One of the features present in the actual data from many areas (Economy, Finance, Hydrology, Astrophysics) is the presence of dependency. This dependence between observations may manifest in several degrees (low dependency, high dependency).
Statistical inference for such data requires a more suitable theorems of more suitable central limit theorems.
The time series analysis is often based on the assumption of stationarity. However the actual data observed in many areas are often non-stationary.
Examples include, stock market cracks often induce breaks in macroeconomic and financial time series, climate change involves changing settings in the generating process of the temperature data, hence the need to introduce more suitable statistical models (locally stationary processes, processes with breaks).

The aim of this international meeting, declined in two themes, is:

– Make a state of the art of different scientific advances in each of the two themes
– Open new directions for research and opportunities for young researchers

Scientific Committee

Jean-Marc Bardet (Université Paris 1- Panthéon-Sorbonne)
Rainer Dahlhaus (University of Heidelberg)
Paul Doukhan (Université de Cergy Pontoise)

Organizing Committee

Mohamed Boutahar (Aix-Marseille Université)
Laurence Reboul (Aix-Marseille Université)

Speakers

• Antoine Ayache  (Université Lille 1)

Stationary increments harmonizable stable fields: upper estimates on path behavior

• Jean-Marc Bardet (Université Paris 1 – Panthéon-Sorbonne)

Asymptotic behavior of the Laplacian quasi-maximum likelihood estimator of affine causal processes

• Pierre Bertrand (Université Clermont-Ferrand)

Verfitting of the Hurst index for a multifractional Brownian motionr

• Marianne Clausel (Institut Joseph Fourier Université de Grenoble)

Large scale reduction principle and application to hypothesis testing

• Jérôme Dedecker  (Université Paris Descartes)

Behavior of the Wasserstein distance between
the empirical and the marginal distributions of alpha-dependent sequences

• Holger Dette (Ruhr-Universitaet Bochum)

Detecting long-range dependence in non-stationary time series

• Michael Eichler (Maastricht University)

Semi-parametric dynamic factor models for non-stationary time series

• Konstantinos Fokianos (University of Cyprus)

Mallows’ Quasi-Likelihood Estimation for Log-linear Poisson
Autoregressions

• Davide Giraudo (Université de Rouen) 

​​Hölderian weak invariance principle for strictly stationary sequences 

• Ieva Grublyté (Vilnius University)

Quasi-MLE for quadratic ARCH model with long memory

• Marc Hoffman (Université Paris Dauphine)

Statistical inference for bifurcating Markov chains

• Adam Jakubowski (Nicolaus Copernicus University)

Phantom distribution functions for dependent random vectors

• William Kengne (Université de Cergy-Pontoise)

Testing for parameter change in a general class of time seriesof counts

• Emilie  Lebarbier (INRA / AgroParisTech)

Segmentation of time-series with various types of dependency

• Jacques Levy-Vehel (INRIA)

Processes with varying local regularities

• Florence Merlevède (Université de Marne-la-Vallée)

Strong approximation for additive functionals of geometrically ergodic Markov chains

• Guy Nason (University of Bristol)

A test for local white noise (and the absence of aliasing) in locallystationary wavelet time series

• Efstathios Paparoditis (University of Cyprus)

Periodogram Based Tests of Stationarity

• Stefan Richter (Heidelberg University)

Adaptive bandwidth selection with cross validation for locallystationary processes

• François Roueff  (TELECOM ParisTech)

Posterior consistency for partially-observed Markov models

• Philippe Soulier (Université Paris Ouest Nanterre)

Fonctional limit theorems for weakly dependent regularly vary-ing time series

• Suhasini Subba Rao (Texas A&M University)

Fourier based statistics for irregular spaced spatial data

• Charles Suquet  (Université Lille 1)

Detecting a changed segment in a sample

• Dag Tjostheim (University of Bergen)

Some further properties and applications  of local Gaussian approximation

• Lionel  Truquet (ENSAI – Campus de Ker-Lann)

Parameter stability and semiparametric inference in time-varyingARCH processes

• Michael Vogt (University of Konstanz)  

Classification of nonparametric time trends

• Dalibor  Volny  (Univers
  ité de Rouen)

Martingale central limit theorems for random fields

• Rainer Von sachs (Université catholique de Louvain)

Time-frequency analysis of locally stationary Hawkes processes