Goodness Of Fit Criteria Research Articles

Estimation of the graft failure by current value joint model, and extension to alternative parameterization structures: Cohort study.

In clinical practice, individuals are followed up to predict the outcome event of interest, and their longitudinal measurements are collected on a regular or irregular basis. We aimed to examine the classical approach, joint model (JM), and alternative parameterization structures using data on the effect of time-varying longitudinal measurements on survival. The motivating cohort dataset included 158 consecutive kidney transplant recipients who had baseline and follow-up data. Although the longitudinal log-transformed estimated glomerular filtration rate (log[eGFR]) measurements and graft failure have an association clinically, the 2 processes are analyzed separately in the classical approach. In addition to the extended Cox model, the current value JM, the weighted cumulative effect JM, and dynamic predictions were performed in the study, by taking advantage of R codes. Of the 158 patients, 34.8% were males. The mean age was 29.8 ± 10.9 years, and the median age was 26 years at the time of transplantation. The hazard ratio for graft failure was 8.80 for a 1-unit decrease in log(eGFR) in the extended Cox model, 10.58 in the current value JM, and 3.65 in the weighted cumulative effect JM. The presence of coronary heart disease was also found to be associated with log(eGFR): 0.199 (P = .03) for the current value JM and 0.197 (P = .03) for the weighted cumulative effect JM. The current value JM was identified as a better model than the extended Cox model and the weighted cumulative effect JM based on parameter and standard error comparison and goodness of fit criteria. JMs should be preferred, as they facilitate better clinical decisions by accounting for the varying slopes and longitudinal variation of estimated glomerular filtration rate among patients. Suitable types of models should be practiced depending on baseline biomarker levels, their trends over time, the distribution of the biomarkers, and the number of longitudinal biomarkers.

Prediction of Maize Crop Yield Using Principal Component Analysis of Weather Parameters

The use of principal component analysis in the development of statistical models for crop yield forecasting has been demonstrated. Maize crop yield data for a period of 21 years (2001-2021) were drawn from the Dacnet website and the weather data were collected from the Meteorological Observatory, Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology Pantnagar, Uttarakhand. Maximum temperature, Minimum temperature, Relative Humidity A.M, Relative Humidity P.M, Total rainfall, Sunshine hours, Wind velocity and Evapotranspiration were the weather parameters considered for the study. Out of the 21-year data, 17-year data were used for training the model while remaining 4 years data were used for testing the model. Weekly data on weather variables was used to create weather indices [1]. This work involves developing forecasting models using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). Applied to extract principal components (PCs) from the correlation matrix of predictors. Five models (Model 1 to Model 5) are developed using MLR, with varying numbers of PCs as regressors which also include time trend and maize yield as dependent variable. The model performance was measured using Adjusted R-squared (adj R2) and Root Mean Square Error (RMSE) as goodness of fit criteria. On the basis of adj R2 and RMSE, model 1 which includes all the calculated weather indices, was found to be best suited model with high adj R2 (74.18 %) and least RMSE (276.36). Hence, this model can be used to forecast maize yield for the studied region.

The potential of mixing model of wind speed distribution in Algerian High Plateaus

The evaluation of wind energy relies primarily on the probability density function PDF, which corresponds well with the wind speed data. Single PDFs are widely used in the assessment of wind. In contrast, homogeneous or heterogeneous mixed models are rarely used, especially in Algeria, where the bimodal wind speed distribution is expected. This research aims to investigate the potential of heterogeneous PDFs Generalized Extreme Value-Weibul and Normal-Extreme Value PDFs in assessing wind energy at three meteorological stations in the high plateau against the single widespread PDFs Weibull and GEV by analyzing five years of archived wind speed data. The estimation of mixed model parameters is obtained by applying the Expectation-Maximization algorithm, and the identification of the appropriate PDF is obtained by four goodness-of-fit (GOF) criteria and compared with the widespread single distributions. The results show that the mixed model surpasses widely the single model for all the GOF criteria used at the three selected sites. The proposed mixed model fits all the wind speed distributions related to unimodal and bimodal regimes.

