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584
BAYESIAN ESTIMATION IN SINGLEINDEX MODELS
"... Abstract: Singleindex models offer a flexible semiparametric regression framework for highdimensional predictors. Bayesian methods have never been proposed for such models. We develop a Bayesian approach incorporating some frequentist methods: Bsplines approximate the link function, the prior on ..."
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Cited by 9 (0 self)
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on the index vector is Fishervon Mises, and regularization with generalized cross validation is adopted to avoid overfitting the link function. A random walk Metropolis algorithm is used to sample from the posterior. Simulation results indicate that our procedure provides some improvement over the best
Using Twitter to Estimate and Predict the Trends and Opinions
"... The most common and conventional way to collect people’s opinions has always been random sampling and asking several survey questions by phone. For instance, if a news media is interested in how popular Obama is ..."
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The most common and conventional way to collect people’s opinions has always been random sampling and asking several survey questions by phone. For instance, if a news media is interested in how popular Obama is
The Complexity of Estimating Rényi Entropy
"... It was recently shown that estimating the Shannon entropy H(p) of a discrete ksymbol distribution p requires Θ(k / log k) samples, a number that grows nearlinearly in the support size. In many applications H(p) can be replaced by the more general Rényi entropy of order α, Hα(p). We determine th ..."
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reconstruction, closeness testing, and other applications, requires only Θ( k) samples. The estimators achieving these bounds are simple and run in time linear in the number of samples. 1
The Complexity of Estimating Rényi Entropy
"... It was recently shown that estimating the Shannon entropy H(p) of a discrete ksymbol distribution p requires Θ(k / log k) samples, a number that grows nearlinearly in the support size. In many applications H(p) can be replaced by the more general Rényi entropy of order α, Hα(p). We determine th ..."
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reconstruction, closeness testing, and other applications, requires only Θ( k) samples. The estimators achieving these bounds are simple and run in time linear in the number of samples. 1
Kernel Mean Estimation and Stein Effect
"... A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is an important part of many algorithms ranging from kernel principal component analysis to Hilbertspace embedding of distributions. Given a finite sample, an empirical average is the standard estimate for the true ke ..."
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Cited by 3 (0 self)
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A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is an important part of many algorithms ranging from kernel principal component analysis to Hilbertspace embedding of distributions. Given a finite sample, an empirical average is the standard estimate for the true
Bayesian assessment of null values via parameter estimation and model comparison
 Perspectives on Psychological Science
, 2011
"... Psychologists have been trained to do data analysis by asking whether null values can be rejected. Is the difference between groups nonzero? Is choice accuracy not at chance level? These questions have been traditionally addressed by null hypothesis significance testing (NHST). NHST has deep problem ..."
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Cited by 23 (6 self)
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richer information than the model comparison approach. Keywords Bayes, model comparison, parameter estimation Psychologists are routinely trained to frame their research design and analysis in terms of rejecting null values. For example, when studying the influence of distraction on response time, we
Variance Estimation of the SurveyWeighted Kappa Measure of Agreement
"... The standard formula due to Fleiss et al. (1969) for estimating the variance of the estimated Cohen’s kappa, may be severely biased when using complex survey data, underestimating the variance. A procedure based on Taylor linearization is presented. The proposed procedure reduces to the Fleiss fo ..."
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Cited by 1 (0 self)
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The standard formula due to Fleiss et al. (1969) for estimating the variance of the estimated Cohen’s kappa, may be severely biased when using complex survey data, underestimating the variance. A procedure based on Taylor linearization is presented. The proposed procedure reduces to the Fleiss
Bayesian sequential inference for nonlinear multivariate diffusions
 Statistics and Computing
, 2006
"... In this paper, we adapt recently developed simulationbased sequential algorithms to the problem concerning the Bayesian analysis of discretely observed diffusion processes. The estimation framework involves the introduction of m −1 latent data points between every pair of observations. Sequential ..."
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Cited by 51 (6 self)
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MCMC methods are then used to sample the posterior distribution of the latent data and the model parameters online. The method is applied to the estimation of parameters in a simple stochastic volatility model (SV) of the U.S. shortterm interest rate. We also provide a simulation study to validate
Estimating Generalization Error Using OutofBag Estimates
"... We provide a method for estimating the generalization error of a bag using outofbag estimates. In bagging, each predictor (single hypothesis) is learned from a bootstrap sample of the training examples; the output of a bag (a set of predictors) on an example is determined by voting. The outofb ..."
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We provide a method for estimating the generalization error of a bag using outofbag estimates. In bagging, each predictor (single hypothesis) is learned from a bootstrap sample of the training examples; the output of a bag (a set of predictors) on an example is determined by voting. The out
Variance estimation for complex surveys in the presence of outliers
 In Proceedings of the Section on Survey Research Methods
, 2006
"... Quantitative variables in surveys often have a markedly skew distribution and, in addition, contain outliers. Robust estimators, which may be used in this situation, generally are biased. In addition linearized variance estimators tend to underestimate the true variance considerably. Alternatives ..."
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Cited by 1 (0 self)
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. Alternatives are Bootstrap variance estimators or estimators based on multiple imputation. A simulation study with data from the Swiss Household Budget Survey shows the effects of outliers on estimators and their variance estimators. Three poverty measures and proposals for the estimation of their variance
Results 11  20
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584