We consider a particular maximum likelihood estimator (MLE) and a computationally-intensive Bayesian method for differentially private estimation of the linear mixed-effects model (LMM) with normal random errors. The LMM is important because it is used in small area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying ...

Maximum likelihood estimate (MLE) is a widely-used approach for PET image reconstruction. However, it has been shown that reconstructing emission tomography based on MLE without regularization would result in noise and edge artifacts. In the attempt to regularize the maximum likelihood estimate , in this paper, we propose a new and eecient method to incorporate the correlated but possibly incom...

In this paper, we deal with hypothesis testing for unknown parameters. A new method is proposed based on a combination of the computational approach test and maximum likelihood estimation (CAT-MLE), introduced by [7]. The CAT-MLE does not require any knowledge of the sampling distribution. It depends heavily on numerical computational and Monte-Carlo simulation study. To illustrate the methodol...

We apply the techniques of stochastic integration with respect to the fractional Brownian motion and the theory of regularity and supremum estimation for stochastic processes to study the maximum likelihood estimator (MLE) for the drift parameter of stochastic processes satisfying stochastic equations driven by fractional Brownian motion with any level of Hölder-regularity (any Hurst parameter)...

We apply the techniques of stochastic integration with respect to the fractional Brownian motion and the theory of regularity and supremum estimation for stochastic processes to study the maximum likelihood estimator (MLE) for the drift parameter of stochastic processes satisfying stochastic equations driven by fractional Brownian motion with any level of Holder-regularity (any Hurst parameter...

In this paper, we present a new maximum likelihood estimator (MLE) of carrier frequency offset for multicarrier signals in a frequency-selective Rayleigh fading channel. The proposed MLE is able to achieve wider estimation range of frequency offset with higher accuracy than previous blind approaches by exploiting the intrinsic structure of multicarrier signals. Simulations show that the estimat...

The neural network joint model of translation or NNJM (Devlin et al., 2014) combines source and target context to produce a powerful translation feature. However, its softmax layer necessitates a sum over the entire output vocabulary, which results in very slow maximum likelihood (MLE) training. This has led some groups to train using Noise Contrastive Estimation (NCE), which side-steps this su...

The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan (1994) is extended to account for the fact that observed equity prices may have been contaminated by trading noises. With the presence of trading noises, the likelihood function based on the observed equity prices can only be evaluated via some nonlinear filtering scheme. We devis...

In this article, we consider the problem of estimating the stress-strength reliability $Pr (X > Y)$ based on upper record values when $X$ and $Y$ are two independent but not identically distributed random variables from the power hazard rate distribution with common scale parameter $k$. When the parameter $k$ is known, the maximum likelihood estimator (MLE), the approximate Bayes estimator and ...

This study is the part of our ongoing research on using ultra-wide band (UWB) technology for navigation at the Ohio State University. Our tests have indicated that the UWB two-way time-of-flight ranges under indoor circumstances follow a Gaussian mixture distribution that may be caused by the incompleteness of the functional model. In this case, to adjust the UWB network from the observed range...