Search Results - subject:"EM Algorithm"

Bulut, Y. Murat; Arslan, Olcay - In: Journal of Multivariate Analysis 137 (2015) C, pp. 173-178

its parameters using EM algorithm. We provide an iteratively reweighting algorithm to compute the ML estimates. Also, we …

Persistent link: https://www.econbiz.de/10011263458

Adaptive estimation for varying coefficient models

Chen, Yixin; Wang, Qin; Yao, Weixin - In: Journal of Multivariate Analysis 137 (2015) C, pp. 17-31

squares based methods, the proposed approach can adapt to different error distributions. An efficient EM algorithm is provided …

Persistent link: https://www.econbiz.de/10011263464

Inference in a structural heteroskedastic calibration model

Castro, Mário; Galea, Manuel - In: Statistical Papers 56 (2015) 2, pp. 479-494

compared. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic …

Persistent link: https://www.econbiz.de/10011241318

On a new absolutely continuous bivariate generalized exponential distribution

Mirhosseini, S.; Amini, M.; Kundu, D.; Dolati, A. - In: Statistical Methods and Applications 24 (2015) 1, pp. 61-83

simultaneously three non-linear equations. We propose to use EM algorithm to compute the maximum likelihood estimators, which can be …

Persistent link: https://www.econbiz.de/10011241334

Tests for statistical significance of a treatment effect in the presence of hidden sub-populations

Karmakar, B.; Dhara, K.; Dey, K.; Basu, A.; Ghosh, A. - In: Statistical Methods and Applications 24 (2015) 1, pp. 97-119

For testing the statistical significance of a treatment effect, we often compare between two parts of a population; one is exposed to the treatment, and the other is not exposed to it. Standard parametric or nonparametric two-sample tests are commonly used for this comparison. But direct...

Persistent link: https://www.econbiz.de/10011241336

Estimation in skew-normal linear mixed measurement error models

Kheradmandi, Ameneh; Rasekh, Abdolrahman - In: Journal of Multivariate Analysis 136 (2015) C, pp. 1-11

In this paper we define a class of skew-normal linear mixed measurement error models. This class provides a useful generalization of normal linear mixed models with measurement error in fixed effects variables. It is assumed that the random effects, model errors and measurement errors follow a...

Persistent link: https://www.econbiz.de/10011208471

A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits

Bernhardt, Paul W.; Zhang, Daowen; Wang, Huixia Judy - In: Computational Statistics & Data Analysis 85 (2015) C, pp. 37-53

multiple longitudinal covariates subject to detection limits. A fast, approximate EM algorithm is developed that reduces the …. Numerical studies demonstrate that the proposed approximate EM algorithm leads to satisfactory parameter and variance estimates … in situations with and without censoring on the longitudinal covariates. The approximate EM algorithm is applied to …

Persistent link: https://www.econbiz.de/10011191014

An Akaike information criterion for multiple event mixture cure models

Dirick, Lore; Claeskens, Gerda; Baesens, Bart - In: European Journal of Operational Research 241 (2015) 2, pp. 449-457

We derive the proper form of the Akaike information criterion for variable selection for mixture cure models, which are often fit via the expectation–maximization algorithm. Separate covariate sets may be used in the mixture components. The selection criteria are applicable to survival models...

Persistent link: https://www.econbiz.de/10011077633

Convergence of the EM algorithm for continuous mixing distributions

Chung, Yeojin; Lindsay, Bruce G. - In: Statistics & Probability Letters 96 (2015) C, pp. 190-195

Beyond the expectation–maximization (EM) algorithm for vector parameters, the EM for an unknown distribution function …

Persistent link: https://www.econbiz.de/10011115951

Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm

Wang, Naichen; Wang, Lianming; McMahan, Christopher S. - In: Computational Statistics & Data Analysis 83 (2015) C, pp. 140-150

challenging using traditional techniques. Consequently, in this paper we develop a novel expectation–maximization (EM) algorithm … finite number of parameters while simultaneously allowing for modeling flexibility. The derivation of the proposed EM … algorithm relies on a three-stage data augmentation involving Poisson latent variables. The resulting algorithm is easy to …

Persistent link: https://www.econbiz.de/10011117690