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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19 Nov 2021
TL;DR: This book explains how to use the tried and tested Imputation Variance Estimation method to estimate the likelihood of a particular event happening in the future.
Abstract: Introduction Introduction Outline How to Use This Book Likelihood-Based Approach Introduction Observed Likelihood Mean Score Approach Observed Information Computation Introduction Factoring Likelihood Approach EM Algorithm Monte Carlo Computation Monte Carlo EM Data Augmentation Imputation Introduction Basic Theory for Imputation Variance Estimation after Imputation Replication Variance Estimation Multiple Imputation Fractional Imputation Propensity Scoring Approach Introduction Regression Weighting Method Propensity Score Method Optimal Estimation Doubly Robust Method Empirical Likelihood Method Nonparametric Method Nonignorable Missing Data Nonresponse Instrument Conditional Likelihood Approach Generalized Method of Moments (GMM) Approach Pseudo Likelihood Approach Exponential Tilting (ET) Model Latent Variable Approach Callbacks Capture-Recapture (CR) Experiment Longitudinal and Clustered Data Ignorable Missing Data Nonignorable Monotone Missing Data Past-Value-Dependent Missing Data Random-Effect-Dependent Missing Data Application to Survey Sampling Introduction Calibration Estimation Propensity Score Weighting Method Fractional Imputation Fractional Hot Deck Imputation Imputation for Two-Phase Sampling Synthetic Imputation Statistical Matching Introduction Instrumental Variable Approach Measurement Error Models Causal Inference Bibliography Index
133 citations
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TL;DR: In this article, the authors derived an estimator for a distribution function possessing an increasing (decreasing) failure rate and also obtained corresponding estimators for the density and the failure rate.
Abstract: : Using the idea of maximum likelihood, we derive an estimator for a distribution function possessing an increasing (decreasing) failure rate and also obtain corresponding estimators for the density and the failure rate. We show that these estimators are consistent.
133 citations
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TL;DR: In this paper, a transposable regularized covariance model is proposed to estimate the mean and non-singular covariance matrices of high-dimensional data in the form of a matrix, where rows and columns each have a separate mean vector and covariance matrix.
Abstract: Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal, in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility.
133 citations
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TL;DR: It is demonstrated that the degree of over-fitting is reduced with a weighting scheme that depends on the signal-to-noise ratio in the data, which improves the accuracy of resolution measurement by the commonly used Fourier shell correlation.
133 citations