Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and handling complex data by adjusting the complexity of the model through the addition or reduction of terms. Moreover, quantile generalized additive regression was deliberated to evaluate relationships with lower quantiles, such as low-yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as conditional tail expectation (CTE), certainty equivalence of revenue (CER), and mean root square loss (MRSL). The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R² of 0.691. MRSL should be taken into consideration initially, as it is a farmer’s revenue assessment that distinguishes between those with and those without insurance. As a result, the GAM model indicates the least fluctuation in farmer income at the 90th percentile. Additionally, our study suggests that this type of insurance could apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production.

This paper is to find the estimators of the meanθfor a normal distribution with meanθand varianceaθ2,a>0,θ>0. These estimators are proposed when the coefficient of variation is known. A mean square error (MSE) is a criterion to evaluate the estimators. The results show that the proposed estimators have preference for asymptotic comparisons. Moreover, the estimator based on jackknife technique has preference over others proposed estimators with some simulations studies.

Cluster analysis is applied to group data so that samples within the same group are similar. A common problem with multivariate data implementation is that the data differs significantly from most of the other data. Outliers can significantly impact data analysis and model performance, making their detection crucial in various domains. This study presents an investigation of the outlier detection method using multiple linear regression for grouped multivariate data. The research compares the performance of the proposed method with two existing approaches, namely the Caroni and Billor (2007) method and the Hardin and Rocke (2004) method. In the case of uncontaminated data, the proposed method demonstrates a high percentage of detected outliers as the number of variables and sample size increase, indicating its effectiveness in outlier identification. In the scenario of contaminated data, the results reveal that the proposed method consistently outperforms both the Caroni and Billor method and the Hardin and Rocke method in terms of accuracy and precision. These findings highlight the effectiveness of the proposed method for outlier detection in grouped multivariate data. The study contributes to the existing knowledge of outlier detection approaches and provides insights into their performance under different data conditions. Researchers and practitioners can benefit from these findings when selecting appropriate outlier detection methods for various applications.

This is the protocol for a review and there is no The objectives are as follows: To compare the efficacy and safety of different chemotherapy regimens in children and young adults with high-grade osteosarcoma when compared to other chemotherapeutic regimens.

In the study of outlier identification in linear-circular regression, two new methods are proposed. By calculating the circular distance of each erroneous value and using the mean resultant length for outlier identification, these methods aim to enhance the precision and reliability of outlier detection. Their effectiveness will be assessed through comprehensive simulations on datasets with and without outlier contamination, comparing them with the previous method. Additionally, the methods were tested on real-world data, specifically wind speed and wind direction data, to further validate their practical applicability. Three metrics are used to evaluate their performance: the probability of correctly identifying all outliers, the masking effect, and the swamping effect. While occasional misclassification of inliers as outliers is possible, the results indicate that both proposed methods demonstrate strong overall performance.