Kernel-based Estimators for Intractable Models
Adaptive warped kernel estimators
... the estimators is also investigated by computing the MISE for the dierent ...least-squares estimators in these cases, whatever the estimation support is, and whatever the chosen distributions ...
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Fast Computation of Kernel Estimators
... of Kernel Estimators Abstract The computational complexity of evaluating the kernel density estimate (or its derivatives) at m evaluation points given n sample points scales quadratically as ...
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Kernel Mean Shrinkage Estimators
... the kernel mean has played much more fundamental role as a basic build- ing block of many kernel-based learning algorithms (Vapnik, 1998; Sch¨ olkopf et ...as kernel principal component ...
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Multivariate goodness-of-fit tests based on kernel density estimators
... distributions based on kernel density ...criteria based on empirical distribution function, ...also based on kernel density estimator and some specific normality tests, ...
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Maxiset in sup-norm for kernel estimators
... the L q -norm (1 < q < ∞) and with polynomial rates of convergence (see Kerkyacharian & Picard (1993)). These results have been generalized by Rivoirard (2004) who proved that linear procedures are outperformed ...
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Kernel‐Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency
... First, we revisit the large sample properties of kernel-based semiparametric estimators and ob- tain novel distributional approximations for members of this large class. By design, these approxi- ...
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Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise
... of kernel-based estimators of the integrated variance in the presence of market microstructure ...optimal kernel-based esti- mator under an assumption that the noise is without memory ...
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Probabilistic short-term wind power forecasting based on kernel density estimators
... The prediction error approach “adds” uncertainty estima- tion to existing “spot” forecasting systems. Early approaches used global evaluation criteria (such as the standard deviation of forecast errors computed over ...
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A Reproducing Kernel Perspective of Smoothing Spline Estimators
... α i K(t, t i ). (43) One of the main attractive of this formulation is a Bayesian interpretation of such models, in which µ can be viewed as a realization of a zero-mean stationary Gaussian process, with prior ...
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New bandwidth selection for kernel quantile estimators
... of kernel quantile ...in kernel smoothing, based on unbiased estimation of a mean integrated squared error curve of which the minimising value determines an optimal ...
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A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions.
... Apart from various rules of thumb Baddeley et al., 2015, § 6.5; Illian et al., 2008, § 3.3; Scott, 1992, § 6; or the first edition of Diggle, 2014, there are essentially two main approach[r] ...
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Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators
... procedures based on kernel estimators of the density of the p-values, as the number m of tested hypotheses grows to ...location models, and for the Student ...
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Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators
... procedures based on kernel estimators of the density of the p-values, as the number m of tested hypotheses grows to ...location models, and for the Student ...
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Cluster-Based Estimators For Multiple And Multivariate Linear Regression Models
... these estimators is that they are imprac- tical to compute exactly in large ...produce estimators with lower consistency rates and breakdown values than the exact theoretical ...
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Bandwidth selection for kernel estimators of the spatial intensity function
... turns this statistic into an unbiased estimator of ℓ(W ). In essence, we have trans- formed the problem of selecting the unknown optimal bandwidth to that of estimating the known size of the study region and, as a ...
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Comparison of kernel density estimators with assumption on number of modes
... We will precise why this definition is available for the different kernels we will consider. The link between h and N ( ˆ fK,h) has been studied by several authors. With a Gaussian kernel (i.e. K is the density ...
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Asymptotics for General Multivariate Kernel Density Derivative Estimators
... with kernel functions has had notable success due to their ease of interpretation and ...goods/services based on the slope of their Engel curve so the first derivative is an essential component for ...
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Asymptotics for general multivariate kernel density derivative estimators
... with kernel functions has had notable success due to their ease of interpretation and ...on kernel density estimation (Parzen (1962)) was also concerned with the estimating the global mode of the density ...
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Antithetic and Monte Carlo kernel estimators for partial rankings
... We then propose two estimators that can be represented as the corresponding empirical mean embeddings; (2) a Monte Carlo kernel esti- mator that is based on sampling independent and iden[r] ...
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Multi-kernel based nonlinear functional connectivity models
... a kernel-based nonlinear regression approach to esti- mate the PC coefficients as well as the PGC test statistics, on the premise that nonlinear estimators will offer an improved fit of the data as ...
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