receivers.

In multilevel modeling, a random intercept or random slope can account for additional outcome variation attributable to individuals and clusters.

Using the deterministic model, a random model of antibiotic resistance will be obtained by adding random effect terms to the parameters of the equation system.

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