The phrase "alternative distributions of" is correct and usable in written English.
It can be used in contexts discussing different types or variations of distributions, often in statistical or mathematical discussions.
Example: "The study examined alternative distributions of data to determine which model best fit the observed results."
Alternatives: "different types of distributions" or "various distributions of".
The power of the test against various alternative distributions of the model errors is illustrated.
In either case, the goal is to obtain information about an individual's preferences based on his or her choice from alternative distributions of outcomes with the same cost.
We perform simulation studies to verify the accuracy of the asymptotic null and alternative distributions of the LRTS.
We also considered four alternative distributions of azimuth.
It has a larger impact on the quantitative model behaviour but the same results can be achieved by alternative distributions of the other model parameters (results not shown).
First, we sought to describe differences in power and sample size requirements across alternative distributions of a prognostic factor and magnitudes of the interaction effect.
As an alternative, distribution of GFP-tagged ATX1 (wild-type or various deletion forms) was followed in the presence of AtMTM1-RFP-fusion protein co-expressed in tobacco leaf-epithelial cells.
The occurrence of atypical clinical features in persons with vCJD that encodes methionine and valine on PRNP codon 129 and human-to-human transmission of vCJD through blood transfusion have raised concern about atypical clinical features and alternative distribution of PrPSc in vCJD (5 ).
The approximation to the alternative distribution of the F-test proposed here is that it is a non-central F with 1 numerator degree of freedom, 2(J - 1) denominator df, and non-centrality parameter (NCP) δ, where δ 2 = J K ∑ α i 2 (K σ P ¯ 2 + σ E 2 ) = N K ∑ α i 2 T (K σ P ¯ 2 + σ E 2 ).
Where the data are skewed and/or heavy tailed, we can model the costs using appropriate alternative distributions instead of assuming normality.
GLMs are an attractive approach when we have covariates, because they offer some of the benefits of alternative distributions and/or transformation without the need to back transform.
