Modeling of Motor Insurance Extreme Claims through Appropriate Statistical Distributions

In the area of insurance, probability modeling has a wide variety of applications. In life insurance, the compensation sum is calculated in advance and may often be estimated using actuarial techniques, while in motor insurance, the claim amount is generally not known in advance. In the insurance business, the improvement of actuarial risk control strategies is an essential technique for controlling insurance risk. Although an insurance company’s risk assessment about its solvency is a complex and detailed problem, its solution begins with statistical modeling of individual claims’ amounts. This article emphasizes the possible ways of obtaining a suitable probability distribution model that accurately explains insurance risks and how to use such a model for risk management purposes. For this reason, we have applied modern programming techniques and statistical software implemented the methods provided based on data on premium amounts of third-party motor insurance claims.

Transitions Into and Out of Voluntary Work Over the Life Course: What is the Effect of Major Life Events?

The aim of the current study is to investigate which major life events are associated with transitions into and out of volunteering over the life course and, especially, why these associations exist. Social Production Function theory is used to derive hypotheses, which are tested using longitudinal data (adult subsample) from Lifelines. Associations between major life events and (a) volunteer take-up, nonvolunteer sample ( N = 59,773) and (b) volunteer cessation, volunteer sample ( N = 32,143) are studied by applying Linear Probability Modeling. Results show clear associations between specific major life events and starting and quitting volunteering. The influence on the latter is stronger than on the former. Most findings are in line with our theory-based expectations indicating that (a) voluntary work contributes especially to the fulfillment of the needs for status, stimulation, and behavioral confirmation and (2) life events causing losses (gains) in these needs are associated with a higher likelihood to take-up (quit) volunteering.

Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis

This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.

Duration of lead time in screening for lung cancer

Background Screening for lung cancer has used chest radiography (CR), low dose computed tomography (LDCT) and sputum cytology (SC). Estimates of the lead time (LT), i.e., the time interval from detection of lung cancer by screening to the development of symptoms, have been derived from longitudinal studies of populations at risk, tumor doubling time (DT), the ratio between its prevalence at the first round of screening and its annual incidence during follow-up, and by probability modeling derived from the results of screening trials. Objective To review and update the estimates of LT of lung cancer. Methods A non-systematic search of the literature for estimates of LT and screening trials. Search of the reference sections of the retrieved papers for additional relevant studies. Calculation of LTs derived from these studies. Results LT since detection by CR was 0.8–1.1 years if derived from longitudinal studies; 0.6–2.1 years if derived from prevalence / incidence ratios; 0.2 years if derived from the average tumor DT; and 0.2–1.0 if derived from probability modeling. LT since detection by LDCT was 1.1–3.5 if derived from prevalence / incidence ratios; 3.9 if derived from DT; and 0.9 if derived from probability modeling. LT since detection of squamous cell cancer by SC in persons with normal CR was 1.3–1.5 if derived from prevalence/incidence ratios; and 2.1 years if derived from the DT of squamous cell cancer. Conclusions Most estimates of the LT yield values of 0.2–1.5 years for detection by CR; of 0.9–3.5 years for detection by LDCT; and about 2 years or less for detection of squamous cell cancer by SC in persons with normal CR. The heterogeneity of the screening trials and methods of derivation may account for the variability of LT estimates.

Signal Reconstruction Based on Probabilistic Dictionary Learning Combined with Group Sparse Representation Clustering

In order to make full use of nonlocal and local similarity and improve the efficiency and adaptability of the NPB-DL algorithm, this paper proposes a signal reconstruction algorithm based on dictionary learning algorithm combined with structure similarity clustering. Nonparametric Bayesian for Dirichlet process is firstly introduced into the prior probability modeling of clustering labels, and then, Dirichlet prior distribution is applied to the prior probability of cluster labels so as to ensure the analyticity and conjugation of the probability model. Experimental results show that the proposed algorithm is not only superior to other comparison algorithms in numerical evaluation indicators but also closer to the original image in terms of visual effects.

A COMPARATIVE STUDY OF TECHNIQUES OF VISUALIZATION OF DISTRIBUTIONS FOR GEOGRAPHICAL DATA

Currently, technological advances have revolutionized the way natural phenomena are studied. Natural phenomena can be represented through distributions of geographic data that are a rich source of information and can be explored in different ways. One of them is the representation of uncertainty through the distribution of probability. Modeling the uncertainty of this type of distribution and representing it in geographic visualization is complicated because maps (common types of geographic visualization) need the visual environment to represent geographic space and there are not many ways to represent any other information. One of the ways often used as a solution is statistical summarization such as mean, but summarizing the data alone may can hide the data’s behavior and generates ambiguity. The concealment of the uncertainty of the data in visualization can be justified by the way the uncertainty is represented that may not be understood by the user. Technical proposals have been proposed to represent distributions, but generally they only represent the presence and spread of uncertainty recently others approaches based on probability of proportion of data, animation and interaction have proposed to make quantification of probability, but have not been used or compared formally for geographic data. The objective was qualitatively compare main approaches to visualize probability distributions on a geographical scenario (includes factors such as distance, size and variation), using the recent proposed approaches in the context of abstract data, analytical tasks and user study. The results show which approach has the better performance in the presented cases.