0,1]Truncated Exponentiated Exponential Burr type X Distributionwith Applications

We introduce a new flexible distribution four-parameter which is called [0,1]Truncated Exponentiated Exponential Burr type X distribution ([0,1] TEE-BX). Using binomial series expansion and exponential expansion, the new distribution is expanded by four parameters. We derive moments, moment generating function, quantile function, order statistics and the Rényi entropy. The maximum likelihood estimation method is used to estimate the parameters of the proposed TEE-BX model [0,1]. Finally, using two real-world data sets, the performance of the [0,1] TEE-BX distribution is explored. Based on the certain goodness of fit criteria, we conclude that the [0,1] TEE-BX distribution has a better fit than the other distributions.

IDENTIFIKASI PROPERTI PSIKOMETRIK SKALA KONSTRUAL DIRI UNTUK MAHASISWA INTERNASIONAL DI INDONESIA

Self-construal is a state in which a person sees himself as a separate identity or as an identity that is more connected to others. Efforts to determine the validity and reliability of self-construal scale for international students in Indonesia are important to do, considering that self-construal is very influential on various psychological variables in international students. One of the most commonly used self-construal measures in different countries is the Self-Construal Scale (SCS) by Theodore M. Singelis (1994). This study aims to determine the psychometric properties of the SCS on international students in Indonesia. Based on the analysis of 100 collected data, it was found that the modified SCS meets content validity, construct validity, and reliability. The modified SCS met the four goodness of fit criteria with RMSEA = 0.075, SRMR = 0.074, and NFI = 0.90 and CFI = 0.95. Then, the reliability of the SCS is seen from the AVE value, which is 0.63 for the independent self-construal and 0.53 for the interdependent self-construal. Overall, the CR value reached 0.96 for independent self-construal and 0.93 for interdependent self-construal. It can be concluded that the SCS is valid and reliable for measuring self-construal among international students in Indonesia.

A Modified Split-Plot Design Rice Yield Model and Analysis

In this research, a modified split-plot design model (SPDM) is introduced, where the mean part of the SPDM is tailored with a three-parameter Gompertz function, which modifies the SPDM to an intrinsically nonlinear SPDM. The model is applied to a balanced 2-replicated 3×42 mixed-level split-plot design experiment to determine the irrigation effect on four varieties of rice trial at four different rates of nitrogen fertilizer. The SPDM parameters were estimated through the methods of estimated generalized least squares (EGLS) and restricted maximum likelihood estimation (REML) for estimating the SPDM variance components. The parameter estimates are compared to estimates from ordinary least squares (OLS) and maximum likelihood estimation (MLE) estimates of the variance using four median adequacy measures and three information criteria for goodness of fit. The results obtained shows that the modified SPDM is a good fit, and its EGLS-REML estimates are of better reliability and adequacy compared to the OLS and EGLS-MLE techniques.

Use of data mining algorithms to predict body weight of Dorper sheep Breed in two selected villages of Lepelle-Nkumpi local municipality in Limpopo province, South Africa

Sheep farmers in village areas find it difficult to estimate body weight without the weighing tools. The best fitted model for estimation of sheep body weight is not conclusive. Classification and regression tree (CART), Chi-square automatic interaction detection (CHAID), Exhaustive chi-square automatic interaction detection (Ex-CHAID) and Multivariate adaptive regression spline (MARS) were used to predict body weight (BW) of 306 Dorper sheep (female = 244 and male = 62) aged 1 to 2 years from linear body measurement traits such as rump height (RH), rump length (RL), heart girth (HG), withers height (WH), and body length (BL). Goodness of fit criteria's such as Pearson's correlation coefficients (r), coefficient of determination (R2), Akaike's information criterion AIC and adjusted R2 (Adj.R2) were computed for data mining algorithms performance assessment. The results indicated that MARS was the best data mining algorithm with r of 0.979, R2 of 0.958, Adj.R2 of 0.957, RMSE of 2.503 and AIC of 422.25. The MARS model provided the most appropriate predictive capability in the prediction of body weight for Dorper sheep with HG, WH, RH, BL, and sex (male) as the important traits. As a result, MARS data mining algorithm might be used to identify factors that might be vital to improve body weight of Dorper sheep for breeding programs.

