Effect of Outliers on the Estimation of Parameters.
Efficiency and robustness control via distorted maximum likelihood estimation
Efficient robust estimation of time-series regression models
The paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust fit. Just like many existing two-step robust methods, the proposed 2S-LWS estimator preserves robust properties of the initial robust estimate. However, contrary to the existing methods, the first-order asymptotic behavior...
Eine Bemerkung zur Existenz erwartungstreuer Schätzer.
Einige Bemerkungen zur Theorie der Maximum Probability Schätzer.
Equivalent algorithms for estimation in linear model with conditions
Errors estimation and the asymptotic distribution of probabilistic estimates.
Estimación de correlaciones utilizando envolturas convexas.
En el presente trabajo se realiza un estudio de la envoltura convexa de una muestra normal bivariante, analizando la distribución de la pendiente de sus aristas. En base a ello se propone un estimador del coeficiente de correlación de la población, investigando algunas propiedades del mismo.
Estimates for distributions of sums of random variables with subexponential distributions.
Estimates of Relative Risk.
Estimates of reliability for the normal distribution
The minimum variance unbiased, the maximum likelihood, the Bayes, and the naive estimates of the reliability function of a normal distribution are studied. Their asymptotic normality is proved and asymptotic expansions for both the expectation and the mean squared error are derived. The estimates are then compared using the concept of deficiency. In the end an extensive Monte Carlo study of the estimates in small samples is given.
Estimating from cross-sectional categorical data subject to misclassification and double sampling: moment-based, maximum likelihood and quasi-likelihood approaches.
Estimating normal density and normal distribution function: is Kolmogorov's estimator admissible?
The statistical estimation problem of the normal distribution function and of the density at a point is considered. The traditional unbiased estimators are shown to have Bayes nature and admissibility of related generalized Bayes procedures is proved. Also inadmissibility of the unbiased density estimator is demonstrated.
Estimating quantiles with Linex loss function. Applications to VaR estimation
Sometimes, e.g. in the context of estimating VaR (Value at Risk), underestimating a quantile is less desirable than overestimating it, which suggests measuring the error of estimation by an asymmetric loss function. As a loss function when estimating a parameter θ by an estimator T we take the well known Linex function exp{α(T-θ)} - α(T-θ) - 1. To estimate the quantile of order q ∈ (0,1) of a normal distribution N(μ,σ), we construct an optimal estimator in the class of all estimators of the form...
Estimating Survivor Function Using Optimally Selected Order Statistics.
Estimating the Variance of the Ratio Estimator for the Midzuno-Sen Sampling Scheme.
Estimation and prediction in regression models with random explanatory variables [Book]
Estimation and tests of the discrete probability law based on the empirical generating function, (two dimensional case).
Estimation de la variance, dans un modèle classique, si les coefficients d'aplatissement des variables sont connus
Estimation des paramètres d'un modèle de Rasch mixte par la méthode GEE : application en qualité de vie