The beta regression model (BRM) is used when the dependent variable may take continuous values and be bounded in interval (0, 1), such as rates, proportions, percentages fractions. Generally, parameters of BRM are estimated by method maximum likelihood estimation (MLE). However, MLE does not offer accurate reliable estimates explanatory variables correlated. To solve this problem, ridge Liu est...

We provide in this paper asymptotic theory for the multivariate GARCH(p, q) process. Strong consistency of the quasi-maximum likelihood estimator (MLE) is established by appealing to conditions given in Jeantheau [19] in conjunction with a result given by Boussama [9] concerning the existence of a stationary and ergodic solution to the multivariate GARCH(p, q) process. We prove asymptotic norma...

Estimating the unconstrained mean and covariance matrix is a popular topic in statistics. However, estimation of parameters Np(?,?) under joint constraints such as ?? = ? has not received much attention. It can be viewed multivariate counterpart classical problem N(????,????2) distribution. In addition to usual inference challenges non-linear among (curved exponential family), one deal with bas...

In this paper, we present an interpretation of the Maximum Likelihood Estimator (MLE) and the Delogne-Kåsa Estimator (DKE) for circle-parameter estimation via convolution. Under a certain model for theoretical images, this convolution is an exact description of the MLE. We use our convolution based MLE approach to find good starting estimates for the parameters of a circle, that is, the centre ...

SAR change detection performance using coherent change detection (CCD) and non-coherent change detection (NCCD) algorithms is demonstrated using high-resolution synthetic aperture radar (SAR) imagery gathered by the General Dynamics Data Collection System. CCD performance comparisons using phaseonly imagery versus complex (amplitude and phase) imagery are also presented. A new image quality met...

As you learned in previous courses, if we have a statistical model we can often estimate unknown “parameters” by the maximum likelihood principle. Suppose we have independent, but not necessarily identically distributed, data. Namely, we model the data {Yi}i=1 as independent random variables with densities (with respect to a common dominating measure) given by pi(·; θ), where θ is an unknown “p...

We study optimal and suboptimal decentralized estimators in wireless sensor networks over orthogonal multipleaccess fading channels in this paper. Considering multiple-bit quantization for digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case o...

The Lindley distribution may serve as a useful reliability model. Applications of this are presented in statistical literature. In article, goodness fit tests for the based on empirical function (EDF) considered. order to compute test statistics, we use maximum likelihood estimate (MLE) suggested by Ghitany et al. (2008), which is simple explicit estimator. Critical points proposed statistics o...

We consider the problem of delay estimation by observations solutions several SDEs. It is known that MLEs for these models are consistent and asymptotically normal, but likelihood ratio functions not differentiable w.r.t. parameter, therefore numerical calculation encounter certain difficulties. propose One-step Two-step MLEs, whose has no such problems provide an estimator equivalent to MLE. T...