Interpolation of random hyperplanes.
Intervalles de confiance pour les estimations des courbes de survie à partir du modèle de Cox
Invalid permutation tests.
Invariance principles for absolutely regular empirical processes
Invariance principles in Hölder spaces.
Invariant Density Estimation.
Irregular sampling and central limit theorems for power variations : the continuous case
In the context of high frequency data, one often has to deal with observations occurring at irregularly spaced times, at transaction times for example in finance. Here we examine how the estimation of the squared or other powers of the volatility is affected by irregularly spaced data. The emphasis is on the kind of assumptions on the sampling scheme which allow to provide consistent estimators, together with an associated central limit theorem, and especially when the sampling scheme depends on...
Iterative feature selection in least square regression estimation
This paper presents a new algorithm to perform regression estimation, in both the inductive and transductive setting. The estimator is defined as a linear combination of functions in a given dictionary. Coefficients of the combinations are computed sequentially using projection on some simple sets. These sets are defined as confidence regions provided by a deviation (PAC) inequality on an estimator in one-dimensional models. We prove that every projection the algorithm actually improves the performance...
Jaroslav Hájek and asymptotic theory of rank tests
-Depth-nearest Neighbour Method and its Performance on Skew-normal Distributons
In the present paper we investigate performance of the -depth-nearest classifier. This classifier, proposed recently by Vencálek, uses the concept of data depth to improve the classification method known as the -nearest neighbour. Simulation study which is presented here deals with the two-class classification problem in which the considered distributions belong to the family of skewed normal distributions.
Kapazitäten statt Wahrscheinlichkeiten? Gedanken zur Grundlegung der Statistik.
Kendall's tau-type rank statistics in genome data
High-dimensional data models abound in genomics studies, where often inadequately small sample sizes create impasses for incorporation of standard statistical tools. Conventional assumptions of linearity of regression, homoscedasticity and (multi-) normality of errors may not be tenable in many such interdisciplinary setups. In this study, Kendall's tau-type rank statistics are employed for statistical inference, avoiding most of parametric assumptions to a greater extent. The proposed procedures...
Kernel Estimation of a Smooth Distribution Function Based on Censored Data.
Kernel estimators and the Dvoretzky-Kiefer-Wolfowitz inequality
It turns out that for standard kernel estimators no inequality like that of Dvoretzky-Kiefer-Wolfowitz can be constructed, and as a result it is impossible to answer the question of how many observations are needed to guarantee a prescribed level of accuracy of the estimator. A remedy is to adapt the bandwidth to the sample at hand.
Kolmogorov-Smirnov two-sample test based on regression rank scores
We derive the two-sample Kolmogorov-Smirnov type test when a nuisance linear regression is present. The test is based on regression rank scores and provides a natural extension of the classical Kolmogorov-Smirnov test. Its asymptotic distributions under the hypothesis and the local alternatives coincide with those of the classical test.
-penalization in functional linear regression with subgaussian design
We study functional regression with random subgaussian design and real-valued response. The focus is on the problems in which the regression function can be well approximated by a functional linear model with the slope function being “sparse” in the sense that it can be represented as a sum of a small number of well separated “spikes”. This can be viewed as an extension of now classical sparse estimation problems to the case of infinite dictionaries. We study an estimator of the regression function...
-type contraction for systems of conservation laws
The semi-group associated with the Cauchy problem for a scalar conservation law is known to be a contraction in . However it is not a contraction in for any . Leger showed in [20] that for a convex flux, it is however a contraction in up to a suitable shift. We investigate in this paper whether such a contraction may happen for systems. The method is based on the relative entropy method. Our general analysis leads us to the new geometrical notion of Genuinely non-Temple systems. We treat in...
adaptive density estimation in a mixing framework
La medida de divergencia de Kagan en el muestreo secuencial con procesos de Dirichlet.
In this paper the Kagan divergence measure is extended in order to establish a measure of the information that a random sample gives about a Dirichlet process as a whole. After studying some of its properties, the expression obtained in sampling from the step n to the step n+1 is given, and its Bayesian properties are studied. We finish proving the good behaviour of a stopping rule defined on the basis of the information obtained in sampling when passing from a step to the following.
Large adaptive estimation in linear regression model. I. Consistency