Results 1 - 10 of 34,272

The Central Role of the Propensity Score in Observational Studies for Causal Effects.

by Paul R Rosenbaum , Donald B Rubin - Biometrika , 1983
"... SUMMARY The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Application ..."
Abstract - Cited by 2779 (26 self) - Add to MetaCart
SUMMARY The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates

Markov Random Field Models in Computer Vision

by S. Z. Li , 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
Abstract - Cited by 516 (18 self) - Add to MetaCart
. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

by Yongyue Zhang, Michael Brady, Stephen Smith - IEEE TRANSACTIONS ON MEDICAL. IMAGING , 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
Abstract - Cited by 639 (15 self) - Add to MetaCart
-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown

N: Meta-analysis in clinical trials

by Rebecca Dersimonian, Nan Laird - Controlled Clinical Trials , 1986
"... ABSTRACT: This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of hom ..."
Abstract - Cited by 1303 (0 self) - Add to MetaCart
relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies. KEY WORDS: random effects model, heterogeneity of treatment effects

High dimensional graphs and variable selection with the Lasso

by Nicolai Meinshausen, Peter Bühlmann - ANNALS OF STATISTICS , 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
Abstract - Cited by 736 (22 self) - Add to MetaCart
The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso

CONDENSATION -- conditional density propagation for visual tracking

by Michael Isard, Andrew Blake , 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to th ..."
Abstract - Cited by 1503 (12 self) - Add to MetaCart
to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion

Nonparametric estimation of average treatment effects under exogeneity: a review

by Guido W. Imbens - REVIEW OF ECONOMICS AND STATISTICS , 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
Abstract - Cited by 630 (25 self) - Add to MetaCart
as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has

Experimental Tests of the Endowment Effect and the Coase Theorem,”

by Daniel Kahneman , Jack L Knetsch , Richard H Thaler - Journal of Political Economy, , 1990
"... Contrary to theoretical expectations, measures of willingness to accept greatly exceed measures of willingness to pay. This paper reports several experiments that demonstrate that this "endowment effect" persists even in market settings with opportunities to learn. Consumption objects (e. ..."
Abstract - Cited by 677 (25 self) - Add to MetaCart
.g., coffee mugs) are randomly given to half the subjects in an experiment. Markets for the mugs are then conducted. The Coase theorem predicts that about half the mugs will trade, but observed volume is always significantly less. When markets for "induced-value" tokens are conducted, the predicted

How much should we trust differences-in-differences estimates?

by Marianne Bertrand, Esther Duflo, Sendhil Mullainathan , 2003
"... Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on femal ..."
Abstract - Cited by 828 (1 self) - Add to MetaCart
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data

Least angle regression

by Bradley Efron, Trevor Hastie, Iain Johnstone, Robert Tibshirani , 2004
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
Abstract - Cited by 1326 (37 self) - Add to MetaCart
The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope
Results 1 - 10 of 34,272