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Displaying 121 – 140 of 1158

Adaptive goodness-of-fit testing from indirect observations

Cristina Butucea, Catherine Matias, Christophe Pouet (2009)

Annales de l'I.H.P. Probabilités et statistiques

In a convolution model, we observe random variables whose distribution is the convolution of some unknown density f and some known noise density g. We assume that g is polynomially smooth. We provide goodness-of-fit testing procedures for the test H0: f=f0, where the alternative H1is expressed with respect to $𝕃_{2}$ -norm (i.e. has the form $ψ_{n}^{- 2} {∥ f - f_{0} ∥}_{2}^{2} \geq 𝒞$ ). Our procedure is adaptive with respect to the unknown smoothness parameterτ of f. Different testing rates (ψn) are obtained according to whether f0 is polynomially...

Adaptive hard-thresholding for linear inverse problems

Paul Rochet (2013)

ESAIM: Probability and Statistics

A number of regularization methods for discrete inverse problems consist in considering weighted versions of the usual least square solution. These filter methods are generally restricted to monotonic transformations, e.g. the Tikhonov regularization or the spectral cut-off. However, in several cases, non-monotonic sequences of filters may appear more appropriate. In this paper, we study a hard-thresholding regularization method that extends the spectral cut-off procedure to non-monotonic sequences....

Additions and correction to “The bootstrap of the mean with arbitrary bootstrap sample”

Miguel A. Arcones, Evarist Giné (1991)

Annales de l'I.H.P. Probabilités et statistiques

Admissible functions for cones and asumptotics of infinitely divisible distributions.

Yakymiv, A.L. (2006)

Publications de l'Institut Mathématique. Nouvelle Série

Almost Higher Order Stochastic Dominance

Cuizhen Niu, Xu Guo (2014)

RAIRO - Operations Research - Recherche Opérationnelle

In this paper, we develop the concept of almost stochastic dominance for higher order preferences and investigate the related properties of this concept.

Almost sure absolute continuity of Bernoulli convolutions

Michael Björklund, Daniel Schnellmann (2010)

Annales de l'I.H.P. Probabilités et statistiques

We prove an extension of a result by Peres and Solomyak on almost sure absolute continuity in a class of symmetric Bernoulli convolutions.

An analysis of the Rüschendorf transform - with a view towards Sklar’s Theorem

Frank Oertel (2015)

Dependence Modeling

We revisit Sklar’s Theorem and give another proof, primarily based on the use of right quantile functions. To this end we slightly generalise the distributional transform approach of Rüschendorf and facilitate some new results including a rigorous characterisation of an almost surely existing “left-invertibility” of distribution functions.

An application of multivariate total positivity to peacocks

Antoine Marie Bogso (2014)

ESAIM: Probability and Statistics

We use multivariate total positivity theory to exhibit new families of peacocks. As the authors of [F. Hirsch, C. Profeta, B. Roynette and M. Yor, Peacocks and associated martingales vol. 3. Bocconi-Springer (2011)], our guiding example is the result of Carr−Ewald−Xiao [P. Carr, C.-O. Ewald and Y. Xiao, Finance Res. Lett. 5 (2008) 162–171]. We shall introduce the notion of strong conditional monotonicity. This concept is strictly more restrictive than the conditional monotonicity as defined in [F....

An Application of the Renewal Theoretic Selection Principle: The First Divisible Sum.

R. Grübel (1985)

Metrika

An Approach to Distribution of the Product of Two Normal Variables

Antonio Seijas-Macías, Amílcar Oliveira (2012)

Discussiones Mathematicae Probability and Statistics

The distribution of product of two normally distributed variables come from the first part of the XX Century. First works about this issue were [1] and [2] showed that under certain conditions the product could be considered as a normally distributed. A more recent approach is [3] that studied approximation to density function of the product using three methods: numerical integration, Monte Carlo simulation and analytical approximation to the result using the normal distribution....