The upper part of Table 2 reports the single-coefficient results for the dummy variable model, including regression coefficients, Newey-West standard errors, and the corresponding t-statistics and p-values.
Mishra and Singh (2010b) provided a review of drought models that include regression models, time series models, probability models, artificial neural network models, and hybrid models; and spatio-temporal drought analysis; drought modeling under climate change scenarios.
The most common models include linear/logistic regression, random forests and boosted decision trees.
GLMs were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression [ 50].
For each model, results include regressions coefficients, their standard errors and related statistics (Wald's statistics, p-values and odds ratios).
We explored alternative regression models, including linear regression for all analyses, to ensure optimal fit of our selected models.
Our method can be also applied to more general models such as generalized linear models, including linear regression and other non-linear regression models with some modification.
Structural equation models constitute a very general and flexible class of statistical models including ordinary regression models and factor analytic models [ 7, 8].
The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models.
These models include empirical correlations, statistical regression and artificial neural networks (ANNs).
