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284
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
, 2004
"... This paper presents a new approach to estimation and inference in panel data models with a multifactor error structure where the unobserved common factors are (possibly) correlated with exogenously given individualspecific regressors, and the factor loadings differ over the cross section units. The ..."
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Cited by 383 (44 self)
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. The estimation procedure has the advantage that it can be computed by OLS applied to an auxiliary regression where the observed regressors are augmented by (weighted) cross sectional averages of the dependent variable and the individual specific regressors. Two different but related problems are addressed: one
Logistic Regression, AdaBoost and Bregman Distances
, 2000
"... We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt al ..."
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Cited by 259 (45 self)
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We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt
Logistic regression with an auxiliary data source
 Proceedings of the TwentySecond International Conference on Machine Learning
, 2005
"... To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper we propose a method to relax this requirement in the context of logistic regression. Assuming Dp and Da are two sets of examples ..."
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Cited by 42 (1 self)
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To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper we propose a method to relax this requirement in the context of logistic regression. Assuming Dp and Da are two sets of examples
Fast Computation of Auxiliary Quantities in Local Polynomial Regression
, 1995
"... We investigate the extension of binning methodology to fast computation of several auxiliary quantities that arise in local polynomial smoothing. Examples include degrees of freedom measures, crossvalidation functions, variance estimates and exact measures of error. It is shown that the computation ..."
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Cited by 5 (0 self)
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We investigate the extension of binning methodology to fast computation of several auxiliary quantities that arise in local polynomial smoothing. Examples include degrees of freedom measures, crossvalidation functions, variance estimates and exact measures of error. It is shown
Bayesian Auxiliary Variable Models for Binary and Multinomial Regression
"... Abstract. In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of ..."
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Abstract. In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance
Failure time regression with continuous informative auxiliary covariates
"... In this paper we use Cox’s regression model to fit failure time data with continuous informative auxiliary variables in the presence of a validation subsample. We first estimate the induced relative risk function by kernel smoothing based on the validation subsample, and then improve the estimation ..."
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In this paper we use Cox’s regression model to fit failure time data with continuous informative auxiliary variables in the presence of a validation subsample. We first estimate the induced relative risk function by kernel smoothing based on the validation subsample, and then improve the estimation
Nonlinear LowDimensional Regression Using Auxiliary Coordinates
"... When doing regression with inputs and outputsthatarehighdimensional,itoftenmakes sense to reduce the dimensionality of the inputs before mapping to the outputs. Much work in statistics and machine learning, such as reducedrank regression, sliced inverse regression and their variants, has focused o ..."
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Cited by 2 (1 self)
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When doing regression with inputs and outputsthatarehighdimensional,itoftenmakes sense to reduce the dimensionality of the inputs before mapping to the outputs. Much work in statistics and machine learning, such as reducedrank regression, sliced inverse regression and their variants, has focused
CS535D Project: Bayesian Logistic Regression through Auxiliary Variables
"... This project deals with the estimation of Logistic Regression parameters. We first review the binary logistic regression model and the multinomial extension, including standard MAP parameter estimation with a Gaussian prior. We then turn to the case of Bayesian Logistic Regression under this same pr ..."
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prior. We review the cannonical approach of performing Bayesian Probit Regression through auxiliary variables, and extensions of this technique to Bayesian Logistic Regression and Bayesian Multinomial Regression. We then turn to the task of feature selection, outlining a transdimensional MCMC approach
Wild Bootstrapping in Finite Populations With Auxiliary
, 1995
"... Consider a finite population u, which can be viewed as a realization of a superpopulation model. A simple ratio model (linear regression, without intercept) with heteroscedastic errors is supposed to have generated u. A random sample is drawn without replacement from u. In this setup a two stage wil ..."
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Cited by 3 (0 self)
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Consider a finite population u, which can be viewed as a realization of a superpopulation model. A simple ratio model (linear regression, without intercept) with heteroscedastic errors is supposed to have generated u. A random sample is drawn without replacement from u. In this setup a two stage
Results 1  10
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284