Valeurs extrémales de suites stationnaires de variables aléatoires m-dépendantes
Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models
In testing that a given distribution Pbelongs to a parameterized family , one is often led to compare a nonparametric estimateAn of some functional A of P with an element Aθn corresponding to an estimate θn of θ. In many cases, the asymptotic distribution of goodness-of-fit statistics derived from the process n1/2(An−Aθn) depends on the unknown distribution P. It is shown here that if the sequences An and θn of estimators are regular in some sense, a parametric bootstrap approach yields valid approximations...
VaR: exchange rate risk and jump risk.
Variable selection through CART
This paper deals with variable selection in regression and binary classification frameworks. It proposes an automatic and exhaustive procedure which relies on the use of the CART algorithm and on model selection via penalization. This work, of theoretical nature, aims at determining adequate penalties, i.e. penalties which allow achievement of oracle type inequalities justifying the performance of the proposed procedure. Since the exhaustive procedure cannot be realized when the number of variables...
Variables finitas condicionalmente especificadas.
En este trabajo se estudia la existencia y unicidad de vectores bidimensionales de variables discretas con recorrido finito, cuando se fijan sus distribuciones condicionadas. Para ello, tras repasar la literatura existente sobre el tema, proporcionamos diversos resultados que relacionan diversos temas de álgebra matricial, especialmente la descomposición singular, con el problema que nos ocupa.
Variance components and an additional experiment
Estimators of parameters of an investigated object can be considered after some time as insufficiently precise. Therefore, an additional measurement must be realized. A model of a measurement, taking into account both the original results and the new ones, has a litle more complicated covariance matrix, since the variance components occur in it. How to deal with them is the aim of the paper.
Variance components and nonlinearity
Unknown parameters of the covariance matrix (variance components) of the observation vector in regression models are an unpleasant obstacle in a construction of the best estimator of the unknown parameters of the mean value of the observation vector. Estimators of variance componets must be utilized and then it is difficult to obtain the distribution of the estimators of the mean value parameters. The situation is more complicated in the case of nonlinearity of the regression model. The aim of the...
Variance components estimators in a replicated regression model
Variance Estimation Using Auxiliary Information: An Almost Unbiased Multivariate Ratio Estimator.
Variance function estimation via model selection
The problem of estimating an unknown variance function in a random design Gaussian heteroscedastic regression model is considered. Both the regression function and the logarithm of the variance function are modelled by piecewise polynomials. A finite collection of such parametric models based on a family of partitions of support of an explanatory variable is studied. Penalized model selection criteria as well as post-model-selection estimates are introduced based on Maximum Likelihood (ML) and Restricted...
Variance of estimator of a discrete parameter
Variance of periodic measure of bounded set with random position
The principal term in the asymptotic expansion of the variance of the periodic measure of a ball in under uniform random shift is proportional to the st power of the grid scaling factor. This result remains valid for a bounded set in with sufficiently smooth isotropic covariogram under a uniform random shift and an isotropic rotation, and the asymptotic term is proportional also to the -dimensional measure of the object boundary. The related coefficients are calculated for various periodic...
Variance of Plug-in Estimators in Multivariate Regression Models
Variance components in regression models are usually unknown. They must be estimated and it leads to a construction of plug–in estimators of the parameters of the mean value of the observation matrix. Uncertainty of the estimators of the variance components enlarge the variances of the plug–in estimators. The aim of the paper is to find this enlargement.
Variance reduction trends on `boosted' classifiers.
Variance Stabilizing and Normalizing Transformations for the Compound Poisson Process.
Variances in spectral analysis of membrane noise
Le fluttuazioni di conduttanza di un modello di canale del potassio di una fibra muscolare che segue una cinetica di Hodgkin e Huxley sono state analizzate attraverso l'analisi spettrale indiretta. Sono state confrontate due diverse stime della densità spettrale e le loro rispettive varianze: quella della prima stima considerata è già nota, mentre quella della seconda stima è stata ricavata da noi nelle medesime ipotesi (distribuzione normale). I risultati teorici sono stati confrontati con quelli...
Variant II of probabilistic model of school-achievement test with double choice response
Variational theorems in gnostical theory of uncertain data
Variations on undirected graphical models and their relationships
We compare alternative definitions of undirected graphical models for discrete, finite variables. Lauritzen [7] provides several definitions of such models and describes their relationships. He shows that the definitions agree only when joint distributions represented by the models are limited to strictly positive distributions. Heckerman et al. [6], in their paper on dependency networks, describe another definition of undirected graphical models for strictly positive distributions. They show that...