Psychometric Characteristics of the Intergroup Anxiety Scale among International Students in Indonesia

When someone perceives significant differences in others, they may feel anxious about the potential for negative interactions or undesirable outcomes that may arise from such interactions. This is referred to as intergroup anxiety. It is important to research the validity and reliability of the intergroup anxiety scale for international students in Indonesia because intergroup anxiety affects various psychological variables in them. One of the most commonly used instruments to measure intergroup anxiety is the Intergroup Anxiety Scale (IAS) by Walter G. Stephan and Cookie White Stephan, developed since 1985. This study aims to evaluate the psychometric properties of the IAS in the context of international students in Indonesia. From the analysis of 100 collected data points, the results show that the IAS used in this study meets the criteria for content validity, construct validity, and reliability. The results indicate that the IAS meets four Goodness of Fit criteria, with RMSEA = 0.079, SRMR = 0.074, NFI = 0.90, and CFI = 0.95. Furthermore, the reliability of the IAS was evaluated with an Average Variance Extracted (AVE) value of 0.57. The overall Composite Reliability (CR) value reached 0.93. Therefore, it can be concluded that the IAS is a valid and reliable tool for measuring self-construal among international students in Indonesia.

Lactation Curves of Mastitic Vrindavani Cattle: A Statistical Approach

Mastitis is one of the important animal disease which causes huge economic loss. The present study was aimed to find the best lactation curve model that describes the milk production pattern of Vrindavani cattle suffered from mastitis. In the study, test day milk yield data of 161 mastitic Vrindavani cattle (incidence of the disease occurring within the three months of parturition) was collected from Cattle and Buffalo Farm, LPM section, ICAR‑IVRI over 5 years (2009-2014). Eight lactation curve models [Inverse quadratic polynomial model (ND), Incomplete Gamma function (WD), Linear decline model (CL), Wilmink model (WL) Mixed log model (ML), Mitscherlich x Exponential (ME), Morant and Gnanasakthy model (MG) and Ali & Schaeffer lactation curve model (AS)] were fitted. The best model was selected based on different goodness of fit criteria and the Durbin-Watson test was used to examine the presence of autocorrelation present in the data set. Kolmogorov-Smirnova test and Shapiro-Wilk were used to test the normality of the residuals. Based on the various goodness of fit test it was observed that the Mixed Log (ML) model was the best fitted model to describe the lactation pattern of mastitic Vrindavani cattle.

Exploring the Influence of Crisis Management, Principal Traits, School Culture, and External Support on the Principal’s Leadership Style in Times of Crisis: A Structural Equation Model

The leadership of school principals was challenged, resulting in many transformations and adjustments during the time of the global health crisis. This study aimed to determine the best-fit model for a principal’s leadership style in times of crisis. There were four exogenous variables, namely: crisis management, principal traits, school culture, and external support; and one endogenous variable which is the principal’s leadership style. Results showed that the levels of these four exogenous variables were high, which means that these variables were much observed. Results also revealed that the four exogenous variables have a significant relationship to the principal’s leadership style with external support having the strongest relationship. The fit indices of the structural equation model (SEM) together with the dimensions revealed that the 5-factor model is the best-fit model as it passed all the criteria for goodness of fit. It is recommended that the 5-factor model of the principal’s leadership style be utilized during the program enhancement of the school heads to improve their leadership style, and even apply it among the schools in Davao Region and beyond.

Development of Waves Critical Thinking Test: Physics Essay Test for High School Student

<p style="text-align: justify;">This study aims to produce a product to evaluate students' critical thinking skills that departs from physics content where students often have misconceptions. This research is a development research with research stages covering a) research and review literature; (b) planning chapter objectives; (c) developing a preliminary form; (d) field-testing the preliminary form; (e) Revise the preliminary form; (f) conducting a main field-test. The Waves Critical Thinking (WCT) test developed consists of 7 questions with 15 specific domains. Total percentage of content validity test was obtained 87.98% with appropriate criteria and based on the construct validity WCT test, the Goodness of Fit criteria were obtained which were classified as fit. The test instrument being tested consists of 15 objective items. The reliability of WCT test results 0.597 as a Cronbach's alpha score with the medium category and all the components have a good level of composite reliability. The outcome of the study was the WCT test with a valid state for measuring students' CT in a specific domain of physics wave material.</p>

Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma.

