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Expectation–maximization algorithm

About: Expectation–maximization algorithm is a research topic. Over the lifetime, 11823 publications have been published within this topic receiving 528693 citations. The topic is also known as: EM algorithm & Expectation Maximization.


Papers
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Journal ArticleDOI
TL;DR: In this paper, an iterative procedure for obtaining maximum likelihood estimates of the model parameters is developed, and the possibility of testing various hypotheses by means of likelihood ratio tests is discussed.
Abstract: After introducing some extensions of a recently proposed probabilistic vector model for representing paired comparisons choice data, an iterative procedure for obtaining maximum likelihood estimates of the model parameters is developed. The possibility of testing various hypotheses by means of likelihood ratio tests is discussed. Finally, the algorithm is applied to some existing data sets for illustrative purposes.

109 citations

Journal ArticleDOI
TL;DR: In this article, it was shown that the EM algorithm converges to the unique solution (F) of the self-consistency equations; the consistency of every iterate of every iteration of the EM a...
Abstract: A ranked set sample consists entirely of independently distributed order statistics and can occur naturally in many experimental settings, including problems in reliability. When each ranked set from which an order statistic is drawn is of the same size, and when the statistic of each fixed order is sampled the same number of times, the ranked set sample is said to be balanced. Stokes and Sager have shown that the edf F n of a balanced ranked set sample from the cdf F is an unbiased estimator of F and is more precise than the edf of a simple random sample of the same size. The nonparametric maximum likelihood estimator (MLE) F of F is studied in this article. Its existence and uniqueness is demonstrated, and a general numerical procedure is presented and is shown to converge to F. If the ranked set sample is balanced, it is shown that the EM algorithm, with F n as a seed, converges to the unique solution (F) of the problem's self-consistency equations; the consistency of every iterate of the EM a...

109 citations

Journal ArticleDOI
TL;DR: It is shown that the proposed EM has a lower computational complexity than the optimum maximum a posteriori estimator and yet incurs only an insignificant loss in performance.
Abstract: In this paper, the problem of joint carrier frequency offset (CFO) and channel estimation for OFDM systems over the fast time-varying frequency-selective channel is explored within the framework of the expectation-maximization (EM) algorithm and parametric channel model. Assuming that the path delays are known, a novel iterative pilot-aided algorithm for joint estimation of the multipath Rayleigh channel complex gains (CG) and the carrier frequency offset (CFO) is introduced. Each CG time-variation, within one OFDM symbol, is approximated by a basis expansion model (BEM) representation. An autoregressive (AR) model is built to statistically characterize the variations of the BEM coefficients across the OFDM blocks. In addition to the algorithm, the derivation of the hybrid Cramer-Rao bound (HCRB) for CFO and CGs estimation in our context of very high mobility is provided. We show that the proposed EM has a lower computational complexity than the optimum maximum a posteriori estimator and yet incurs only an insignificant loss in performance.

109 citations

Journal ArticleDOI
TL;DR: In this paper, an extension of the EM algorithm is presented for the problem of parameter estimation of continuous time, finite state or infinite state (diffusions) Markov processes observed via a noisy sensor.

108 citations

Journal ArticleDOI
TL;DR: In this paper, maximum likelihood estimators for the mean of a population, based on censored samples that can be transformed to normality, are calculated via the expectation-maximization algorithm.
Abstract: The reporting procedures for potentially toxic pollutants are complicated by the fact that concentrations are measured using small samples that include a number of observations lying below some detection limit. Furthermore, there is often a small number of high concentrations observed in combination with a substantial number of low concentrations. This results in small, nonnormally distributed censored samples. This article presents maximum likelihood estimators for the mean of a population, based on censored samples that can be transformed to normality. The method estimates the optimal power transformation in the Box-Cox family by searching the censored-data likelihood. Maximum likelihood estimators for the mean in the transformed scale are calculated via the expectation-maximization algorithm. Estimates for the mean in the original scale are functions of the estimated mean and variance in the transformed population. Confidence intervals are computed using the delta method and the nonparametric percentil...

108 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023114
2022245
2021438
2020410
2019484
2018519