Conditional Density Estimation - Parameter Results and In-
Empirical Bayes Conditional Density Estimation
... mixing weights. We have shown that a data-driven selection of the prior hyper- parameters can lead to inferential answers that are comparable, for large sample sizes, to those of hierarchical posteriors in automatically ...
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Regularized Non-Parametric Multivariate Density and Conditional Density Estimation
... Multivariate Density and Conditional Density Estimation Peter Krauthausen and Uwe ...non-parametric density and conditional density estimation is ...both ...
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A quantile-copula approach to conditional density estimation
... Taking advantage of this regression formulation, Fan et al. [ 4 ] generalized the kernel-based estimate of the conditional density using local polynomial techniques. This makes it possible to tackle the ...
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Conditional density estimation with class probability estimators
... a conditional density estimate is available, then prediction intervals can be derived from ...computing conditional density estimates using a class proba- bility estimator, where this ...
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Parameter estimation via conditional expectation: a Bayesian inversion
... Since the parameters of the model to be estimated are uncertain, all relevant information may be obtained via their stochastic description. In order to extract information from the posterior, most estimates take the form ...
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Adaptive Smoothing Parameter in Kernel Density Estimation and Parameter Estimation in Normal Mixture Distributions
... provides results which are straightforward to visualize and intuitive to interpret particularly in univariate ...The density estimate depends on the starting position of the ...the density estimate ...
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Using conditional kernel density estimation for wind power density forecasting
... nche (2006) explains that, in reality, the form of the power curve depends on meteorological variables such as wind direction, temperature, local air density and precipitation. He notes that the behavior of power ...
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The Hybrid Density Filter for Nonlinear Estimation based on Hybrid Conditional Density
... exact density as ...exact density and the Gaussian mixture density approximation resulting from the ...GPF results in a significant difference in ...
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Nonstandard Estimation of Inverse Conditional Density-Weighted Expectations
... semiparametric estimation of function means that are scaled by an unknown conditional density ...a conditional expecta- tion with respect to a continuously distributed “special regressor” with ...
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Nonparametric Estimation and Symmetry Tests for Conditional Density Functions.
... nonparametric estimation of g( ·|x), since the estimation is localized by the kernel ...the conditional distribution of T ∗ given {X i ,Y i } is asymptotically equal to the null-hypothesis ...
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A Bayesian approach to parameter estimation for kernel density estimation via transformations
... kernel density estimation of bivariate insurance claim data via ...kernel density estimator based on original data does not perform ...the density of the original data can be estimated through ...
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Sequential risk efficient estimation of the parameter in the uniform density
... (Received 7 October 1997 and in revised form 22 October 1998) Abstract. We develop a risk-efficient sequential procedure for estimating the parameter θ of the uniform density on (0,θ). We give explicit ...
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Posterior Consistency in Conditional Density Estimation by Covariate Dependent Mixtures
... Introduction. Estimation of conditional distributions is an important problem in em- pirical ...modeling conditional densities in the Bayesian framework. First, the conditional distributions ...
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Fast conditional density estimation for quantitative structure-activity relationships
... the conditional density of the activity given the struc- ture instead of a point ...a conditional density es- timate is available, it is easy to derive prediction intervals of ...for ...
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Approximation results for parameter estimation in non-linear elastomers
... This class of systems was introduced in [BGS, BLMY] and further studied in [BLGMY] as a model for the behavior of nonlinear elastomers . These materials, which are used in the development of active and passive vibration ...
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Nonparametric estimation and inference for conditional density based Granger causality measures.
... nonparametric estimation and inference for conditional density based Granger causality mea- sures that quantify linear and nonlinear Granger ...
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Monthly river flow simulation with a joint conditional density estimation network
... thetic data set simulated from a known bivariate distribu- tion, which was designed such that it can mimic typical behavior of seasonal variability of monthly river flow. The Clayton copula was used to construct the ...
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Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions
... In model testing, similar effort was initially made to obtain the support region of the non-parametric CCF estimate, denoted as S N P , and the support region of the theoretical CCF under H 0 , denoted as S H 0 . Here the ...
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Robust parameter estimation of density functions under fuzzy interval observations
... remain constant (0.25) and jumps to 1 for the Dirac function on 30. A natural issue is whether in the case of missing data, one may replace them by the whole range of the ran- dom variable, say an interval [a, b], or ...
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Gravitational waves: search results, data analysis and parameter estimation
... seven-dimensional parameter space, can be mapped to the two-dimensional subspace of non- precessing binaries, character- ized by the mass ratio and a single effective total ...with parameter biases in the ...
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