MOTIVATION Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples. RESULTS An ext...

Quantifying uncertainty for parameter estimates obtained from matched-field geoacoustic inversions using a Bayesian approach requires estimation of the uncertainties in the data due to ambient noise as well as modeling errors. In this study, the variance parameter of the Gaussian error model, hereafter called error variance, is assumed to describe the data uncertainty. In practice, this paramet...

This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE) with the expectation-maximization algorithm in Con...

An important aspect of estimation theory is characterizing the best achievable performance in a given estimation problem, as well as determining estimators that achieve the optimal performance. The traditional Cramér-Rao type bounds provide benchmarks on the variance of any estimator of a deterministic parameter vector under suitable regularity conditions, while requiring a-priori specification...

Unbiased estimation is an efficient alternative to optimal estimation when the noise statistics are not fully known and/or the model undergoes temporary uncertainties. In this paper, we investigate the effect of embedded unbiasedness (EU) on optimal finite impulse response (OFIR) filtering estimates of linear discrete time-invariant statespace models. A new OFIR-EU filter is derived by minimizi...

Abstract: This paper deals with the problem of estimating a density based on observations which are contaminated by a normally distributed error whose variance is unknown. In the case of a completely unknown error variance, the impossibility of a uniformly consistent estimation is shown; however, a semi-uniformly consistent estimator is constructed under nonparametric smoothness conditions on t...

This paper considers an Type I Generalized Half Logistic Distribution. We discussed the scale (σ) and shape (θ) parameters estimation using the median ranks method (Benard‟s approximation). Rama Krishna(2008) 1 studied the Type I Generalized Half Logistic Distribution scale (σ) and shape (θ) parameters estimation using the Least Square Method in two step estimation method. Also we computed Aver...

This paper deals with a sensor scheduling considering estimation error variance and communication energy in sensor networked feedback system. We propose an decentralized estimation algorithm with unknown inputs in each sensor node. Most existing works deal with the sensor network systems as sensing systems and it is difficult to apply them to the real physical feedback control systems Then some...