Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...
Maximum likelihood estimation of the parameters of a statistical model involves maximizing the likelihood or, equivalently, the log likelihood with respect to the parameters. The parameter values at ...
The rapid accumulation of genome sequence data has made phylogenetics an indispensable tool to various branches of biology. However, it has also posed considerable statistical and computational ...
Identify characteristics of “good” estimators and be able to compare competing estimators. Construct sound estimators using the techniques of maximum likelihood and method of moments estimation.
The following data are taken from Lawless (1982, p.193) and represent the number of days it took rats painted with a carcinogen to develop carcinoma. The last 2 observations are censored data from a ...
Huque & Katti's (1976) three conditions for weak consistency of the maximum conditional likelihood estimate are discussed in connection with a two parameter exponential family of distributions. It is ...
In operational risk measurement, the estimation of severity distribution parameters is the main driver of capital estimates, yet this remains a nontrivial challenge for many reasons. Maximum ...
In the process of loan pricing, stress testing, capital allocation, modeling of probability of default (PD) term structure and International Financial Reporting Standard 9 expected credit loss ...
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