Publications associées à Quasi-likelihood

Boosting likelihood learning with event reweighting

Andrea Wulzer, Siyu Chen, Alfredo Glioti

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer Nature2024

An experimental and analytical study of wind turbine wakes under pressure gradient

Fernando Porté Agel, Arslan Salim Dar, Abraham Starbuck Gertler

This work is dedicated to the systematic investigation of wind turbine wakes under the effect of pressure gradients. Wind tunnel experiments are carried out with a wind turbine positioned on straight ramps of increasing angle such that it experiences an ap ...

2023

Max-infinitely divisible models and inference for spatial extremes

Emeric Rolland Georges Thibaud, Raphaël Huser

For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max-stable models are inadequate to capture the rate of joint tail decay, and to estimate jo ...

WILEY2021

Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

Ian Smith, Alain Nussbaumer, Sai Ganesh Sarvotham Pai

Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updatin ...

2018

Gyrokinetic modeling of impurity peaking in JET H-mode plasmas

Henri Weisen, Yann Camenen, Mikhail Maslov

Quantitative comparisons are presented between gyrokinetic simulations and experimental values of the carbon impurity peaking factor in a database of JET H-modes during the carbon wall era. These plasmas feature strong NBI heating and hence high values of ...

Amer Inst Physics2017

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Accurate Directional Inference for Vector Parameters in Linear Exponential Families

Anthony Christopher Davison

We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value i ...

American Statistical Association2014

Space-time modelling of extreme events

Anthony Christopher Davison, Raphaël Huser

Max-stable processes are the natural analogues of the generalized extreme-value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replicati ...

Wiley-Blackwell2014

Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics

Martin Vetterli, Luciano Sbaiz, Feng Yang, Yue Lu

We study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversam ...

Institute of Electrical and Electronics Engineers2012

A Quasi-Likelihood Approach to Zero-Inflated Spatial Count Data

Anthea Monod

The increased accessibility of data that are geographically referenced and correlated increases the demand for techniques of spatial data analysis. The subset of such data comprised of discrete counts exhibit particular difficulties and the challenges furt ...

EPFL2012

Robust Parameter Estimation for the Ornstein-Uhlenbeck Process

Sonja Rieder

In this thesis, we treat robust estimation for the parameters of the Ornstein–Uhlenbeck process, which are the mean, the variance, and the friction. We start by considering classical maximum likelihood estimation. For the simulation study, where we also in ...

EPFL2012

Comparison of Models for Olfactometer Data

Anthony Christopher Davison, Ingrid Ricard

Olfactometer experiments are used to study the responses of arthropods to potential attractants, for purposes such as understanding natural defenses of plants against their herbivores. Such experiments typically lead to multivariate data consisting of smal ...

Springer-Verlag2011

Robust inference for generalized linear models : binary and poisson regression

Sahar Hosseinian Ehrensberger

Generalized Linear Models have become a commonly used tool of data analysis. Such models are used to fit regressions for univariate responses with normal, gamma, binomial or Poisson distribution. Maximum likelihood is generally applied as fitting method. I ...

EPFL2009

Statistical inference for olfactometer data

Anthony Christopher Davison, Ingrid Ricard

Olfactometer experiments are used to determine the effect of odours on the behaviour of organisms such as insects or nematodes, and typically result in data comprising many groups of small overdispersed counts. We develop a non-homogeneous Markov chain mod ...

2007

General state-space models : randomised quasi-Monte Carlo methods for general Kalman recursion

Thomas Gsponer

In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...

EPFL2004