138,396 research outputs found
Asymptotic consistency under large entropy sampling designs with unequal probabilities
No full textA large part of survey sampling literature is devoted to unequal probabilities sampling designs without replacement. Brewer and Hanif (1983) provided a summary of these sampling designs. The maximum entropy designs is one of them. Consistency results have been proven for the maximum entropy sampling (Hájek, 1964). The aim is to give sufficient conditions under which Hájek (1964) consistency results still hold for large entropy sampling designs which are different from the maximum entropy design. These conditions involve modes of convergence of sampling designs towards the maximum entropy design. We show that these conditions are satisfied for the popular Rao-Sampford (Rao, 1965, Sampford, 1967) design. Our consistency results are applied to the Hájek (1964) simple variance estimator. This estimator does not require joint-inclusion probabilities and can be easily estimated using weighted least squares regression (Berger, 2004, 2005b). Deville (1999) conjectured that this estimator is suitable for any sampling designs (see also Brewer and Donadio, 2003). Our consistency result gives regularity conditions under which this estimator is consistent which justifies Deville’s (1999) conjecture
A Note on the asymptotic equivalence of jackknife and linearization variance estimation for the Gini Coefficient
No full textThe Gini coefficient has proved valuable as a measure of income inequality. In cross-sectional studies of the Gini coefficient, information about the accuracy of its estimate is crucial. We show how to use jackknife and linearization to estimate the variance of the Gini coefficient, allowing for the effect of the sampling design. The aim is to show the asymptotic equivalence (or consistency) of the generalised jackknife estimator and the Taylor linearization estimator for the variance of the Gini coefficient. A brief simulation study supports our findings
Majesty : gavotte pour piano / par. Rod. Berger ; [ill. par] G. Grellet
No full textTitre uniforme : Berger, Rodolphe (1864-1916). Compositeur. [Majesty. Piano]Piano, Musique de -- +* 1800......- 1899......+:19e siècle:Gavottes (piano) -- +* 1800......- 1899......+:19e siècle
Denkmal wahrer Freundschaft bey dem Grabe unsers geliebten Freundes Iohann Ernst Wilhelm Weigand, d. G. G. Befl. aus Römhild
No full textGedächtnisgedicht auf Johann Ernst Wilhelm Weigand, +1790[H. T. Ch. Berger, d. G. G. Bef. aus Römhild. G. Ch. Büchner, d. g. G. Befl. a. d. Hildburgh. H. W. Dorn, d. G. G. Befl. a. d. Hennebergischen ...]Autopsie nach Exemplar der ULB Sachsen-AnhaltVorlageform des Erscheinungsvermerks: Iena, gedruckt mit Fiedlerischen Schriften 1790
Empirical Likelihood Confidence Intervals under the Rao-Hartley-Cochran Sampling Design
No full textThe Hartley-Rao-Cochran (RHC) sampling design (Rao et al., 1962) is a popular unequal probability sampling design. We show how empirical likelihood confidence intervals can be derived under this sampling design. Berger and De La Riva Torres (2012) proposed an empirical likelihood approach which can be used for point estimation and to construct confidence intervals under complex sampling designs. We show how this approach can be adjusted for the RHC sampling design. The proposed approach intrinsically incorporates sampling weights and auxiliary information. It may give better coverages than standard methods even when the sampling distribution of the parameters of interest is not normal. The proposed approach is simple to implement and less computer intensive than bootstrap. The proposed approach does not rely on re-sampling, linearisation, variance estimation, or design-effects
Empirical likelihood confidence intervals and significance test for regression parameters under complex sampling designs
No full textConfidence intervals based on ordinary least squares may have poor coverages for regression parameters when the effect of sampling design is ignored. Standard confidence intervals based on design variances may not have the right coverages when the sampling distribution is skewed. Berger and De La Riva Torres (2012) proposed an empirical likelihood approach which can be used for point estimation and to construct confidence intervals under complex sampling designs for a single parameter. We show that this approach can be extended to test the significance of a subset of model parameters and to derive confidence intervals. The proposed approach is not a straightforward extension of Berger and De La Riva Torres (2012) approach, because we consider the situation when the parameter is multidimensional and the parameter of interest is a subset of the parameter. This requires profiling which is not covered by Berger and De La Riva Torres (2012). The proposed approach intrinsically incorporates sampling weights, design variables, and auxiliary information. It may yield to more accurate confidence intervals when the sampling distribution of the regression parameters is not normal, the point estimator is biased, or the regression model is not linear. The proposed approach is simple to implement and less computer intensive than bootstrap. The proposed approach does not rely on re-sampling, linearisation, variance estimation, or design-effect
A Berger type normal holonomy theorem for complex submanifolds
No full textWe prove a kind of Berger-Simons' Theorem for the normal holonomy group of a complex submanifold of the projective spac
Sylphes et lutins : pièce de genre : [pour piano] / Rodolphe Berger ; [ill. par] G. [et] G. Fraipont
No full textTitre uniforme : Berger, Rodolphe (1864-1916). Compositeur. [Sylphes et lutins. Piano]Piano, Musique de -- +* 1900......- 1999......+:20e siècle
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