Set estimation under convexity type assumptions
Simple estimators of the parameters of generalized Tukey’s -family
Smoothing and preservation of irregularities using local linear fitting
For nonparametric estimation of a smooth regression function, local linear fitting is a widely-used method. The goal of this paper is to briefly review how to use this method when the unknown curve possibly has some irregularities, such as jumps or peaks, at unknown locations. It is then explained how the same basic method can be used when estimating unsmooth probability densities and conditional variance functions.
Smoothing Histograms by Means of Lattice- and Continuous Distributions.
Some adaptive estimators for slope parameter
An adaptive estimator (of a slope parameter) based on rank statistics is constructed and its asymptotic optimality is studied. A complete orthonormal system is incorporated in the adaptive determination of the score generating function. The proposed sequential procedure is based on a suitable stopping rule. Various properties of the sequential adaptive procedure and the stopping rule are studied. Asymptotic linearity results of linear rank statistics are also studied and some rates of the convergence...
Some applications of probability generating function based methods to statistical estimation
After recalling previous work on probability generating functions for real valued random variables we extend to these random variables uniform laws of large numbers and functional limit theorem for the empirical probability generating function. We present an application to the study of continuous laws, namely, estimation of parameters of Gaussian, gamma and uniform laws by means of a minimum contrast estimator that uses the empirical probability generating function of the sample. We test the procedure...
Some recent results on the law of the iterated logarithm for Banach space valued random variables
Spatial prediction of the mark of a location-dependent marked point process: How the use of a parametric model may improve prediction
We discuss the prediction of a spatial variable of a multivariate mark composed of both dependent and explanatory variables. The marks are location-dependent and they are attached to a point process. We assume that the marks are assigned independently, conditionally on an unknown underlying parametric field. We compare (i) the classical non-parametric Nadaraya-Watson kernel estimator based on the dependent variable (ii) estimators obtained under an assumption of local parametric model where explanatory...
Spatially adaptive density estimation by localised Haar projections
Given a random sample from some unknown density we devise Haar wavelet estimators for with variable resolution levels constructed from localised test procedures (as in Lepski, Mammen and Spokoiny (Ann. Statist.25(1997) 927–947)). We show that these estimators satisfy an oracle inequality that adapts to heterogeneous smoothness of , simultaneously for every point in a fixed interval, in sup-norm loss. The thresholding constants involved in the test procedures can be chosen in practice under...
Statistical inference for fault detection: a complete algorithm based on kernel estimators
This article presents a new concept for a statistical fault detection system, including the detection, diagnosis, and prediction of faults. Theoretical material has been collected to provide a complete algorithm making possible the design of a usable system for statistical inference on the basis of the current value of a symptom vector. The use of elements of artificial intelligence enables self-correction and adaptation to changing conditions. The mathematical apparatus is founded on the methodology...
Strong consistency of regression function estimates
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...
Suavización no paramétrica en fiabilidad.
En este trabajo consideramos estimaciones no paramétricas de las funciones de razón de fallo y supervivencia en fiabilidad haciendo uso de suavizaciones no paramétricas de la función de distribución empírica (datos no censurados) y de la distribución de Kaplan-Meier (datos censurados). Se obtienen sesgos, varianzas y distribuciones asintóticas de los estimadores aquí propuestos probándose mediante técnicas de expansiones de segundo orden la eficiencia de éstos respecto de otras estimaciones introducidas...
Sur la convergence L1 et L∞ de l'estimateur de la partition aléatoire pour une densité
Sur l'usage de la validation croisée en analyse en composantes principales
Tail-behaviour of location estimators in non-regular cases
Técnicas de validación cruzada en la estimación de la densidad bajo condiciones de dependencia.
Se estudian modificaciones de las técnicas de validación cruzada de Kullback-Leibler y mínimos cuadrados para obtener el parámetro de suavización asociado a un estimador general no paramétrico de la función de densidad, a partir de la muestra, en el supuesto de que los datos verifican alguna condición débil de dependencia.Se demuestra que los parámetros obtenidos por estas dos técnicas son asintóticamente óptimos. Y se realiza un estudio de simulación.
Text document classification based on mixture models
Finite mixture modelling of class-conditional distributions is a standard method in a statistical pattern recognition. This paper, using bag-of-words vector document representation, explores the use of the mixture of multinomial distributions as a model for class-conditional distribution for multiclass text document classification task. Experimental comparison of the proposed model and the standard Bernoulli and multinomial models as well as the model based on mixture of multivariate Bernoulli distributions...
The double kernel method in density estimation
The emergence of french probabilistic statistics. Borel and the Institut Henri Poincaré around the 1920s
This paper concerns the emergence of modern mathematical statistics in France after the First World War. Emile Borel’s achievements are presented, and especially his creation of two institutions where mathematical statistics was developed: the Statistical Institute of Paris University, (ISUP) in 1922 and above all the Henri Poincaré Institute (IHP) in 1928. At the IHP, a new journal Annales de l’Institut Henri Poincaré was created in 1931. We discuss the first papers in that journal dealing with...