Research Article
On Sparre Andersen Model with Partial Premium Payment Strategy to Shareholders with Dependence via Sperman Copula
Delwendé Abdoul-Kabir Kafando,
François Xavier Ouedraogo,
Lassané Sawadogo,
Kiswendsida Mahamoudou Ouedraogo,
Pierre Clovis Nitiema
Issue:
Volume 13, Issue 1, February 2024
Pages:
1-7
Received:
8 December 2023
Accepted:
22 December 2023
Published:
8 January 2024
Abstract: This paper is based on the Poisson composite risk model, popularised for its flexibility in modelling loss occurrences. However, it innovates by incorporating a strategy of distributing dividends to shareholders, adding a realistic dimension to the financial implications. A key element is the introduction of a constant threshold 'b', representing a critical amount beyond which claims become significant. This threshold makes it possible to distinguish between small, routine claims and major events with a significant impact on reserves. In addition, the model introduces a dependency between the amount of claims and the time between claims via the Spearman copula. This copula captures the non-independence often observed in insurance data, where large claims tend to be followed by claim-free periods or vice versa. The analysis then focuses on the integro-differential equation associated with the model, which describes the evolution of Gerber's Shiu function, a fundamental element in assessing the reserve required to cover future obligations. The Laplace transform of this function is also studied, providing valuable information on the distribution of the long-term reserve.
Abstract: This paper is based on the Poisson composite risk model, popularised for its flexibility in modelling loss occurrences. However, it innovates by incorporating a strategy of distributing dividends to shareholders, adding a realistic dimension to the financial implications. A key element is the introduction of a constant threshold 'b', representing a...
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Research/Technical Note
Statistical Analysis of Regional Heterogeneity of Marital Dissolution Among Women in Ethiopia, EDHS 2016: Application of Multilevel Logistic Regression Model
Abdisa Teshome*,
Jaleta Abdisa
Issue:
Volume 13, Issue 1, February 2024
Pages:
8-20
Received:
4 January 2024
Accepted:
16 January 2024
Published:
7 March 2024
Abstract: Divorce is a major life stressor for the individuals involved, with potentially strong negative consequences for the mental and physical health of all members of the family. The aim of this study was to investigate the existence of regional heterogeneity marital dissolution among women in Ethiopia. The study used data from the 2016 Ethiopia Demographic and Health Survey which was a stratified two stage cluster sampling procedure was used. The researcher has been used (n=11405) of all married women from the selected population of study nested within nine regional states and two administrative cities in Ethiopia at time of interview. The Multilevel model were used to explore the major risk factors and regional variations of marital dissolution in Ethiopia using R statistical software. The descriptive result revealed that among eligible married women the proportion of marital dissolution was 9.91%. Among the three multilevel logistic models the random slope model found to be the best description of the data set and to evaluate the within and between regional heterogeneity of marital dissolution. Using this model variables that significantly affect the marital dissolution in Ethiopia were residence, education level of women, work status of women, duration of marriage, number of children, education level of husband and number of unions. The effects of the determinant variables are the same for each region, but the number of children and education level of husband were the two variables which varies within and between in each region. The other important result from this paper is that missing data analysis using appropriate imputation technique was performed to make better inferences.
Abstract: Divorce is a major life stressor for the individuals involved, with potentially strong negative consequences for the mental and physical health of all members of the family. The aim of this study was to investigate the existence of regional heterogeneity marital dissolution among women in Ethiopia. The study used data from the 2016 Ethiopia Demogra...
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Research Article
Comparative Analysis of the Various Statistical Techniques Applied to Assess Participants' Laboratory Performance in an Ordinary Portland Cement (OPC) Proficiency Testing Program
Issue:
Volume 13, Issue 1, February 2024
Pages:
21-28
Received:
8 February 2024
Accepted:
21 February 2024
Published:
7 March 2024
Abstract: Outliers in data analysis pose both challenges and opportunities for researchers. On one hand, if not adequately addressed, outliers can distort statistical analyses and lead to flawed conclusions. Conversely, outliers can also offer valuable insights into underlying processes or factors at play. One commonly used method for identifying outliers is through the analysis of interquartile ranges (IQRs). By accurately detecting and treating these anomalies, researchers can ensure the accuracy and validity of their findings. The major causes of outliers in data analysis stem from measurement and sampling errors. These errors can arise from issues such as human errors in data collection or problems with measurement equipment. Researchers must comprehend these causes to appropriately address outliers and minimize their impact on the analysis. Treating outliers effectively can greatly enhance data analysis by providing a more precise representation of underlying patterns and relationships. Removal or adjustment of extreme values enables researchers to obtain a clearer and more reliable picture of the phenomena under investigation, leading to crucial insights and facilitating further analyses and decision-making. Addressing outliers also offers opportunities for additional research and a deeper understanding of the underlying processes or factors at play. By extensively investigating the reasons behind outliers, researchers can gain valuable insights that can guide future research efforts and contribute to more informed decision-making based on the data. An exemplary illustration of the significance of accurate assessment techniques in statistical analyses is the OPC fineness study. This study analyzed the impact of various assessment methods on scoring results by comparing data from different laboratories using z-scores. The findings of this study demonstrated that the choice of assessment technique significantly influenced the scoring outcomes. Therefore, careful consideration of assessment procedures is crucial for obtaining reliable and comparable results in statistical analyses. In conclusion, outliers in data analysis present both challenges and opportunities for researchers. Accurately detecting and addressing outliers is essential for obtaining reliable and meaningful results. A comprehensive understanding of the causes of outliers, such as measurement and sampling errors, is necessary for appropriate treatment. Effectively treating outliers enhances the accuracy and validity of analysis and provides avenues for further research and informed decision-making. The OPC fineness study exemplifies the importance of assessment techniques in statistical analyses. A nuanced understanding of outlier detection and treatment is indispensable for drawing valid statistical conclusions.
Abstract: Outliers in data analysis pose both challenges and opportunities for researchers. On one hand, if not adequately addressed, outliers can distort statistical analyses and lead to flawed conclusions. Conversely, outliers can also offer valuable insights into underlying processes or factors at play. One commonly used method for identifying outliers is...
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