Durable remission has been observed in patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) treated with chimeric antigen receptor (CAR) T-cell therapy. Consequently, hazard functions for overall survival (OS) are often complex, requiring the use of flexible methods for extrapolations. We aimed to retrospectively compare the predictive accuracy of different survival extrapolation methods and evaluate the validity of goodness-of-fit (GOF) criteria-based model selection for CAR T-cell therapies in R/R LBCL. OS data were sourced from JULIET, ZUMA-1, and TRANSCEND NHL 001. Standard parametric, mixture cure, cubic spline, and mixture models were fit to multiple database locks (DBLs), with varying follow-up durations. GOF was assessed using the Akaike information criterion and Bayesian information criterion. Predictive accuracy was calculated as the mean absolute error (MAE) relative to OS observed in the most mature DBL. For all studies, mixture cure and cubic spline models provided the best predictive accuracy for the least mature DBL (MAE 0.013‒0.085 and 0.014‒0.128, respectively). The predictive accuracy of the standard parametric and mixture models showed larger variation (MAE 0.024‒0.162 and 0.013‒0.176, respectively). With increasing data maturity, the predictive accuracy of standard parametric models remained poor. Correlation between GOF criteria and predictive accuracy was low, particularly for the least mature DBL. Our analyses demonstrated that mixture cure and cubic spline models provide the most accurate survival extrapolations of CAR T-cell therapies in LBCL. Furthermore, GOF should not be the only criteria used when selecting the optimal survival model.

The evaluation of potentially toxic elements using artificial neural networks and fuzzy linear regression analysis methods in cappadocian volcanic ash soils of Turkey

The aim of this study is to determine the relationships between some potentially toxic elements (PTE) (Cu, Mn, Ni, Zn) in human stomach and intestinal tissues and toxic element contents in soil, vegetables and fruits. This study was conducted in the eastern of Erciyes Strato volcano, an area of 2400 km2 in Turkey. Tissue samples taken from the stomach and intestines of people living in the study area, soils, vegetables, and fruits were used as material. In the study, tissue samples of 26 people's stomach and intestines, 576 soil samples from 192 points and 3 different depths (0–30 cm, 30–60 cm, and 60–90 cm) and vegetable and fruit samples from 137 sampling points were taken. Cu, Mn, Ni, and Zn contents of human tissue samples, soil samples, vegetable and fruit samples were determined. Artificial Neural Networks method (ANN) and Fuzzy Linear Regression Analysis (FLRA) methods were used to determine the relationships between PTE contents in human tissue samples and soils, vegetables, and fruits. Root Mean squared error (RMSE) and coefficient of determination (R2) indices were used as the test criteria for goodness of fit. When compared with ANN method, it was determined that PTE values in stomach and intestinal tissue estimated by FLRA method had the lowest error and high R2 values. It was found that the most effective variable in estimating the average PTE value in stomach and intestinal tissue is PTE values in soil. It was determined that the FLRA regression analysis method has a better predictive power than the ANN method. Using FLRA and ANN regression methods, it was determined that there is a statistically high relationship between PTE contents in soils and stomach and intestinal tissues. It is recommended to make the study findings more meaningful with effective and reliable service planning by using different regression analysis methods in ecological and clinical studies.

Adaptation of the Career Decision Making Profile Measurement Tool for Students in Indonesia

In recent years, there was an increasing interest in the importance of individual differences in career decision making, which reflected the strategy pattern adopted by individuals. Optimal decisions were made by several individual when these strategies were adaptive. Therefore, this study aimed to adapt the measurement tool known as Career Decision Making Profile developed by Gati et al. (2010). To achieve this objective, a quantitative approach was employed, and a convenience sampling technique was used to select a sample of 204 Indonesian students. The results showed that the measurement tool had good internal consistency, with Cronbach's Alpha values ranging from .707 to .762. The Confirmatory Factor Analysis also indicated that the measurement tool model fulfilled the criteria for goodness of fit. Therefore, this tool in the Indonesian language was measured the pattern of career decision making strategies among students.

