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We consider the problem of testing equality of functionsf j: [0,1] →Rfor j =1,2,...,J the basis ofJindependent samples from possibly different distributions under the assumption that the functions are...
Statistical emulators of computer simulators have proven to be useful in a variety of applications. The widely adopted model for emulator building, using a Gaussian process model with strictly positiv...
Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they provide a natural choi...
Given a Reproducing Kernel Hilbert Space (H, h., .i) of real-valued functions and a suitable measure \mu over the source space, we decompose H as sum of a subspace of centered functions for {\mu} and ...
Two-point correlation functions are used throughout cosmology as a measure for the statistics of random fields. When used in Bayesian parameter estimation, their likelihood function is usually replac...
In a nonparametric linear regression model we study a variant of LASSO, called pLASSO, which does not require the knowledge of the scaling parameter σ of the noise or bounds for it. This work derive...
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the res...
We consider the problem of estimating a function $s$ on $[-1,1]^{k}$ for large values of $k$ by looking for some best approximation by composite functions of the form $g\circ u$. Our solution is based...
This note demonstrates that it is possible to bound the expectation of an arbitrary norm of a random matrix drawn from the Stiefel manifold in terms of the expected norm of a standard Gaussian matrix ...
We consider a class of sparsity-inducing regularization terms based on submodular functions. While earlier work has focused on non-decreasing functions, we explore symmetric submodular functions and t...
Deterministic computer simulations are often used as a replacement for com- plex physical experiments. Although less expensive than physical experi- mentation, computer codes can still be time-consu...
The notion of probability density for a random function is not as straightforward as in finite-dimensional cases. While a probability density function generally does not exist for functional data, w...
We consider a positive stationary generalized Ornstein–Uhlenbeck process Vt = e−tZ t0es− ds + V0 for t  0,and the increments of the integrated generalized Ornstein–Uhlenbeck process...
The use of regularization, or penalization, has become increasingly common in highdimensional statistical analysis over the past decade, where a common goal is to simultaneously select important var...

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