We use the theory developed in FR to derive efficient algorithms for extended maximum likelihood estimation in log-linear models under Poisson and product multinomial schemes. The restriction to these sampling schemes is motivated by a variety of reasons. First, these schemes encode sampling constraints that arise most frequently in practice. In particular, these are the sampling schemes practi...

One of the main problems in carrying out psychoacoustic experiments is the time required to measure a single threshold. In this study we compare the accuracy of threshold estimation in a 2I2AFC task for detecting a 2kHz tone in either a broadband noise or a notched-noise. Tone thresholds were estimated in three normal-hearing listeners using either a Levitt procedure to track 79% correct, or a ...

When the data are sparse, optimization of hyperparameters kernel in Gaussian process regression by commonly used maximum likelihood estimation (MLE) criterion often leads to overfitting. We show that choosing (in this case, length parameter and regularization parameter) based on a completeness basis corresponding linear problem is superior MLE. facilitated use high-dimensional model representat...

This paper is devoted to the controlled drift estimation of mixed fractional Ornstein-Uhlenbeck process. We will consider two models: one optimal input where we find function which maximize Fisher information for unknown parameter and other with a constant as function. Large sample asymptotical properties Maximum Likelihood Estimator (MLE) deduced using Laplace transform computations or Cameron...

Many population genetics tools employ composite likelihoods, because fully modeling genomic linkage is challenging. But traditional approaches to estimating parameter uncertainties and performing model selection require full likelihoods, so these tools have relied on computationally expensive maximum-likelihood estimation (MLE) on bootstrapped data. Here, we demonstrate that statistical theory ...

We consider some inference problems concerning the drift parameters vector of diffusion process. Namely, we consider the case where the parameters vector is suspected to satisfy certain restriction. Under such a design and imprecise prior information, we propose Stein-rule (or shrinkage) estimators which improves over the performance of the classical maximum likelihood estimator (MLE). By using...

Sometimes the maximum likelihood estimation procedure for the probit model fails. There may be two reasons: the maximum likelihood estimate (MLE) just does not exist or computer overflow error occurs during the computation of the cumulative distribution function (cdf). For example, the approximation explosive effect due to an inaccurate computation of the cdf for a large value of the argument o...

Abstract: This paper describes our approach of applying clustering techniques in the detection of UXOs (Unexploded Ordances). The clustering algorithms help us to integrate information from various sensors. Because of the specific characteristic of our application, a new MLE (maximum likelihood estimation) method is designed. The MLE is integrated into the clustering algorithm whose inputs are ...

In this paper, we study, in some new ways, the estimation of unimodal densities. Several methods for estimating unimodal densities are proposed: plug-in MLE, pregrouping techniques, linear spline MLE. Based on the maximum likelihood method, an automatic procedure for estimating a unimodal density as well as its mode is proposed. We also give asymptotic theory for the proposed estimators. An imp...