A Note on N in Bayesian Information Criterion for Item Response Models.

This study compared the performance of Poisson regression, negative binomial regression and generalized negative binomial regression models using road traffic crashes data collected from Ekiti state command of Federal Road Safety Commission (FRSC). The method of maximum likelihood estimation was employed to determine the parameter estimate for each models, the significance of each variable consider were examined. The criteria used in selecting the best model fitted are Akaike information criterion (AIC), Bayesian information criterion and Deviance. R software package was used in analysing the data. Firstly multicollinearity was tested to determine if there is high correlation between the predictor variables. Data were analysed and the result from the three model were compared using Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the deviance.The result from the three models were compared using AIC, BIC and Deviance, AIC values for the models are (414.79, 476.8 and 587.312), BIC values (490.8873, 495.59 and 589.321) and Deviance value (61.93, 66.927 and 69.927) respectively. Generalized negative binomial regression (GNBR) had the least value of AIC and BIC, thus consider as the best model for analyzing road traffic crashes data in Ekiti state, Nigeria. Having shown a smaller AIC, BIC and deviance values, the generalized negative binomial was consider a better fit model when analysing road traffic crashes in Ekiti state, Nigeria.

The accuracy of determining the height of trees is essential both in forestry and in scientific research. Height is usually determined using specific models, where it is a function of the diameter at breast height. On the materials of 23 sample plots with the measurement of model trees in birch stands, the parameters were determined for 29 two-parameter and three-parameter models that are most often found in literary sources. The following metrics evaluated the quality of the models: root mean square error, mean absolute percentage error, coefficient of determination, adjusted coefficient of determination, Akaike information criterion, and Bayesian information criterion. Three-parameter models of the dependence of height on diameter by a set of metrics show somewhat better quality than two-parameter models. Nevertheless, in general, the differences between most models are minor. Along with the models selected as the best, the Näslund and Chapman-Richards equations, which are often used in the literature as the most flexible, showed good quality. The methodology of this study allows you to repeat the same work for tree species and forest conditions, for which information on the nature of the relationship of height with diameter is incomplete or missing.

Background: Removing dyes from wastewater is crucial for environmental protection and public health. Dyes used in various industries can be toxic and persistent, posing threats to aquatic ecosystems and human health. Methods: This study examined the efficiency of beech wood-derived activated carbon (BW-AC) in removing Acid Red 18 (AR18) and Methylene Blue (MB) dyes. A batch adsorption procedure was used to investigate the impacts of various operational variables, including pH, contact time, adsorbent dosage, and initial dye concentration. Results: Changes in pH did not significantly affect removal efficiency. However, increasing contact time and adsorbent dosage notably enhanced removal rates, achieving nearly complete removal after 180 minutes. Various statistical metrics were used to identify the best-fitting model, including the corrected Akaike information criterion (AICc ) and Bayesian information criterion (BIC). The general order kinetic model provided the best fit to the experimental data based on its AICc values (range: -45.42 to 12.435) and BIC values (range: -48.16 to 6.67) for AR18. For MB dye, the ranges were -5.81 to 7.057 for AICc and -9.58 to 3.282 for BIC. Regarding the adsorption isotherms, the correlation capabilities of the models, as assessed by AICc and BIC, were ranked as follows for AR18: Freundlich, Liu, and Langmuir; while for MB dye, the ranking was Langmuir, Liu, and Freundlich. Conclusion: The results demonstrate that BW-AC is a highly promising adsorbent for dye removal from aqueous solutions, offering a sustainable, environmentally friendly, and cost-effective solution for water and industrial wastewater treatment.

