Publication
Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models
Related publications (44)
Extremal behaviour of aggregated data with an application to downscaling
Sebastian Engelke, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville
The distribution of spatially aggregated data from a stochastic process may exhibit tail behaviour different from that of its marginal distributions. For a large class of aggregating functionals we introduce the -extremal coefficient, which quantifies this ...
OXFORD UNIV PRESS2019Encoding and Decoding Models in Cognitive Electrophysiology
Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model- ...
Frontiers Media Sa2017Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
Wulfram Gerstner, Tilo Schwalger, Moritz Deger
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacti ...
Public Library of Science2017Interference Functionals in Poisson Networks
We propose and prove a theorem that allows the calculation of a class of functionals on Poisson point processes that have the form of expected values of sum-products of functions. In proving the theorem, we present a variant of the Campbell-Mecke theorem f ...
Ieee-Inst Electrical Electronics Engineers Inc2016Amplitude And Phase Variation Of Point Processes
We develop a canonical framework for the study of the problem of registration of multiple point processes subjected to warping, known as the problem of separation of amplitude and phase variation. The amplitude variation of a real random function {Y(x) : x ...
Institute of Mathematical Statistics2016Bayesian Uncertainty Management in Temporal Dependence of Extremes
Anthony Christopher Davison, Thomas Lugrin, Jonathan A. Tawn
Both marginal and dependence features must be described when modelling the extremes of a stationary time series. There are standard approaches to marginal modelling, but long- and short-range dependence of extremes may both appear. In applications, an assu ...
Springer Verlag2016Bayesian uncertainty management in temporal dependence of extremes
Anthony Christopher Davison, Thomas Lugrin, Jonathan A. Tawn
Both marginal and dependence features must be described when modelling the extremes of a stationary time series. There are standard approaches to marginal modelling, but long-and short-range dependence of extremes may both appear. In applications, an assum ...
Springer Verlag2016Max-stable processes and stationary systems of Levy particles
We study stationary max-stable processes {n(t): t is an element of R} admitting a representation of the form n(t) = max(i is an element of N) (U-i +Y-i(t)), where Sigma(infinity)(i=1) delta U-i is a Poisson point process on R with intensity e(-u)du, and Y1 ...
Elsevier2015Statistical structure of neural spiking under non-Poissonian stimulation
Can we understand the interspike interval (ISI) statistics of spontaneous neural activity? What is the relation between input and output statistics of a neuron? --> Important for understanding population activity. Most theoretical studies assume that neuro ...
2014Statistical models of effective connectivity in neural microcircuits
To appreciate how neural circuits in the brain control behaviors, we must identify how the neurons comprising the circuit are connected. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured ...
EPFL2014