In this paper, we derive the recurrence relations for the moments of function of single and two order statistics from Lindley distribution. We also consider the maximum likelihood estimation (MLE) of the parameter of the distribution based on multiply type-II censoring. The maximum likelihood estimator is comupted numerically because it does not have an explicit form for the parameter. Then, a ...

Maximum Likelihood Estimation (MLE) is a widely used statistical estimation method. In this lecture, we will study its properties: efficiency, consistency and asymptotic normality. MLE is a method for estimating parameters of a statistical model. Given the distribution of a statistical model f(y ; θ) with unkown deterministic parameter θ, MLE is to estimate the parameter θ by maximizing the pro...

Continuing increases in computing power and availability mean that many maximum likelihood estimation (MLE) problems previously thought intractable or too computationally difficult can now be tackled numerically. However, ML parameter estimation for distributions whose only analytical expression is as quantile functions has received little attention. Numerical MLE procedures for parameters of n...

Many machine learning tasks can be formulated in terms of predicting structured outputs. In frameworks such as the structured support vector machine (SVM-Struct) and the structured perceptron, discriminative functions are learned by iteratively applying efficient maximum a posteriori (MAP) decoding. However, maximum likelihood estimation (MLE) of probabilistic models over these same structured ...

The purpose of the work described in this paper is to investigate the use of autoregressive (AR) model by using maximum likelihood estimation (MLE) also interpretation and performance of this method to extract classifiable features from human electroencephalogram (EEG) by using Artificial Neural Networks (ANNs). ANNs are evaluated for accuracy, specificity, and sensitivity on classification of ...

MAXIMUM LIKELIHOOD ESTIMATION OF EXPONENTIALS CONTAINED IN SIGNAL-DEPENDENT NOISES Publication No._______ Steven John Apollo, Ph.D. The University of Texas at Arlington, 1991 Supervising Professor: Michael T. Manry The problem of maximum likelihood estimation (MLE) of exponentials in signal-dependent noise is addressed as well as a methodology to attack the problem. Estimation of exponentials h...

A procedure for solving exact maximum likelihood estimation (MLE) is proposed for non-invertible non-Gaussian MA processes. By augmenting certain latent variables, the exact likelihood of all relevant innovations can be expressed explicitly according to a set of recursions (Breidt and Hsu, 2005). Then, the exact MLE is solved numerically by EM algorithm. Two alternative estimators are proposed ...

In this paper, we report on the impact that slight changes in question format have on student response to onedimensional vector subtraction tasks. We use Maximum Likelihood Estimation (MLE) analysis to analyze students’ responses on six very similar questions which vary in context (physics or mathematics), vector alignment (both pointing to the right or opposed), and operation (left-right subtr...

We introduce a maximum Lq-likelihood estimation (MLqE) of mixture models using our proposed expectation maximization (EM) algorithm, namely the EM algorithm with Lq-likelihood (EM-Lq). Properties of the MLqE obtained from the proposed EMLq are studied through simulated mixture model data. Compared with the maximum likelihood estimation (MLE) which is obtained from the EM algorithm, the MLqE pro...

In optical coherence tomography (OCT), unbiased and low variance Doppler frequency estimators are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible. However, it is known that the Kasai autocorrelation estimator, unexpectedly, performs worse as acquisition rates increase. Here we suggest that maximum likelihood estimators (...