Application of Statistical Distributions to PM10 Concentrations: Van, Türkiye

Air pollution is one of the most serious environmental pollution problems that adversely affect human health and the environment. Industrializing and frequent use of low-quality coals for heating purposes and incorrect combustion techniques may cause intense air pollution in the winter season. There are several studies on air quality employing different methods using various air pollutants (carbon monoxide, nitrogen oxides, ground-level ozone, and particle pollution) in the related literature. In this study PM_10 (concentration of 10 micrometers or smaller size of air pollutants) levels in Van province which is one of the most crowded provinces in Eastern Anatolia, Türkiye. Due to the fuels used for heating in Van, the air quality may be higher than limits set by regulations several times during the year. In this study, PM_10 levels of Van are modeled using lognormal, Weibull, and Gamma distributions. Information and goodness of fit criteria are used to compare their performance. In addition, predictions of exceedances are provided for the PM_10 concentration higher than given limits. According to the results, the Gamma distribution performed better than the other two distributions in modeling the PM_10 concentrations in Van and predicted the exceedances accurately.

Optimization of test day for milk yield recording and sire evaluation in Murrah buffaloes.

In the present study, random regression models (RRM) were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Legendre polynomial function (LP), with the objective to find the best combination of "minimum test-day model," which would be essential and sufficient to evaluate the trait successfully. Data included for analysis were 10,615 first lactation monthly test-day milk yield records (5th, 35th, 65th, …, 305th) from 965 Murrah buffaloes for the period 1975-2018. Cubic to octic-order orthogonal polynomials with homogeneous residual variances were used for the estimation of genetic parameters. Random regression models with sixth-order were selected based on goodness of fit criteria like lower AIC, BIC and residual variance. Heritability estimates ranged from 0.079 (TD6) to 0.21(TD10). For both ends of lactation, the additive genetic and environmental variances were higher and ranged from 0.21 ± 0.12 (TD6) to 0.85 ± 0.35 kg2 (TD1) and 3.74 ± 0.36 (TD11) to 1.36 ± 0.14 kg2 (TD9), respectively. Between adjacent test-day records, genetic correlation estimates ranged from 0.09 ± 0.31 (TD1 and TD2) to 0.97 ± 0.03 (TD3 and TD4; TD4 and TD5), but values gradually declined as the distance between test days increased. Negative genetic correlations were also obtained between TD1 with TD3 to TD9, TD2 with TD9 and TD10, and TD3 with TD10. On the basis of genetic correlations, models with 5 and/or 6 test-days combination were able to account for 86.1%-98.7% of variation along the lactation. Models with fourth and fifth-order LP functions were considered to account for variance with combinations of 5 and/or 6 test-day milk yields. The model with 6 test-day combinations had a higher rank correlation (0.93) with model using 11 monthly test-day milk yield records. On the basis of relative efficiency, the model with 6 monthly test day combinations with fifth-order was more efficient (maximum 99%) than the model using 11 monthly test-day milk yield records. Looking into the similar accuracy with the 11TD model, and the low resources requirement, we recommend the use of the "6 test-day combination model" for sire evaluation. These models may help in reducing the cost and time for data recording of milk yield.

Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA.

By considering the existence of two separate analysis families and the usage of different data frequencies, this study aims to examine the effect of method choice, data frequency, and sector-based energy consumption on carbon dioxide (CO2) emissions by performing machine learning (ML) algorithms and time series econometric (TS) models simultaneously. In this situation, the study examines the United States (USA), considers sector-based energy consumption indicators as explanatory variables, uses monthly and yearly data between January 1973 and December 2021, estimates CO2 emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R2 and goodness of fit criteria; (ii) the estimation performance of the models increases with the high-frequency (i.e., monthly) data; (iii) the ML algorithms perform much better in case of high-frequency usage; (iv) some thresholds identify the effects of the sector-based energy consumption indicators on the CO2 emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO2 emissions for monthly and yearly data, respectively. Hence, the study provides to help the understanding role of method choice, data frequency, and sector-based energy consumption for the estimation of CO2 emissions. Based on the results, this study proposes that US policymakers should consider the ML algorithms, use higher-frequency data, and include sector-based energy consumption indicators to have a better estimation of CO2 emissions.

