This research aims to determine the factors of clean and healthy living behavior that influence environmental-based diseases in Lampung Province, Indonesia using structural equations with the Weighted Least Square diagonal estimation method. Data were collected from students living in all districts in Lampung Province, Indonesia (n = 8524). Our findings show that environmental-based diseases are influenced by the clean and healthy living behavior of students in Lampung Province, Indonesia. The cleaner and healthier a society is, the smaller the incidence of environmental-based diseases in the community. Factors such as the use of clean water, the habit of washing hands, the use of clean water in latrines and the habit of not smoking, can reduce the number of people suffering from environmental-based diseases by 31%, 4%, 12%, 16%, respectively.

This paper investigates the impact of temporally aggregated data on the power and significance level of Ramsey’s RESET test, which is commonly used to assess the functional form of a model by examining nonlinear relationships. Through Monte Carlo techniques, we analyze the influence of temporal aggregation on the effectiveness of the test. Our findings demonstrate that temporal aggregation significantly affects both the power and significance level of the test, potentially leading to distortions in detecting nonlinear relationships and erroneous conclusions about model specifications. However, empirical analysis reveals that the effect of temporal aggregation on the RESET test varies across different pairs of stock market indexes and temporal aggregation levels. This underscores the importance of carefully considering the choice of stock market indexes and the level of temporal aggregation when conducting the RESET test, ensuring the accuracy and reliability of empirical research employing this statistical test.

This article proposes estimation of population mean using factor-type (F-T) estimator in the presence of measurement errors under systematic sampling scheme. The factor-type (F-T) estimator is biased and the expression of bias, MSE and optimum MSE of proposed estimator is obtained up to first order of approximation under the concept of large sample approximations and a comparative study of this estimator along with related pre-existing estimators is taken out. A simulation study has been performed to ratify the performance of proposed estimator in systematic sampling. The proposed factor-type (F-T) estimator is found better than other existing estimators as considered under in this study.

This research evaluates the performance of the ARIMA method in forecasting hierarchical time series data of tourist arrivals in Australia from 1998 to 2016 using a bottom-up strategy. A comparative analysis is conducted between the predicted results and the actual data for both short-term and long-term periods, as well as with various normalization methods for each hierarchical level. The study concludes that ARIMA generally performs better in short-term forecasting at a hierarchical level. However, the evaluation results indicate that SMAPE (Symmetric Mean Absolute Percentage Error) values fluctuate across different forecasting periods, influenced by prediction data generated from various ARIMA models. This study does not determine whether one normalization method is superior to another, as the evaluation results show no significant differences. Nevertheless, this research provides insights into the effectiveness of hierarchical time series forecasting using the ARIMA method and a bottom-up strategy at each hierarchical level for both short-term and long-term periods. It also assesses the performance of various normalization methods used.

This study compared three approaches (i.e., averaging within-study effect sizes, three-level meta-analysis, and robust variance estimation) to handle dependent correlational effect sizes in conducting a meta-analysis. Data were from a meta-analytic study examining the relationship between writing self-efficacy and writing proficiency. To examine the differences in the performance of the three approaches, seven conditions were created by the number of studies and the number of effect sizes per study. While all three approaches produced similar results in the average effect size and standard error, the averaging approach had much smaller variance estimates. The patterns were basically consistent across different conditions. This study informs meta-analysts of appropriate procedures in handling the dependent effect sizes.

This paper introduces a novel Extended Generalized Frechet (EGFr) distribution, a flexible extension of the Frechet distribution. The EGFr incorporates additional parameters that provide enhanced flexibility for modeling diverse data sets, especially those with complex patterns or extreme values. The probability density function of the EGFr is derived from the T-X family of distributions and can be expressed as a linear combination of Frechet densities. We investigate the statistical properties of the EGFr, including moments, quantiles, hazard functions and order statistics. Maximum likelihood estimation is used to estimate the model parameters. Extensive simulations demonstrate the consistency and efficiency of the EGFr in parameter estimation. Real-life applications to reliability datasets demonstrate the superior performance of the EGFr over existing Frechet-based distributions. The EGFr’s ability to accurately capture complex data patterns and provide reliable estimates makes it a valuable tool for researchers and practitioners in the fields of reliability engineering and sciences.