Topic
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.
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24 Jan 1997
TL;DR: Causality and Path Models: Embedding common factors in a Path Model, Measurement, Causation and Local Independence in Latent Variable Models, On the Identifiability of Nonparametric Structural Models, Estimating the Causal effects of Time Varying Endogeneous Treatments by G-Estimation of Structural Nested Models, Latent Variables- Model as Instruments, with Applications to Moment Structure Analysis as discussed by the authors.
Abstract: Causality and Path Models- Embedding Common factors in a Path Model- Measurement, Causation and Local Independence in Latent Variable Models- On the Identifiability of Nonparametric Structural Models- Estimating the Causal effects of Time Varying Endogeneous Treatments by G-Estimation of Structural Nested Models- Latent Variables- Model as Instruments, with Applications to Moment Structure Analysis- Bias and Mean Square Error of the Maximum Likelihood Estimators of the Parameters of the Intraclass Correlation Model- Latent Variable Growth Modeling with Multilevel Data- High-Dimensional Full-Information Item Factor Analysis- Dynamic Factor Models for the Analysis of Ordered Categorical Panel data- Model Fitting Procedures for Nonlinear Factor Analysis Using the Errors-in-Variables Parameterization- Multivariate Regression with Errors in Variables: Issues on Asymptotic Robustness- Non-Iterative fitting of the Direct Product Model for Multitrait-Multimethod Correlation Matrices- An EM Algorithm for ML Factor Analysis with Missing Data- Optimal Conditionally Unbiased Equivariant Factor Score Estimators
139 citations
TL;DR: In this paper, the role of martingale limit theory in the theory of maximum likelihood estimation for continuous-time stochastic processes is investigated and analogues of classical statistical concepts and quantities are also suggested.
Abstract: This paper is mainly concerned with the asymptotic theory of maximum likelihood estimation for continuous-time stochastic processes. The role of martingale limit theory in this theory is developed. Some analogues of classical statistical concepts and quantities are also suggested. Various examples that illustrate parts of the theory are worked through, producing new results in some cases. The role of diffusion approximations in estimation is also explored. MAXIMUM LIKELIHOOD ESTIMATION; CONTINUOUS-TIME STOCHASTIC PROCESSES; ASYMPTOTIC THEORY; MARTINGALE LIMIT THEORY; DIFFUSION APPROXIMATIONS
139 citations
06 Dec 2011
TL;DR: This work proposes a 'learning-based' approach, WiGEM, where the received signal strength is modeled as a Gaussian Mixture Model (GMM) where Expectation Maximization (EM) is used to learn the maximum likelihood estimates of the model parameters.
Abstract: We consider the problem of localizing a wireless client in an indoor environment based on the signal strength of its transmitted packets as received on stationary sniffers or access points. Several state-of-the-art indoor localization techniques have the drawback that they rely extensively on a labor-intensive 'training' phase that does not scale well. Use of unmodeled hardware with heterogeneous power levels further reduces the accuracy of these techniques.We propose a 'learning-based' approach, WiGEM, where the received signal strength is modeled as a Gaussian Mixture Model (GMM). Expectation Maximization (EM) is used to learn the maximum likelihood estimates of the model parameters. This approach enables us to localize a transmitting device based on the maximum a posteriori estimate. The key insight is to use the physics of wireless propagation, and exploit the signal strength constraints that exist for different transmit power levels. The learning approach not only avoids the labor-intensive training, but also makes the location estimates considerably robust in the face of heterogeneity and various time varying phenomena. We present evaluations on two different indoor testbeds with multiple WiFi devices. We demonstrate that WiGEM's accuracy is at par with or better than state-of-the-art techniques but without requiring any training.
139 citations
TL;DR: In this article, a table and method for computing the maximum likelihood solution which converges more rapidly than the standard probit method is presented, and a procedure is presented for obtaining more accurate initial approximations, and the problem of the bias of the estimation in small samples is considered.
Abstract: The estimation of the parameters of dosage response curves by the standard probit method is an iterative process beginning with approximations to the parameters and using one or more cycles of computations to “improve” these estimates until they converge. The present paper gives a table and method for computing the maximum likelihood solution which converges more rapidly than the standard probit method. A procedure is presented for obtaining more accurate initial approximations, and the problem of the bias of the maximum likelihood estimates in small samples is considered.
138 citations
TL;DR: In this paper, a conditional model for the covariate distribution was proposed to reduce the number of nuisance parameters for the distribution of the covariates in the E-step of the EM algorithm.
Abstract: SUMMARY Incomplete covariate data arise in many data sets. When the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990). This method requires the estimation of many nuisance parameters for the distribution of the covariates. Unfortunately, in data sets when the percentage of missing data is high, and the missing covariate patterns are highly non-monotone, the estimates of the nuisance parameters can lead to highly unstable estimates of the parameters of interest. We propose a conditional model for the covariate distribution that has several modelling advantages for the E-step and provides a reduction in the number of nuisance parameters, thus providing more stable estimates in finite samples. We present a clinical trials example with six covariates, five of which have some missing values.
138 citations