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In this paper, we have compared some linear and nonlinear models for explaining and forecasting the productionof two different crops, Mango and the Sugarcane using area of fields as the auxiliary variable. The models under considerations are compared on the basis of different fitting measures such as, Coefficient of Determination (R²),Residual Mean Square (s²), Mean Absolute Error (MAE) and Akaike Information Criterion (A.I.C.). The two primary data sets have been collected, first for sugarcane production from the Sitapur district of Uttar Pradesh state in India and second for mango production from the Lucknow district of Uttar Pradesh state in India. The fitting measures are calculated for the collected primary data sets and the best fitted models are selected and recommended for further use on the basis of the fitting measures. From the data analysis for both the data sets, it is found that the Compound, Growth, Exponential and Logistic models are equally good for practical applications. [ABSTRACT FROM AUTHOR]

Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model's forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model's accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike's information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires. [ABSTRACT FROM AUTHOR]

Occupancy models are a vital tool for ecologists studying the patterns and drivers of species occurrence, but their use often involves selecting among models with different sets of occupancy and detection covariates. The information‐theoretic approach, which employs information criteria such as Akaike's information criterion (AIC) is arguably the most popular approach for model selection in ecology and is often used for selecting occupancy models. However, the information‐theoretic approach risks selecting models that produce inaccurate parameter estimates due to a phenomenon called collider bias, a type of confounding that can arise when adding explanatory variables to a model. Using simulations, we investigated the consequences of collider bias (using an illustrative example called M‐bias) in the occupancy and detection processes of an occupancy model, and explored the implications for model selection using AIC and a common alternative, the Schwarz criterion (or Bayesian information criterion, BIC). We found that when M‐bias was present in the occupancy process, AIC and BIC selected models that inaccurately estimated the effect of the focal occupancy covariate, while simultaneously producing more accurate predictions of the site‐level occupancy probability than other models in the candidate set. In contrast, M‐bias in the detection process did not impact the focal estimate; all models made accurate inferences, while the site‐level predictions of the AIC/BIC‐best model were slightly more accurate. Our results show that information criteria can be used to select occupancy covariates if the sole purpose of the model is prediction, but must be treated with more caution if the purpose is to understand how environmental variables affect occupancy. By contrast, detection covariates can usually be selected using information criteria regardless of the model's purpose. These findings illustrate the importance of distinguishing between the tasks of parameter inference and prediction in ecological modeling. Furthermore, our results underline concerns about the use of information criteria to compare different biological hypotheses in observational studies. [ABSTRACT FROM AUTHOR]

It is a big challenge to select and combine multiple similarity-based approaches in a proper way for link prediction. Recently, some studies have focused on hybrid link prediction, but neglected to select models for link prediction. Actually, model averaging is more promising to reduce the risk of misspecified. Therefore, this paper proposes a novel linear model to integrate various kinds of local indices, and employs two typical model averaging approaches, Smooth Akaike Information Criterion (S-AIC) and Smooth Bayesian Information Criterion (S-BIC), for hybrid link prediction. It is worth noting that the objective function motivated by KL divergence matches our model quite well without unconfirmed links, which indicates node pairs of observed links should have larger similarities than unconnected node pairs. Experimental results on six datasets demonstrate that the proposed method can achieve more accurate performance. Our work provides a promising way for hybrid link prediction, and is a preliminary exploration to study on model averaging for high dimensional matrices. • A novel linear model is proposed to integrate multiple local indices via a new defined objective function motivated by KL divergence without unconfirmed links. • Two effectiveness model averaging approaches are proposed for hybrid link prediction based on S-AIC and S-BIC. • Model averaing is a promising way to organize multiple algorithms for link prediction. [ABSTRACT FROM AUTHOR]

Background: In elite football (soccer), periodic health examination (PHE) could provide prognostic factors to predict injury risk. Objective: To develop and internally validate a prognostic model to predict individualised indirect (non-contact) muscle injury (IMI) risk during a season in elite footballers, only using PHE-derived candidate prognostic factors. Methods: Routinely collected preseason PHE and injury data were used from 152 players over 5 seasons (1st July 2013 to 19th May 2018). Ten candidate prognostic factors (12 parameters) were included in model development. Multiple imputation was used to handle missing values. The outcome was any time-loss, index indirect muscle injury (I-IMI) affecting the lower extremity. A full logistic regression model was fitted, and a parsimonious model developed using backward-selection to remove factors that exceeded a threshold that was equivalent to Akaike's Information Criterion (alpha 0.157). Predictive performance was assessed through calibration, discrimination and decision-curve analysis, averaged across all imputed datasets. The model was internally validated using bootstrapping and adjusted for overfitting. Results: During 317 participant-seasons, 138 I-IMIs were recorded. The parsimonious model included only age and frequency of previous IMIs; apparent calibration was perfect, but discrimination was modest (C-index = 0.641, 95% confidence interval (CI) = 0.580 to 0.703), with clinical utility evident between risk thresholds of 37–71%. After validation and overfitting adjustment, performance deteriorated (C-index = 0.589 (95% CI = 0.528 to 0.651); calibration-in-the-large = − 0.009 (95% CI = − 0.239 to 0.239); calibration slope = 0.718 (95% CI = 0.275 to 1.161)). Conclusion: The selected PHE data were insufficient prognostic factors from which to develop a useful model for predicting IMI risk in elite footballers. Further research should prioritise identifying novel prognostic factors to improve future risk prediction models in this field. Trial registration: NCT03782389 [ABSTRACT FROM AUTHOR]