Adaptive circular deconvolution by model selection under unknown error distribution

We consider a circular deconvolution problem, in which the density $f$ of a circular random variable $X$ must be estimated nonparametrically based on an i.i.d. sample from a noisy observation $Y$ of $X$. The additive measurement error is supposed to be independent of $X$. The objective of this work was to construct a fully data-driven estimation procedure when the error density $\varphi$ is unknown. We assume that in addition to the i.i.d. sample from $Y$, we have at our disposal an additional i.i.d. sample drawn independently from the error distribution. We first develop a minimax theory in terms of both sample sizes. We propose an orthogonal series estimator attaining the minimax rates but requiring optimal choice of a dimension parameter depending on certain characteristics of $f$ and $\varphi$, which are not known in practice. The main issue addressed in this work is the adaptive choice of this dimension parameter using a model selection approach. In a first step, we develop a penalized minimum contrast estimator assuming that the error density is known. We show that this partially adaptive estimator can attain the lower risk bound up to a constant in both sample sizes $n$ and $m$. Finally, by randomizing the penalty and the collection of models, we modify the estimator such that it no longer requires any previous knowledge of the error distribution. Even when dispensing with any hypotheses on $\varphi$, this fully data-driven estimator still preserves minimax optimality in almost the same cases as the partially adaptive estimator. We illustrate our results by computing minimal rates under classical smoothness assumptions.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ422 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Consistent Density Deconvolution under Partially Known Error Distribution

We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identifiable from the observations if we restrict the class of considered distributions by a simple condition in the time domain. A minimum distance estimator is shown to be consistent imposing only a slightly stronger assumption than the identification condition.deconvolution, error measurement, density estimation

Adaptive Gaussian inverse regression with partially unknown operator

This work deals with the ill-posed inverse problem of reconstructing a function $f$ given implicitly as the solution of $g = Af$, where $A$ is a compact linear operator with unknown singular values and known eigenfunctions. We observe the function $g$ and the singular values of the operator subject to Gaussian white noise with respective noise levels $\varepsilon$ and $\sigma$. We develop a minimax theory in terms of both noise levels and propose an orthogonal series estimator attaining the minimax rates. This estimator requires the optimal choice of a dimension parameter depending on certain characteristics of $f$ and $A$. This work addresses the fully data-driven choice of the dimension parameter combining model selection with Lepski's method. We show that the fully data-driven estimator preserves minimax optimality over a wide range of classes for $f$ and $A$ and noise levels $\varepsilon$ and $\sigma$. The results are illustrated considering Sobolev spaces and mildly and severely ill-posed inverse problems

First second of leptons

A poorly constrained parameter in the Standard Model of Cosmology is the lepton asymmetry l = \sum_f l_f=\sum_f(n_f+n_{\nu_f})/s. Each flavour asymmetry l_f with f=e, \mu, {\tau} is the sum of the net particle density of the charged leptons n_f and their corresponding neutrinos, normalized with the entropy density s. Constraints on l_f \leq O(0.1) from BBN and CMB allow for lepton flavour asymmetries orders of magnitudes larger then the baryon asymmetry b ~ 10^{-10}. In this article we show how such large lepton (flavour) asymmetries influence the early universe, in particular the freeze out of WIMPs and the cosmic QCD transition.Comment: 4 pages, 2 figures; prepared for the 12th international conference on Topics in Astroparticle and Underground Physics, TAUP2011. v2: matches accepted versio

Nonparametric frontier estimation from noisy data

A new nonparametric estimator of production frontiers is defined and studied when the data set of production units is contaminated by measurement error. The measurement error is assumed to be an additive normal random variable on the input variable, but its variance is unknown. The estimator is a modification of the m-frontier, which necessitates the computation of a consistent estimator of the conditional survival function of the input variable given the output variable. In this paper, the identification and the consistency of a new estimator of the survival function is proved in the presence of additive noise with unknown variance. The performance of the estimator is also studied through simulated data.production frontier, deconvolution, measurement error, efficiency analysis

Nonparametric Frontier Estimation from Noisy Data

A new nonparametric estimator of production a frontier is defined and studied when the data set of production units is contaminated by measurement error. The measurement error is assumed to be an additive normal random variable on the input variable, but its variance is unknown. The estimator is a modification of the m-frontier, which necessitates the computation of a consistent estimator of the conditional survival function of the input variable given the output variable. In this paper, the identification and the consistency of a new estimator of the survival function is proved in the presence of additive noise with unknown variance. The performance of the estimator is also studied through simulated data.

Non parametric estimation in the presence of noise with unknown distribution

This thesis is concerned with the development of estimation techniques in four models involving statistical inverse problems with noise in the operator. Firstly, we consider a density deconvolution model on the real line: a probability density is to be estimated from observations which are subject to an independent additive measurement error. Assuming that the error is centered and normally distributed with unknown variance, we develop an intuitive time-domain condition on the target density which allows for its identification and consistent estimation by means of a minimum distance estimator. Next, we consider a stochastic frontier model. Our aim consists in estimating the support boundary of a two dimensional probability distribution based on observations with independent additive and normally distributed noise in one dimension. Exploiting the deconvolution techniques from the first chapter, we develop a consistent two step procedure for the non parametric estimation of the frontier, using in particular the m-frontier technique. In the following chapter, we look at the special density deconvolution model where the densities are supported on the circle instead of the real line. We drop the normality hypothesis for the error distribution. Instead, we assume that in addition to the sample of contaminated observations, a sample drawn from the error distribution is available. Minimax theory in both sample sizes is developed and a fully data-driven estimator is defined and shown to be minimax optimal over a wide range of density classes. Finally, we consider a regression model with instrumental variables. The minimax rates for the non parametric estimation of the structural function are developed and shown to be attained by an adaptive estimator in certain cases

Mapping Spikes to Sensations

Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data) is observed in parallel with spike trains in sensory neurons (the neurometric data). Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of candidate neural codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research

Particle asymmetries in the early universe

The total lepton asymmetry $l=\sum_f l_f$ in our universe is only poorly constrained by theories and experiments. It might be orders of magnitudes larger than the observed baryon asymmetry $b\simeq {\cal O}(10^{-10})$, $|l|/b \leq {\cal O}(10^{9})$. We found that the dynamics of the cosmic QCD transition changes for large asymmetries. Predictions for asymmetries in a single flavour $l_f$ allow even larger values. We find that asymmetries of $l_f\leq {\cal O}(1)$ in a single or two flavours change the relic abundance of WIMPs. However, large lepton and large individual lepton flavour asymmetries influences significantly the dynamics of the early universe.Comment: 7 pages,8 figures; Proceedings of the Erice workshop on Nuclear Physics 2010 "Particle and Nuclear Astrophysics

Lepton asymmetry and the cosmic QCD transition

We study the influence of lepton asymmetry on the evolution of the early Universe. The lepton asymmetry $l$ is poorly constrained by observations and might be orders of magnitude larger than the baryon asymmetry $b$, $|l|/b \leq 2\times 10^8$. We find that lepton asymmetries that are large compared to the tiny baryon asymmetry, can influence the dynamics of the QCD phase transition significantly. The cosmic trajectory in the $\mu_B-T$ phase diagram of strongly interacting matter becomes a function of lepton (flavour) asymmetry. Large lepton asymmetry could lead to a cosmic QCD phase transition of first order.Comment: 23 pages, 14 figures; matches published version, including Erratum. Conclusions, pictures, numerics remained unchange