The two-parameter logistic (2PL) item response theory model is a statistical model for analyzing multivariate binary data. In this article, two groups are brought onto a common metric using the 2PL model using linking methods. The linking methods of mean–mean linking, mean–geometric–mean linking, and Haebara linking are investigated in nonrobust and robust specifications in the presence of differential item functioning (DIF). M-estimation theory is applied to derive linking errors for the studied linking methods. However, estimated linking errors are prone to sampling error in estimated item parameters, thus resulting in artificially increased the linking error estimates in finite samples. For this reason, a bias-corrected linking error estimate is proposed. The usefulness of the modified linking error estimate is demonstrated in a simulation study. It is shown that a simultaneous assessment of the standard error and linking error in a total error must be conducted to obtain valid statistical inference. In the computation of the total error, using the bias-corrected linking error estimate instead of the usually employed linking error provides more accurate coverage rates.

Growth curve analysis can help to optimize the management, determine nutritional requirements, predict the weight of animals at a specific age, and to select highly productive animals. Therefore, this study aimed to find the best-fitted nonlinear functions to provide a specific shape of the growth curve from birth to yearling age in different generations of Boer x Central Highland goats. Gompertz, Logistic, Brody, Von Bertalanffy, Monomolecular, Negative exponential, and Richards models were evaluated to quantify their ability to describe the biological growth curve. Root mean square error (RMSE), Bayesian information criterion (BIC), adjusted coefficient of determination (AdjR2), and Akaike's information criterion (AIC) were used to evaluate the goodness of fit and flexibility of the models. Data were analyzed using the nonlinear regression procedure of SAS. High AdjR2 and lower AIC, BIC, and RMSE values are indicators of best-fitted model. The best-fitting model for the first filial generation (F1), second filial generation (F2), and male goats' growth data was Brody function, whereas the Richards model, followed by Brody, best described the growth of third filial generation (F3) and female goats. The values of parameter A (asymptotic weight) for F1, F2, F3, female, and male goats based on the Brody model were 30.5±1.32, 28.2±1.38, 24.4±1.04, 27.8±0.94, and 29.8±1.32 kg for F1, F2, F3, female, and male goats, respectively. As per the best-fitted growth function, the asymptotic weight tended to reduce when the filial generation increased. The asymptotic weight for male goats was higher than for female goats. F1 had a slightly small value of parameter K, followed by F2 and F3. Both males and females had similar maturity rates. Based on the Brody function, the correlation between maturation rate and mature weight was high (-0.98, P<0.001). The correlation estimates for A-B and B-K were 0.27 and -0.15, respectively. Brody was best fitted for most goat categories, although Richards, followed by Brody, was best fitted for female and F3 goats. Besides, Brody could be better than Richards due to the ease of interpretation, convergence, and applicability for a small sample size. Therefore, the Brody function can predict the mature body weight, maturation rate, and growth rate of Boer x Central Highland goats and be used to formulate breeding and management strategies for profitable goat farming.

This study was conducted to determine the live weight model of the broiler chicks by using the most appropriate mathematical growth curves. Live weights were used in broiler chicks grown for 0-6 weeks. Logistics, Gompertz, Weibull, Hossfeld and Von Bertalanffy models and multivariate adaptive regression splines (MARS) data mining algorithm were used to define the live weights of the chickens. In the comparison of the models, the determination coefficient (R2), mean square error (MSE), Akaike's Information Criterion (AIC) and Schwarz Bayesian Information Criterion (BIC) values were used. As a result of the study, it is seen that Gompertz model is the best model to define live weight of the broilers in the Gompertz model, R2, MSE, RMSE, AIC, BIC and growth rates for male broiler were 0.9998, 470.570, 21.681, 68.750, 68.934 and 0.241, respectively. The actual measured live weight values and the weight values estimated by Logistics, Gompertz, Weibull, Hossfeld, Von Bertalanffy models and MARS algorithm are close and in harmony with each other in the graph. However, the weight values estimated from the MARS algorithm are much closer to the observed live weight values. The represent study also demonstrated a very high predictive performance of the MARS data mining algorithm for describing the growth of chicken. In conclusion, MARS algorithm can be a good alternative for breeders aiming at describing the weight-age relationship of broiler chickens.

