scholarly journals Mixed Poisson Regression Models with Varying Dispersion Arising from Non-Conjugate Mixing Distributions

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 16
Author(s):  
George Tzougas ◽  
Natalia Hong ◽  
Ryan Ho
Keyword(s):  
Life Insurance ◽  
Poisson Regression ◽  
Regression Models ◽  
Likelihood Estimation ◽  
Dispersion Parameters ◽  
Modelling Framework ◽  
Mixing Distributions ◽  
Insurance Portfolio ◽  
Varying Dispersion ◽  
Different Levels

In this article we present a class of mixed Poisson regression models with varying dispersion arising from non-conjugate to the Poisson mixing distributions for modelling overdispersed claim counts in non-life insurance. The proposed family of models combined with the adopted modelling framework can provide sufficient flexibility for dealing with different levels of overdispersion. For illustrative purposes, the Poisson-lognormal regression model with regression structures on both its mean and dispersion parameters is employed for modelling claim count data from a motor insurance portfolio. Maximum likelihood estimation is carried out via an expectation-maximization type algorithm, which is developed for the proposed family of models and is demonstrated to perform satisfactorily.

General mixed Poisson regression models with varying dispersion

2015 ◽  
Vol 26 (6) ◽  
pp. 1263-1280 ◽  
Author(s):  
Wagner Barreto-Souza ◽  
Alexandre B. Simas
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Varying Dispersion

scholarly journals AN EM ALGORITHM FOR FITTING A NEW CLASS OF MIXED EXPONENTIAL REGRESSION MODELS WITH VARYING DISPERSION

2020 ◽  
Vol 50 (2) ◽  
pp. 555-583 ◽  
Author(s):  
George Tzougas ◽  
Dimitris Karlis
Keyword(s):  
Regression Models ◽  
Real Data ◽  
Dispersion Parameters ◽  
Ml Estimation ◽  
Exponential Models ◽  
Exponential Regression ◽  
Natural Choice ◽  
Type Algorithm ◽  
Varying Dispersion ◽  
Heavy Tailed

AbstractRegression modelling involving heavy-tailed response distributions, which have heavier tails than the exponential distribution, has become increasingly popular in many insurance settings including non-life insurance. Mixed Exponential models can be considered as a natural choice for the distribution of heavy-tailed claim sizes since their tails are not exponentially bounded. This paper is concerned with introducing a general family of mixed Exponential regression models with varying dispersion which can efficiently capture the tail behaviour of losses. Our main achievement is that we present an Expectation-Maximization (EM)-type algorithm which can facilitate maximum likelihood (ML) estimation for our class of mixed Exponential models which allows for regression specifications for both the mean and dispersion parameters. Finally, a real data application based on motor insurance data is given to illustrate the versatility of the proposed EM-type algorithm.

scholarly journals Effect of air pollution on household insurance purchases. Evidence from China household finance survey data

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242282
Author(s):  
Wenxia Zhao
Keyword(s):  
Air Pollution ◽  
Health Insurance ◽  
Life Insurance ◽  
Poisson Regression ◽  
Regression Models ◽  
Household Finance ◽  
Economic Effects ◽  
Using Data ◽  
The Impact ◽  
Degree Of Heterogeneity

In recent years, the health and economic effects of air pollution have attracted considerable attention, and health and insurance services have been closely related to residents’ welfare. However, there are few studies on the influence of pollution on household purchases of insurance. Using data from the 2013 and 2015 China Household Finance Surveys, this study investigates the effect of air pollution on insurance purchases using Logit and Poisson regression models. It is found that air pollution significantly increases the probability of household insurance purchases and the level of premium expenditure, although the impact of air pollution on insurance purchases shows a degree of heterogeneity. Health insurance is more sensitive to air pollution than life insurance and other types of insurance. In areas where NO2 and O3 are the main types of pollutants, air pollution has a greater impact on household insurance purchases.

scholarly journals Anticipated help-seeking for cancer symptoms before and after the coronavirus pandemic: results from the Onco-barometer population survey in Spain

2021 ◽  
Author(s):  
Dafina Petrova ◽  
Marina Pollán ◽  
Miguel Rodriguez-Barranco ◽  
Dunia Garrido ◽  
Josep M. Borrás ◽  
...  
Keyword(s):  
Poisson Regression ◽  
Help Seeking ◽  
Regression Models ◽  
Population Survey ◽  
Waiting Times ◽  
Perceived Barriers ◽  
Older Individuals ◽  
Cancer Symptoms ◽  
Before And After ◽  
Diagnostic Delays

Abstract Background The patient interval—the time patients wait before consulting their physician after noticing cancer symptoms—contributes to diagnostic delays. We compared anticipated help-seeking times for cancer symptoms and perceived barriers to help-seeking before and after the coronavirus pandemic. Methods Two waves (pre-Coronavirus: February 2020, N = 3269; and post-Coronavirus: August 2020, N = 1500) of the Spanish Onco-barometer population survey were compared. The international ABC instrument was administered. Pre–post comparisons were performed using multiple logistic and Poisson regression models. Results There was a consistent and significant increase in anticipated times to help-seeking for 12 of 13 cancer symptoms, with the largest increases for breast changes (OR = 1.54, 95% CI 1.22–1–96) and unexplained bleeding (OR = 1.50, 1.26–1.79). Respondents were more likely to report barriers to help-seeking in the post wave, most notably worry about what the doctor may find (OR = 1.58, 1.35–1.84) and worry about wasting the doctor’s time (OR = 1.48, 1.25–1.74). Women and older individuals were the most affected. Conclusions Participants reported longer waiting times to help-seeking for cancer symptoms after the pandemic. There is an urgent need for public interventions encouraging people to consult their physicians with symptoms suggestive of cancer and counteracting the main barriers perceived during the pandemic situation.

scholarly journals Applications of Clustering with Mixed Type Data in Life Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Shuang Yin ◽  
Guojun Gan ◽  
Emiliano A. Valdez ◽  
Jeyaraj Vadiveloo
Keyword(s):  
Life Insurance ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Financial Statements ◽  
Life Insurers ◽  
Insurance Portfolio ◽  
Policy Information ◽  
Spatial Attributes ◽  
Type Data ◽  
Significant Mortality

Death benefits are generally the largest cash flow items that affect the financial statements of life insurers; some may still not have a systematic process to track and monitor death claims. In this article, we explore data clustering to examine and understand how actual death claims differ from what is expected—an early stage of developing a monitoring system crucial for risk management. We extended the k-prototype clustering algorithm to draw inferences from a life insurance dataset using only the insured’s characteristics and policy information without regard to known mortality. This clustering has the feature of efficiently handling categorical, numerical, and spatial attributes. Using gap statistics, the optimal clusters obtained from the algorithm are then used to compare actual to expected death claims experience of the life insurance portfolio. Our empirical data contained observations of approximately 1.14 million policies with a total insured amount of over 650 billion dollars. For this portfolio, the algorithm produced three natural clusters, with each cluster having lower actual to expected death claims but with differing variability. The analytical results provide management a process to identify policyholders’ attributes that dominate significant mortality deviations, and thereby enhance decision making for taking necessary actions.

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