Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

Bayesian estimation methods form a dynamic branch of statistical inference, utilising Bayes’ theorem to update probabilities in light of new evidence. This framework combines prior knowledge with ...

Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 180, No. 4 (OCTOBER 2017), pp. 1191-1209 (19 pages) Area level models, such as the Fay–Herriot model, aim to improve ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 46, No. 3 (September/septembre 2018), pp. 399-415 (17 pages) For sparse and high-dimensional data analysis, a valid ...

We suggest a new method for integrating volatility information for estimating the value-at-risk and conditional value-at-risk of a portfolio. This new method is developed from the perspective of ...

Discover how credibility theory helps actuaries use historical data to estimate risks and set insurance premiums; learn how ...

The covariance matrix of asset returns is the key input for many problems in finance and economics. This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from ...

The empirical Bayes estimation is based on Bayes statistics. It integrates a correlation method with statistical estimations to integrate prior knowledge or beliefs about the parameters of the dataset ...

Before the outbreak of coronavirus, the seasonal flu was one of the most dangerous infectious diseases, but a lot of people have trouble telling the difference between a flu and a cold by their ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...