Kenya is among the countries that are continuously investing in wind energy to meet her electricity demand. Kenya is working towards its vision 2030 of achieving a total of 2GW of energy from wind industry. To achieve this, there is a need that all the relevant data on wind characteristics must be available. The purpose of this study is, therefore, to find the most efficient two-parameter model for fitting wind speed distribution for Narok County in Kenya, using the maximum likelihood method. Hourly wind speed data collected for a period of three years (2016 to 2018) from five sites within Narok County was used. Each of the distribution’s parameters was estimated and then a suitability test of the parameters was conducted using the goodness of fit test statistics, Kolmogorov-Smirnov, and Anderson-Darling. An efficiency test was determined using the Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, with the best decision taken based on the distribution having a smaller value of AIC and BIC. The results showed that the best distributions were the gamma distribution with the shape parameter of 2.47634 and scale parameter of 1.25991, implying that gamma distribution was the best distribution for modeling Narok County wind speed data.

The determination of livestock growth patterns is important for meat or milk production systems, and nonlinear models are used to summarize and interpret the information. The aim of this study was to more accurately estimate growth curve parameters in buffalo cows by evaluating and selecting nonlinear mixed models that employ different types of residuals and include or not contemporary groups (CG) as a covariate. Weight records from 720 animals obtained over a period of 60 months were used. The growth curves were fit using nonlinear mixed-effects models. The Bertalanffy, Gompertz and Logistic models were evaluated. Modelling residuals using four structures (constant, combined, exponential and proportional) and the inclusion or not of CG in the models were also evaluated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the model. In addition to estimating the parameters of the nonlinear growth models and their correlations, the instantaneous growth rate and inflection point were obtained. The Bertalanffy model with a combined residual structure and CG exhibited the lowest AIC and BIC values. Asymptotic weight (A) estimates ranged from 621.8 to 742.1 kg, and the maturity rate (k) ranged from 0.068 to 0.115 kg/month. The correlation between A and k ranged from −0.32 to −0.82 among the models evaluated. The selection criteria indicated that the Bertalanffy model was the most suitable for growth curve analysis in buffaloes.

Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with -cells and the other with no -cell component to determine their capability in predicting glucose concentration and determine type 1 diabetes pathways using published glucose concentration data for four groups of experimental mice. The groups of mice were numbered Mice Group 1–4, depending on the diabetes severity of each group, with severity increasing from group 1–4. A Markov Chain Monte Carlo method based on a Bayesian framework was used to fit the model to determine the best model structure. Akaike information criteria (AIC) and Bayesian information criteria (BIC) approaches were used to assess the best model structure for type 1 diabetes. In fitting the model with no -cells to glucose level data, we varied insulin absorption rate and insulin clearance rate. However, the model with -cells required more parameters to match the data and we fitted the -cell glucose tolerance factor, whole body insulin clearance rate, glucose production rate, and glucose clearance rate. Fitting the models to the blood glucose concentration level gave the least difference in AIC of , and a difference in BIC of for Mice Group 4. The estimated AIC and BIC values were highest for Mice Group 1 than all other mice groups. The models gave substantial differences in AIC and BIC values for Mice Groups 1–3 ranging from to . Our results suggest that the model without -cells provides a more suitable structure for modelling type 1 diabetes and predicting blood glucose concentration for hypoglycaemic episodes.

This study presents a comparative analysis of the Inverse Weibull and Inverse Lognormal distributions using both simulated and real-world data with Maximum Likelihood Estimation (MLE).Model selection criteria included Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Anderson-Darling (AD), and Kolmogorov-Smirnov (KS) tests.Six simulated sample sizes (50, 100, 150, 200, 250, and 500) were used, with 1000 replications each.Results showed the Inverse Lognormal distribution consistently had lower AIC and BIC values at small to moderate sample sizes.Furthermore, real-world stock price data (sample size 100) from the Nigerian Stock Exchange was analyzed.Descriptive statistics and goodness-of-fit tests favored the Inverse Lognormal model.These findings support the utility of AIC, BIC, AD, and KS in lifetime data modeling and model selection.

