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Wyświetlanie 1-2 z 2
Tytuł:
General linear model – an effective tool for analysis of claim severity in motor third party liability insurance
Autorzy:
Šoltés, Erik
Zelinová, Silvia
Bilíková, Mária
Powiązania:
https://bibliotekanauki.pl/articles/1186927.pdf
Data publikacji:
2019-12-10
Wydawca:
Główny Urząd Statystyczny
Tematy:
general linear model
claim severity
motor third party liability insurance
least squares means
Opis:
The paper focuses on the analysis of claim severity in motor third party liability insurance under the general linear model. The general linear model combines the analyses of variance and regression and makes it possible to measure the influence of categorical factors as well as the numerical explanatory variables on the target variable. In the paper, simple, main and interaction effects of relevant factors have been quantified using estimated regression coefficients and least squares means. Statistical inferences about least-squares means are essential in creating tariff classes and uncovering the impact of categorical factors, so the authors used the LSMEANS, CONTRAST and ESTIMATE statements in the GLM procedure of the Statistical Analysis Software (SAS). The study was based on a set of anonymised data of an insurance company operating in Slovakia; however, because each insurance company has its own portfolio subject to changes over time, the results of this research will not apply to all insurance companies. In this context, the authors feel that what is most valuable in their work, is the demonstration of practical applications that could be used by actuaries to estimate both the claim severity and the claim frequency, and, consequently, to determine net premiums for motor insurance (regardless of whether for motor third party liability insurance or casco insurance.
Źródło:
Statistics in Transition new series; 2019, 20, 4; 13-31
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Extracting relevant predictors of the severity of mental illnesses from clinical information using regularisation regression models
Autorzy:
Kaushik, Sakshi
Sabharwal, Alka
Grover, Gurprit
Powiązania:
https://bibliotekanauki.pl/articles/2107145.pdf
Data publikacji:
2022-06-14
Wydawca:
Główny Urząd Statystyczny
Tematy:
adaptive LASSO
group LASSO
mental disorder
multicollinearity
random forest imputation
ridge regression
severity of an illness
Opis:
Mental disorders are common non-communicable diseases whose occurrence rises at epidemic rates globally. The determination of the severity of a mental illness has important clinical implications and it serves as a prognostic factor for effective intervention planning and management. This paper aims to identify the relevant predictors of the severity of mental illnesses (measured by psychiatric rating scales) from a wide range of clinical variables consisting of information on both laboratory test results and psychiatric factors . The laboratory test results collectively indicate the measurements of 23 components derived from vital signs and blood tests results for the evaluation of the complete blood count. The 8 psychiatric factors known to affect the severity of mental illnesses are considered, viz. the family history, course and onset of an illness, etc. Retrospective data of 78 patients diagnosed with mental and behavioural disorders were collected from the Lady Hardinge Medical College & Smt. S.K, Hospital in New Delhi, India. The observations missing in the data are imputed using the non-parametric random forest algorithm. The multicollinearity is detected based on the variance inflation factor. Owing to the presence of multicollinearity, regularisation techniques such as ridge regression and extensions of the least absolute shrinkage and selection operator (LASSO), viz. adaptive and group LASSO are used for fitting the regression model. Optimal tuning parameter λ is obtained through 13-fold cross-validation. It was observed that the coefficients of the quantitative predictors extracted by the adaptive LASSO and the group of predictors extracted by the group LASSO were comparable to the coefficients obtained through ridge regression.
Źródło:
Statistics in Transition new series; 2022, 23, 2; 129-152
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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