Statistical Analysis of Fourier Coefficients in a Restricted Harmonic Dial.
Statistical analysis of periodic autoregression
Methods for estimating parameters and testing hypotheses in a periodic autoregression are investigated in the paper. The parameters of the model are supposed to be random variables with a vague prior density. The innovation process can have either constant or periodically changing variances. Theoretical results are demonstrated on two simulated series and on two sets of real data.
Statistical analysis of probabilistic model of the school-achievement test with triple-choice response
Statistical analysis of the generalized additive semiparametric survival model with random covariates.
Generalizations of the additive hazards model are considered. Estimates of the regression parameters and baseline function are proposed, when covariates are random. The asymptotic properties of estimators are considered.
Statistical choice of non-separated one-parameter models.
The purpose of this paper is to study the asymptotic choice between two models {F(x|α), α ∈ A ⊆ R} and {G(x|β), β ∈ B ⊆ R}, A and B being intervals but such that for (α0, β0}, and only for this pair, we have F(x|α0) = G(x|β0).
Statistical estimates of the parameters of stable laws
Statistical Inference about the Drift Parameter in Stochastic Processes
In statistical inference on the drift parameter in the Wiener process with a constant drift there is a large number of options how to do it. We may, for example, base this inference on the properties of the standard normal distribution applied to the differences between the observed values of the process at discrete times. Although such methods are very simple, it turns out that more appropriate is to use the sequential methods. For the hypotheses testing about the drift parameter it is more...
Statistical inference for stochastic parabolic equations: a spectral approach.
Statistical inference on the traffic intensity for the M/M/ queueing system.
Statistical models for analysing criminal careers
Statistique asymptotique presque-sûre de modèles statistiques convexes
Statistiques de diffusions et temps local
Stirling distributions and Stirling numbers of the second kind. Computational problems in statistics
Stochastic algorithm for Bayesian mixture effect template estimation
The estimation of probabilistic deformable template models in computer vision or of probabilistic atlases in Computational Anatomy are core issues in both fields. A first coherent statistical framework where the geometrical variability is modelled as a hidden random variable has been given by [S. Allassonnière et al., J. Roy. Stat. Soc.69 (2007) 3–29]. They introduce a Bayesian approach and mixture of them to estimate deformable template models. A consistent stochastic algorithm has been introduced...
Straight-Line Models in Star-Shaped Mixtures.
Strong Consistency of the MLE Under Random Censoring.
Strong convergence bound of the Pareto index estimator under right censoring.
Strong convergence for weighted sums of WOD random variables and its application in the EV regression model
The strong convergence for weighted sums of widely orthant dependent (WOD) random variables is investigated. As an application, we further investigate the strong consistency of the least squares estimator in EV regression model for WOD random variables. A simulation study is carried out to confirm the theoretical results.
Strong law of large numbers for additive extremum estimators
Extremum estimators are obtained by maximizing or minimizing a function of the sample and of the parameters relatively to the parameters. When the function to maximize or minimize is the sum of subfunctions each depending on one observation, the extremum estimators are additive. Maximum likelihood estimators are extremum additive whenever the observations are independent. Another instance of additive extremum estimators are the least squares estimators for multiple regressions when the usual assumptions...
Strong uniform consistency rates of some characteristics of the conditional distribution estimator in the functional single-index model
The aim of this paper is to establish a nonparametric estimate of some characteristics of the conditional distribution. Kernel type estimators for the conditional cumulative distribution function and for the successive derivatives of the conditional density of a scalar response variable Y given a Hilbertian random variable X are introduced when the observations are linked with a single-index structure. We establish the pointwise almost complete convergence and the uniform almost complete convergence...