We consider a one-dimensional stochastic differential equation that is observed on fine grid of equally spaced time points. A novel approach for approximating the transition density presented, which based an Itô-Taylor expansion sample path, combined with application so-called ϵ -expansion. The resulting approximation economical respect to number terms needed achieve given level accuracy in hig...

This article presents the analysis of the Type-II hybrid progressively censored data when the lifetime distributions of the items follow Type-II generalized logistic distribution. Maximum likelihood estimators (MLEs) are investigated for estimating the location and scale parameters. It is observed that the MLEs can not be obtained in explicit forms. We provide the approximate maximum likelihood...

BACKGROUND To evaluate clinicopathological features, radiotherapeutic parameters, and their associations with responses to radiotherapy (RT) in patients with myeloid sarcoma (MS). METHODS We reviewed 20 patients receiving RT for MS lesions (in 43 RT courses) and analyzed the patients' clinicopathologic features and radiotherapeutic parameters, and their associations with complete responses (C...

A computationally e cient method for structured covariance matrix estimation is presented. The proposed method provides an Asymptotic (for large samples) Maximum Likelihood estimate of a structured covariance matrix and is referred to as AML. A closed-form formula for estimating Hermitian Toeplitz covariance matrices is derived which makes AML computationally much simpler than most existing Her...

Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees (Felsenstein, 1981). Finding optimal ML trees appears to be a very hard computational task, but for tractable cases, ML is the method of choice. In particular, algorithms and heuristics for ML take longer to run than algorithms and heuristics for the second major character based criterion, m...

Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees (Felsenstein, 1981). Finding optimal ML trees appears to be a very hard computational task, but for tractable cases, ML is the method of choice. In particular, algorithms and heuristics for ML take longer to run than algorithms and heuristics for the second major character based criterion, m...