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336,805
An Assessment of Bayesian Inference in Nonparametric Logistic Regression
, 1996
"... A Monte Carlo study is performed to assess the properties of a Bayesian procedure for inference in nonparametric regression with a binary response variable. The logodds (logit) of the probability of the response is modeled as an integrated Wiener process. This leads to a generalized smoothing spl ..."
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Cited by 2 (2 self)
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spline as the posterior mode. Such priors have been used by many authors for nonparametric regression with Gaussian errors. In the logistic regression setup the posterior is analytically intractable. Monte Carlo approximation (specifically importance sampling) is used to evaluate posterior
Partially Improper Gaussian Priors for Nonparametric Logistic Regression
, 1995
"... A "partially improper" Gaussian prior is considered for Bayesian inference in logistic regression. This includes generalized smoothing spline priors that are used for nonparametric inference about the logit, and also priors that correspond to generalized random effect models. Necessary and ..."
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Cited by 2 (2 self)
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A "partially improper" Gaussian prior is considered for Bayesian inference in logistic regression. This includes generalized smoothing spline priors that are used for nonparametric inference about the logit, and also priors that correspond to generalized random effect models. Necessary
NONPARAMETRIC LOGISTIC REGRESSION: REPRODUCING KERNEL HILBERT SPACES
, 2003
"... Abstract. We study maximum penalized likelihood estimation for logistic regression type problems. The usual difficulties encountered when the logodds ratios may become large in absolute value are circumvented by imposing a priori bounds on the estimator, depending on the sample size (n) and smooth ..."
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Abstract. We study maximum penalized likelihood estimation for logistic regression type problems. The usual difficulties encountered when the logodds ratios may become large in absolute value are circumvented by imposing a priori bounds on the estimator, depending on the sample size (n
Printed in U.SA Estimating Relative Risk Functions in CaseControl Studies Using a Nonparametric Logistic Regression
"... The authors describe an approach to the analysis of casecontrol studies in which the exposure variables are continuous, i.e., quantitative variables, and one wishes neither to categorize levels of the exposure variable nor to assume a loglinear relation between level of exposure and disease risk. ..."
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than zero: Values less than one imply lower risk; the value one implies no risk, and values greater than one imply increased risk, when compared with a reference value. The authors describe how a nonparametric logistic regression can be used to estimate and display these RRFs. Using data from a
Additive Logistic Regression: a Statistical View of Boosting
 Annals of Statistics
, 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
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Cited by 1719 (25 self)
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be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multiclass generalizations based on multinomial likelihood are derived
Quantile Regression
 JOURNAL OF ECONOMIC PERSPECTIVES—VOLUME 15, NUMBER 4—FALL 2001—PAGES 143–156
, 2001
"... We say that a student scores at the fifth quantile of a standardized exam if he performs better than the proportion � of the reference group of students and worse than the proportion (1–�). Thus, half of students perform better than the median student and half perform worse. Similarly, the quartiles ..."
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Cited by 937 (10 self)
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, the quartiles divide the population into four segments with equal proportions of the reference population in each segment. The quintiles divide the population into five parts; the deciles into ten parts. The quantiles, or percentiles, or occasionally fractiles, refer to the general case. Quantile regression
Regression quantiles
 Econometrica
, 1978
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 870 (19 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Designadaptive nonparametric regression
 Journal of the American Statistical Association
, 1992
"... Visual realism is defined as the degree an image appears to people to be a photo rather than computer generated. Can computers predict human visual realism perception? ..."
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Cited by 427 (28 self)
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Visual realism is defined as the degree an image appears to people to be a photo rather than computer generated. Can computers predict human visual realism perception?
Nonparametric estimation of average treatment effects under exogeneity: a review
 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 ..."
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Cited by 597 (26 self)
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considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functionalform assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions
Results 1  10
of
336,805