The stability of the problem of statistical estimation and choice of the loss function
The Stepwise Regression Algorithm Seen from the Statistician's Point of View
The strong pointwise convergence of nearest neighbor function fitting algorithm with applications to system identification
The sum-product algorithm: algebraic independence and computational aspects
The sum-product algorithm is a well-known procedure for marginalizing an “acyclic” product function whose range is the ground set of a commutative semiring. The algorithm is general enough to include as special cases several classical algorithms developed in information theory and probability theory. We present four results. First, using the sum-product algorithm we show that the variable sets involved in an acyclic factorization satisfy a relation that is a natural generalization of probability-theoretic...
The teaching of probability theory and statistics in secondary schools from the occupation of the liberation. (Probabilités et statistiques dans l'enseignement secondaire de l'occupation à la libération.)
The "Thirty-seven Percent Rule" and the secretary problem with relative ranks
We revisit the problem of selecting an item from n choices that appear before us in random sequential order so as to minimize the expected rank of the item selected. In particular, we examine the stopping rule where we reject the first k items and then select the first subsequent item that ranks lower than the l-th lowest-ranked item among the first k. We prove that the optimal rule has k ~ n/e, as in the classical secretary problem where our sole objective is to select the item of lowest rank;...
The treatment of commercial uncertitude during medieval scholasticism. (Le traitement de l'incertitude commerciale dans la scolastique médiévale.)
The truncated least squares method for a class of autoregressive models.
The two-dimensional linear relation in the errors-in-variables model with replication of one variable
We present a two-dimensional linear regression model where both variables are subject to error. We discuss a model where one variable of each pair of observables is repeated. We suggest two methods to construct consistent estimators: the maximum likelihood method and the method which applies variance components theory. We study asymptotic properties of these estimators. We prove that the asymptotic variances of the estimators of regression slopes for both methods are comparable.
The Type A Uncertainty
If in the model of measurement except useful parameters, which are to be determined, other auxiliary parameters occur as well, which were estimated from another experiment, then the type A and B uncertainties of measurement results must be taken into account. The type A uncertainty is caused by the new experiment and the type B uncertainty characterizes an accuracy of the parameters which must be used in estimation of useful parameters. The problem is to estimate of the type A uncertainty in the...
The UMVUE of P(X<Y) Based on Type-II Censored Samples from Weinman Multivariate Exponential Distributions.
The Uniqueness of the Transfer Function of Linear System from Input-Output Observations.
The unreplicated complex linear functional relationship model and its application.
The use of copulas in the study of certain transforms of random variables with applications in finance.
The use of third-order moments in structural models.
Structural models are usually estimated using only second order moments (covariances or correlations). When variables are nor multivariate normally distributed, however, methods that also fit higher order moments, such as skewnesses, are theoretically asymptotically preferable. This article reports result from a Monte Carlo simulation study in which estimators that fit both second-order moments and third-order moments are compared with estimators that fit only second-order moments.
The variogram and estimation error in connection with the assessment of continuous streams.
The weights of the results of partial tests for determining the total result of the test
Théorème de limite centrale pour un estimateur non paramétrique de la variance d'un processus de diffusion multidimensionnelle
Théorèmes limite fonctionnels pour les processus de vraisemblance (cadre asymptotiquement non gaussien)
Theoretical and practical considerations regarding bonus-malus system.