AIM To investigate the differences in the pharmacokinetics of Prograf and the prolonged release formulation Advagraf and to develop a Bayesian estimator to estimate tacrolimus inter-dose area under the curve (AUC) in renal transplant patients receiving either Prograf or Advagraf. METHODS Tacrolimus concentration-time profiles were collected, in adult renal transplant recipients, at weeks 1 an...

Learning automata (LAs) play a crucial role as a reinforcement scheme to solve engineering problems even in nonstationary environments. However, their low rate of convergence has considered as the main drawback in the works of literature. Estimator algorithms have suggested as a successful attempt to alleviate the inconvenience. The aim of this paper is to discuss various types of estimator alg...

When observing data x1, . . . , xt modelled by a probabilistic distribution pθ(x), the maximum likelihood (ML) estimator θML = argmaxθ ∑︀t i=1 ln pθ(xi) cannot, in general, safely be used to predict xt+1. For instance, for a Bernoulli process, if only “tails” have been observed so far, the probability of “heads” is estimated to 0. Laplace’s famous “add-one” rule of succession (e.g., [Grü07]) re...

Parameter estimation for the K distribution is an essential part of the statistical analysis of non-Rayleigh sonar reverberation or clutter for performance prediction, estimation of scattering properties, and for use in signal and information processing algorithms. Computational issues associated with maximum likelihood (ML) estimation techniques for K-distribution parameters often force the us...

Respondent-driven sampling is a network-based technique to collect information and make estimation about behavior and composition of social groups in hidden population. The non-randomly selected samples prohibit the use of the sample mean as a statistically valid estimator. Researchers have proposed several asymptotically unbiased estimators, but many fail to realize that the high variance of t...

The asymptotic parameter estimation is investigated for a class of linear stochastic systems with unknown parameter θ : dXt θα t β t Xt dt σ t dWt. Continuous-time Kalman-Bucy linear filtering theory is first used to estimate the unknown parameter θ based on Bayesian analysis. Then, some sufficient conditions on coefficients are given to analyze the asymptotic convergence of the estimator. Fina...