Browsing by Subject "Bias correction"

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An adjusted coefficient of determination (R2) for generalized linear mixed models in one go
(2023) Piepho, Hans‐Peter
The coefficient of determination (R2) is a common measure of goodness of fit for linear models. Various proposals have been made for extension of this measure to generalized linear and mixed models. When the model has random effects or correlated residual effects, the observed responses are correlated. This paper proposes a new coefficient of determination for this setting that accounts for any such correlation. A key advantage of the proposed method is that it only requires the fit of the model under consideration, with no need to also fit a null model. Also, the approach entails a bias correction in the estimator assessing the variance explained by fixed effects. Three examples are used to illustrate new measure. A simulation shows that the proposed estimator of the new coefficient of determination has only minimal bias.
Bias correction and trend analysis of temperature and rainfall in Eastern India
(2024) Srivastava, Rajiv Kumar; Sadhukhan, Biplab; Chakraborty, Arun; Panda, Rabindra Kumar
In this study trend analysis and bias correction have been done for dry (January–May) and wet (June–September) seasons under two future climate period 2021–2050 and 2051–2080 with respect to the current climate period 1980–2012 in Eastern India. The different representative concentration pathways (RCPs) of 2.6, 4.5, 6.0, and 8.5 were used to assess the future trend of the study area. Results indicate that the increasing RCP increases temperature (maximum and minimum) in all regions due to higher radiative forces (4–8.5 W/m 2 ) with respect to the baseline temperature during the period 2051–2080. Further, the bias-corrected rainfall has a declined trend with respect to baseline, and RCP’s values for both the time slices (2021–2050 and 2051–2080) showed less scattering in the amount of rainfall for the wet season in comparison to the dry season.