[PDF] Top 20 Bayesian Nonparametric Estimation of Ex post Variance
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Bayesian Nonparametric Estimation of Ex post Variance
... Several different approaches have been proposed to estimating ex-post variance under microstructure noise. Zhou (1996) first introduced the idea of using a kernel-based method to estimate ... See full document
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Robust Estimation of Local Genetic Ancestry in Admixed Populations Using a Nonparametric Bayesian Approach
... ancestry estimation prob- lems, typically no prior information is provided for the an- cestral populations and the ancestries of given individuals are recovered as mean proportions of each possible ...ancestry ... See full document
17
Does the Box Cox transformation help in forecasting macroeconomic time series?
... This paper contributes to the debate in two ways: first, we propose a fast nonparametric method based on the estimation of the prediction error variance (p.e.v.) of the normalized Box-Cox power ... See full document
23
Bayesian nonparametric estimation of EQ-5D utilities for United States using the existing United Kingdom data
... The preference based health state measure, EQ-5D, pro- vides 243 possible health states, in the time when the empirical survey conducted could only gather a valu- ation for a small subset. Therefore, the purpose of mod- ... See full document
11
Essays in Electoral Behavior and Bayesian Data Analysis
... likelihood estimation, relying on asymptotic normality in making inferences about the error variance and covariance ...the Bayesian approach based on Gibbs sampling is that it allows obtaining ... See full document
229
Kernel Density Estimation for the Eigenvalues of Variance Covariance Matrix of FFT Scaling of DNA Sequences: An Empirical Study of Some Organisms
... representations by measuring the sensitivity, specificity, correlation coefficient (CC) and the processing time for the protein coding region detection. The proposed technique based on digital filters was used to ... See full document
26
Bayesian Nonparametric Covariance Regression
... A nonparametric Nadaraya-Watson kernel estimator was proposed by Yin et ...a Bayesian nonparametric approach to simultaneously modeling µ(x) and ...a Bayesian nonparametric approach—the ... See full document
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Bayesian Nonparametric Crowdsourcing
... Crowdsourcing services are becoming very popular as a mean of outsourcing tasks to a large crowd of users. The best-known tool is Mechanical Turk (Amazon, 2005), in which requesters are able to post small tasks ... See full document
21
Nonparametric Quantile Estimation
... The next example is based on actual measurements of bone density (BMD) in adolescents. The data was originally reported in Bachrach et al. (1999) and is also analyzed in Hastie et al. (2001). 1 Figure 2 (a) shows a ... See full document
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On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models*
... that Bayesian nonparametric procedures have good frequentist properties when the loss function under study is somewhat related to the Kullback-Leibler divergence, or to phrase it differently when tests with ... See full document
13
Nonparametric Bayesian Aggregation for Massive Data
... optimal estimation rate, and our aggregated credible ball achieves desirable credibility level and also frequentist coverage while possessing asymptotically the same radius as the oracle ...underlying ... See full document
81
An Analysis of Selected Art Songs for High Voice by Adolphus Hailstork, A Performer's Guide
... function, Bayesian nonparametric ap- proaches place a prior on the space of distribution functions; examples are the cel- ebrated Dirichlet process, Polya tree priors, neutral to the right priors, Bernstein ... See full document
128
Bayesian Nonparametric Methods For Causal Inference And Prediction
... The motivation for using our EDP longitudinal model for this problem is as follows. Our interest lies in calculating the incidence of diabetes within one year of initiating a SGA, supplementing the outcome with ... See full document
102
Bayesian Semi-supervised Learning with Application to ROC Surface Estimation.
... a Bayesian nonparametric method by directly modeling the underlying distributions of the three categories by Dirichlet Process mixture ...the Bayesian bootstrap ... See full document
99
On Bayesian nonparametric estimation for stochastic processes
... transforms a prior distribution on the parameter space to a posterior distribution. Thus, taking a posterior expectation given x is equivalent to. multiplying 9 by the prior-normalized l[r] ... See full document
19
Bayesian Inference about Some Geometric Aspects of Nonparametric Functions.
... Recently, a promising algorithm called the Subspace Constrained Mean Shift (SCMS) algo- rithm, is proposed in Ozertem and Erdogmus (2011). It can be considered as an algorithm that generalizes the celebrated mean shift ... See full document
125
Categorization as nonparametric Bayesian density estimation
... fixed variance) over the value of some continuous stimulus dimension, then the resulting mixture distribution drawn from the DPMM is the one illustrated in ... See full document
51
Correlated Spatiotemporal Data Modeling Using Generalized Additive Mixed Model and Bivariate Smoothing Techniques
... the variance components by using marginal quasi-likelihood are ...classical nonparametric regression model and by Zhang et ...Gaussian nonparametric mixed models, where they treated the smoothing ... See full document
9
Prospects for statistical methods in dairy cattle breeding
... Accurate prediction of breeding values is of great importance for cattle improvement programmes. The prediction of breeding values requires knowledge of the magnitude of the variances and covariances of random effects. ... See full document
9
Nonparametric regression method for estimating the error variance in unistage sampling
... following section gives a review of nonparametric regression, states a nonparametric regression based estimator of the total, gives expressions for its bias and variance, and suggests ho[r] ... See full document
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