Estimation non paramétrique de l'espérance et de la variance de la loi de reproduction d'un processus de ramification
Estimation of the drift function for Ito processes and a class of semimartingales via histogram sieve
A histogram sieve estimator of the drift function in Ito processes and some semimartingales is constructed. It is proved that the estimator is pointwise and L¹ consistent and its finite-dimensional distributions are asymptotically normal. Our approach extends the results of Leśkow and Różański (1989a).
Estimation of the transition density of a Markov chain
We present two data-driven procedures to estimate the transition density of an homogeneous Markov chain. The first yields a piecewise constant estimator on a suitable random partition. By using an Hellinger-type loss, we establish non-asymptotic risk bounds for our estimator when the square root of the transition density belongs to possibly inhomogeneous Besov spaces with possibly small regularity index. Some simulations are also provided. The second procedure is of theoretical interest and leads...
Estimation paramétrique d'un processus de diffusion avec retards
Estimator of variance of Wiener process based on its integral
Exponential inequalities for VLMC empirical trees
A seminal paper by Rissanen, published in 1983, introduced the class of Variable Length Markov Chains and the algorithm Context which estimates the probabilistic tree generating the chain. Even if the subject was recently considered in several papers, the central question of the rate of convergence of the algorithm remained open. This is the question we address here. We provide an exponential upper bound for the probability of incorrect estimation of the probabilistic tree, as a function...
Exponential rate of convergence of maximum likelihood estimators for inhomogeneous Wiener processes
Hidden Markov model likelihoods and their derivatives behave like i.i.d. ones
How to combine fast heuristic Markov chain Monte Carlo with slow exact sampling.
Hurwicz's estimator of the autoregressive model with non-normal innovations
Using the Bahadur representation of a sample quantile for m-dependent and strong mixing random variables, we establish the asymptotic distribution of the Hurwicz estimator for the coefficient of autoregression in a linear process with innovations belonging to the domain of attraction of an α-stable law (1 < α < 2). The present paper extends Hurwicz's result to the autoregressive model.
Identifications optimales de paramètres pour un système linéaire excité par un bruit gaussien
Inference about stationary distributions of Markov chains based on divergences with observed frequencies
For data generated by stationary Markov chains there are considered estimates of chain parameters minimizing –divergences between theoretical and empirical distributions of states. Consistency and asymptotic normality are established and the asymptotic covariance matrices are evaluated. Testing of hypotheses about the stationary distributions based on –divergences between the estimated and empirical distributions is considered as well. Asymptotic distributions of –divergence test statistics are...
LAN and LAMN for systems of interacting diffusions with branching and immigration
Likelihood for interval-censored observations from multi-state models.
We consider the mixed dicrete-continuous pattern of observation in a multi-state model; this is a classical pattern because very often clinical status is assessed at discrete visit times while time of death is observed exactly. The likelihood can easily be written heuristically for such models. However a formal proof is not easy in such observational patterns. We give a rigorous derivation al the likelihood for the illness-death model based on applying Jacod´s formula to an observed bivariate counting...
Limit theorems for stationary Markov processes with L2-spectral gap
Let be a discrete or continuous-time Markov process with state space where is an arbitrary measurable set. Its transition semigroup is assumed to be additive with respect to the second component, i.e. is assumed to be a Markov additive process. In particular, this implies that the first component is also a Markov process. Markov random walks or additive functionals of a Markov process are special instances of Markov additive processes. In this paper, the process is shown to satisfy the...
Minimax and Bayes estimation when the loss function is unknown
Minimum distance estimator for a hyperbolic stochastic partial differentialequation
We study a minimum distance estimator in -norm for a class ofnonlinear hyperbolic stochastic partial differential equations, driven by atwo-parameter white noise. The consistency and asymptotic normality of thisestimator are established under some regularity conditions on thecoefficients. Our results are applied to the two-parameterOrnstein-Uhlenbeck process.
Moment estimation of customer loss rates from transactional data.
Monte Carlo simulation of Markov chain steady-state distribution.
Nonparametric adaptive estimation for pure jump Lévy processes
This paper is concerned with nonparametric estimation of the Lévy density of a pure jump Lévy process. The sample path is observed at n discrete instants with fixed sampling interval. We construct a collection of estimators obtained by deconvolution methods and deduced from appropriate estimators of the characteristic function and its first derivative. We obtain a bound for the -risk, under general assumptions on the model. Then we propose a penalty function that allows to build an adaptive estimator....