scholarly journals Distributions in Life Insurance

1990 ◽  
Vol 20 (1) ◽  
pp. 81-92 ◽  
Author(s):  
Jan Dhaene
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Life Insurance ◽  
Wide Class ◽  
Stochastic Theory ◽  
Stochastic Approach ◽  
Higher Order ◽  
Loss Functions ◽  
Higher Order Moments

AbstractIn most textbooks and papers that deal with the stochastic theory of life contingencies, the stochastic approach is restricted to the computation of expectations and higher order moments. For a wide class of insurances on a single life, we derive the distribution and the probability density function of the benefit and the loss functions. Both the continuous and the discrete case are considered.

scholarly journals Bayesian Estimation of the Parameter of the p-Dimensional Size-Biased Rayleigh Distribution

2017 ◽  
Vol 56 (1) ◽  
pp. 88-91
Author(s):  
Arun Kumar Rao ◽  
Himanshu Pandey ◽  
Kusum Lata Singh
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Bayesian Estimation ◽  
Density Function ◽  
Rayleigh Distribution ◽  
Survival Model ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Hazard Functions ◽  
Squared Error

In this paper, we have derived the probability density function of the size-biased p-dimensional Rayleigh distribution and studied its properties. Its suitability as a survival model has been discussed by obtaining its survival and hazard functions. We also discussed Bayesian estimation of the parameter of the size-biased p-dimensional Rayleigh distribution. Bayes estimators have been obtained by taking quasi-prior. The loss functions used are squared error and precautionary.

scholarly journals Concentration Fluctuations and Odor Dispersion in Lagrangian Models

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 27 ◽  
Author(s):  
Enrico Ferrero ◽  
Alon Manor ◽  
Luca Mortarini ◽  
Dietmar Oettl
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Stochastic Models ◽  
Scientific Community ◽  
Higher Order ◽  
Concentration Fluctuations ◽  
Lagrangian Stochastic Models ◽  
Odor Annoyance ◽  
Odor Dispersion

In this paper, a review of the Lagrangian stochastic models developed in the last decades for the simulation of the concentration–fluctuation dispersion is presented. The main approaches available in the literature are described and their ability in reproducing the higher order moments of the probability density function is discussed. Then, the Lagrangian approaches for evaluating of the odor annoyance are presented. It is worth to notice that, while Lagrangian stochastic models for mean concentrations are well-known and their ability in correctly reproducing the observation is well assessed, concerning concentration fluctuations the approaches are often new and unknown for most of the scientific community.

Sign in / Sign up

Export Citation Format

Share Document