Constrained Bayesian estimates overcome the over shrinkness toward the mean which usual Bayes and em-pirical Bayes estimates produce by matching first and second empirical moments; subsequently, a constrained Bayes estimate is recommended to use in case the research objective is to produce a histogram of the estimates considering the location and dispersion. The well-known squared error loss function exclusively emphasizes the precision of estimation and may lead to biased estimators. Thus, the balanced loss function is suggested to reflect both goodness of fit and precision of estimation. In insurance pricing, the accurate location estimates of risk and also dispersion estimates of each risk group should be considered under proper loss fun...
A bound is given for the Bayes risk of an estimator under truncated squared error loss. The bound de...
Many premium calculating problems in actuarial science consider the number of claims, denoted as K, ...
The derivation of loss distribution from insurance data is a very interesting research topic but at ...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
Abstract. Using an approach based on Bayesian inference, we propose a method to compute an estimate ...
The paper compares the performance of some widely used Bayesian estimators such as Bayes estimator, ...
The Minimum Risk Equivariant (MRE), estimator is a widely used estimator which has several well-know...
Constrained Bayes methodology represents an alternative to the posterior mean (empirical Bayes) meth...
This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesi...
Loss distributions have a number of uses in the pricing and reserving of casualty insurance. Many au...
It is often of interest to find the maximum or near maxima among a set of vector-valued parameters i...
Abstract. The use of the Pareto distribution as a model for various socio-economic phenomena dates b...
This paper is intended as a guide to statistical inference for loss distributions. There are three b...
Insurance risks data typically exhibit skewed behaviour. In this paper, we propose a Bayesian approa...
A bound is given for the Bayes risk of an estimator under truncated squared error loss. The bound de...
Many premium calculating problems in actuarial science consider the number of claims, denoted as K, ...
The derivation of loss distribution from insurance data is a very interesting research topic but at ...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
Abstract. Using an approach based on Bayesian inference, we propose a method to compute an estimate ...
The paper compares the performance of some widely used Bayesian estimators such as Bayes estimator, ...
The Minimum Risk Equivariant (MRE), estimator is a widely used estimator which has several well-know...
Constrained Bayes methodology represents an alternative to the posterior mean (empirical Bayes) meth...
This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesi...
Loss distributions have a number of uses in the pricing and reserving of casualty insurance. Many au...
It is often of interest to find the maximum or near maxima among a set of vector-valued parameters i...
Abstract. The use of the Pareto distribution as a model for various socio-economic phenomena dates b...
This paper is intended as a guide to statistical inference for loss distributions. There are three b...
Insurance risks data typically exhibit skewed behaviour. In this paper, we propose a Bayesian approa...
A bound is given for the Bayes risk of an estimator under truncated squared error loss. The bound de...
Many premium calculating problems in actuarial science consider the number of claims, denoted as K, ...
The derivation of loss distribution from insurance data is a very interesting research topic but at ...