Statistical Modeling for Forecasting Fertilizer Consumption in India

Fertilizers have contributed significantly to increased agricultural yields, particularly for cereal crops and they will still be an important part of the science-based farming that is needed to feed the world's growing population. Fertilizers replenish the soil nutrients lost by the harvested crops, promote the use of high-yielding cultivars and boost biomass in tropical soils that are deficient in nutrients. In this study, data on fertilizer consumption in India was gathered from Agricultural Statistics at a Glance from 1950-51 to 2020-21 and utilized to fit the ARIMA model and forecast future usage. Forecasting has been done using the Box-Jenkins ARIMA approach. The ARIMA model is the most popular and widely applied forecasting model for time series data. The data was calculated using autocorrelation and partial autocorrelation functions. R programming software was used to estimate model parameters. The performance of the fitted model was evaluated using various goodness of fit criteria, such as AIC, BIC and MAPE. Empirical results revealed that the ARIMA (1,2,1) model was best suited to forecasting India's future total fertilizer use. Similarly, the ARIMA model was fitted for nitrogen, phosphorus, and potassium consumption in India independently. Forecasts from 2021-22 to 2030-31 are calculated using the chosen model. By 2030-31, total fertilizer use is predicted to reach 32,058.55 thousand tonnes. Policymakers should preferably base their judgments on reliable forecasts in order to tighten policies and achieve outcomes. Predicting future events using an appropriate time series model will assist policymakers, marketing strategies in making decisions related to export/ import and developing appropriate fertilizer consumption strategies.

Probable maximum tropical cyclone parameters for east and west coast of India

Storm surge simulation models require certain parameters for evaluating the worst possible event that could occur at a site with respect to a certain return period. The most significant probable maximum tropical cyclone parameters are pressure deficiency at the centre (ΔP) and maximum wind speed (Vmax) during the cyclone. In this study, the probable maximum tropical cyclone parameters that would yield the maximum probable storm surge along the Indian coasts of Arabian Sea and Bay of Bengal are estimated. Datasets are created based on various existing data archives for each basin. The datasets are subjected to extreme value analysis for determining the ΔP and Vmax parameters. The data are fitted to various probability distributions (Gumbel, Fréchet, Weibull and Log-normal) whose parameters (scale, shape, and location parameters) are estimated using graphical (least square fit) and numerical (order statistics approach) methods. A mean recurrence interval of 1000 and 10,000 years is considered for strategic structures. The best fit distribution and its parameters are obtained based on goodness of fit criteria. The resulting ΔP and Vmax are compared with theoretical maximum cyclone parameter values of each basin and revised till an optimal set of values are reached. The analysis shows that ΔP and Vmax values for Arabian Sea and Bay of Bengal are best represented by Weibull distribution. The estimated parameters are useful input to a storm surge model to determine the design basis flood level for the strategic coastal sites.

Investigation of egg production curve in ostrich using nonlinear functions

In most countries, ostrich farming is considered a developing branch of the efficient poultry industry. The profitability of ostrich farm requires specific consideration of productions features such as the female fertility, egg production, hatchability, and growth performance. Hence, this study aimed to fit nonlinear functions to describe the ostrich egg production pattern to achieve the most appropriate and recommendable mathematical function for future studies. For this purpose, 14,507 daily records of 184 female ostriches in 5 production seasons (periods) during 2016 to 2021 were used. Five nonlinear functions including Incomplete gamma (Wood function), Corrected gamma (McNally), nonlinear Logistic (Yang), Logistic (Nelder), and Lokhorst were fitted for modeling the egg production curve in ostrich. The goodness of fit criteria's including Mean Square Error (MSE), Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and selection of the best function. The results indicated that the Wood and the McNally functions with a slight difference in all fitting criteria were the best-fitted functions and the Yang function with the highest values of MSE, LRT, AIC, BIC, were the most inappropriate function to describe the ostrich egg production curve. The McNally and the Wood can be recommended as appropriate functions to describe egg production during 5 production seasons in the studied ostrich flock.