Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Pelletier, Mariane"'
Publikováno v:
In Expert Systems With Applications February 2024 236
Mining frequent itemsets and association rules is an essential task within data mining and data analysis. In this paper, we introduce PrefRec, a recursive algorithm for finding frequent itemsets and association rules. Its main advantage is its recurs
Externí odkaz:
http://arxiv.org/abs/2011.14195
Autor:
Mokkadem, Abdelkader1 (AUTHOR) abdelkader.mokkadem@uvsq.fr, Pelletier, Mariane1 (AUTHOR), Raimbault, Louis1,2 (AUTHOR)
Publikováno v:
Statistical Analysis & Data Mining. Oct2023, Vol. 16 Issue 5, p411-435. 25p.
In a pioneer work, R\'ev\'esz (1973) introduces the stochastic approximation method to build up a recursive kernel estimator of the regression function $x\mapsto E(Y|X=x)$. However, according to R\'ev\'esz (1977), his estimator has two main drawbacks
Externí odkaz:
http://arxiv.org/abs/0812.3973
We apply the stochastic approximation method to construct a large class of recursive kernel estimators of a probability density, including the one introduced by Hall and Patil (1994). We study the properties of these estimators and compare them with
Externí odkaz:
http://arxiv.org/abs/0807.2960
Let $\theta$ and $\mu$ denote the location and the size of the mode of a probability density. We study the joint convergence rates of semirecursive kernel estimators of $\theta$ and $\mu$. We show how the estimation of the size of the mode allows to
Externí odkaz:
http://arxiv.org/abs/0801.2070
In this paper, we prove large deviations principle for the Nadaraya-Watson estimator and for the semi-recursive kernel estimator of the regression in the multidimensional case. Under suitable conditions, we show that the rate function is a good rate
Externí odkaz:
http://arxiv.org/abs/math/0703341
Publikováno v:
Annals of Statistics 2007, Vol. 35, No. 4, 1749-1772
A stochastic algorithm for the recursive approximation of the location $\theta$ of a maximum of a regression function was introduced by Kiefer and Wolfowitz [Ann. Math. Statist. 23 (1952) 462--466] in the univariate framework, and by Blum [Ann. Math.
Externí odkaz:
http://arxiv.org/abs/math/0610487
Publikováno v:
Annals of Applied Probability 2006, Vol. 16, No. 3, 1671-1702
The first aim of this paper is to establish the weak convergence rate of nonlinear two-time-scale stochastic approximation algorithms. Its second aim is to introduce the averaging principle in the context of two-time-scale stochastic approximation al
Externí odkaz:
http://arxiv.org/abs/math/0610329
Let $f$ be a probability density and $C$ be an interval on which $f$ is bounded away from zero. By establishing the limiting distribution of the uniform error of the kernel estimates $f_n$ of $f$, Bickel and Rosenblatt (1973) provide confidence bands
Externí odkaz:
http://arxiv.org/abs/math/0606526