In this study, nine non linear growth curve models were used to determine the goodness of fit by the body weight measurements of the total number of 178 partridges(Alectoris chukar), 93 females, and 85 males, respectively. The R 2(coefficients of determination) values for the total partridges, females and males in Brody, Gompertz, Logistic, von Bertalanffy, asymptote regression,exponential, Monomolecular, Richards and Weibull-type were 0.985, 0.980 and 0.984, 0.997, 0.998 and 0.998, 0.996, 0.999 and 0.999, 0.995, 0.995 and 0.996, 0.985, 0.980 and 0.984, 0.891, 0.871 and 0.892, 0.985, 0.980 and 0.984, 0.997, 0.999 and 0.999, 0.997, 0.999 and 0.999, respectively. The R 2 values for Gompertz, Logistic, von Bertalanffy, Richards and Weibull-type were >0.99, while the exponential (<0.90) had the lowest. What’s more, the Gompertz, Logistic, Richards and Weibull-type models best described the data because of lower MSE (mean square error), AIC(Akaike’s information criteria) and BIC(Schwarz Bayesian information criterion), higher adj. R 2(Adjusted coefficient of determination) and r(the correlation coefficient between measured body weight and estimated body weight) and there was not an autocorrelation between the residual values. As a result, based on goodness of fit criteria; R 2, adj.R 2, MSE, r, AIC, BIC values, the Weibull-type model best described live weight data of the Partridges(Alectoris chukar).

PND63 Knowledge and Understanding of Alzheimer'S Disease: Revisiting the Psychometric Properties of the Adks Questionnaire

Shrimp farmed for a long period pauses its growth because of changes in environmental conditions or the achievement of the pond's carrying capacity. A modification of the von Bertalanffy model has been suggested to describe seasonal or oscillatory growth. Multiple logistic function is another alternative. This study used data from three earthen ponds farming the white legs shrimp Penaeus vannamei from spring to autumn, 202 days of commercial culture. An oscillatory version of the von Bertalanffy model, the Richards model, and double and triple logistic functions were contrasted. The best model was selected using information theory based on the Bayesian information criterion (BIC). We assumed a normal distribution of error. The triple logistic was the best model to describe the growth in the three earthen ponds because the lowest BIC value and BIC weight is more than 90%. The present study concluded that the multi-logistics approach best describes the growth in farmed shrimp P. vannamei if partial harvest is applied.

The PlotIRT set of R functions facilitates plotting a variety of curves associated with item response theory (IRT). These functions were developed so that researchers using IRT could have a simple, yet flexible method for creating graphics associated with item parameters. These graphics can be customized in numerous ways to produce attractive figures that can be used in manuscripts or presentations. The R software environment was chosen so that users could produce IRT graphics via free software. The plots that are available in PlotIRT are item and test characteristic curves, item and test information curves, expected item score functions, and posterior density distributions. The R functions are applicable to the two-parameter logistic, three-parameter logistic, graded, nominal, and multiple-choice models. The functions work with both individual items or with groups of items, which do not need to be based on the same IRT model. If the user chooses, the graphics can be customized with a number of options. These options include varying the color of the lines by item, response category, or test; making the lines a solid, dashed, or other pattern; labeling the response category curves; specifying the number of items per plot and the number of plots per window; labeling the plot and axes; and adjusting the font and its size. The posterior distribution plot requires the user to specify a response pattern for the items, and it may be produced along with the item characteristic curves and the underlying y distribution. Basic R functions can be used to read in an assortment of item parameter files, or item parameters can be typed directly into R arrays. As the function is submitted, plots appear in windows within the R interface. An option is included in the function to save the plots as any file type supported by R, including postscript and portable document format.

Mycoplasma pneumoniae infection prediction model for hospitalized community-acquired pneumonia children.