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Wyszukujesz frazę "Wesołowski, Jacek" wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Rotation schemes and Chebyshev polynomials
Autorzy:
Wesołowski, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/18105126.pdf
Data publikacji:
2023-06-13
Wydawca:
Główny Urząd Statystyczny
Opis:
There is a continuing interplay between mathematics and survey methodology involving different branches of mathematics, not only probability. This interplay is quite obvious as regards the first of the two options: probability vs. non-probability sampling, as proposed and discussed in Kalton (2023). There, mathematics is represented by probability and mathematical statistics. However, sometimes connections between mathematics and survey methodology are less obvious, yet still crucial and intriguing. In this paper we refer to such an unexpected relation, namely between rotation sampling and Chebyshev polynomials. This connection, introduced in Kowalski and Wesołowski (2015), proved fundamental for the derivation of an explicit form of the recursion for the BLUE µ^t of the mean on each occasion t in repeated surveys based on a cascade rotation scheme. This general result was obtained under two basic assumptions: ASSUMPTION I and ASSUMPTION II, expressed in terms of the Chebyshev polynomials. Moreover, in that paper, it was conjectured that these two assumptions are always satisfied, so the derived form of recursion is universally valid. In this paper, we partially confirm this conjecture by showing that ASSUMPTION I is satisfied for rotation patterns with a single gap of an arbitrary size.
Źródło:
Statistics in Transition new series; 2023, 24, 3; 47-59
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-domain Neyman-Tchuprov optimal allocation
Autorzy:
Wesołowski, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/1186924.pdf
Data publikacji:
2019-12-10
Wydawca:
Główny Urząd Statystyczny
Tematy:
stratified SRSWOR
Opis:
The eigenproblem solution of the multi-domain efficient allocation is identified as a direct generalization of the classical Neyman-Tchuprov optimal allocation in stratified SRSWOR. This is achieved through analysis of eigenvalues and eigenvectors of a suitable population-based matrix D. Such a solution is an analytical companion to NLP approaches, which are often used in applications, see, e.g. Choudhry, Rao and Hidiroglou (2012). In this paper we are interested rather in the structure of the optimal allocation vector and relative variance than in such purely numerical tools (although the eigenproblem solution provides also numerical solutions, see, e.g. Wesołowski and Wieczorkowski (2017)). The domain-wise optimal allocation and the respective optimal variance of the estimator are determined by the unique I direction (defined in terms of the positive eigenvector of matrix D) in the space R , where I is the number of domains in the population.
Źródło:
Statistics in Transition new series; 2019, 20, 4; 1-12
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamic K-Composite Estimator for an Arbitrary Rotation Scheme
Autorzy:
Ciepiela, Przemysław
Gniado, Małgorzata
Wesołowski, Jacek
Wojtyś, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/466030.pdf
Data publikacji:
2012
Wydawca:
Główny Urząd Statystyczny
Opis:
Classical K-composite estimator was proposed in Hansen et al. (1955). Its optimality properties were developed in Rao and Graham (1964). This estimator gives an alternative solution to quasi-optimal estimation under rotation sampling when it is allowed that units leave the sample for several occasions and then come back. Such situations happen frequently in real surveys and are not covered by the recursive optimal estimator introduced by Patterson (1955). However the K-composite estimator suffers from certain disadvantages. It is designed for a stable situation in the sense that its basic parameter is kept constant on all occasions. Additionally it is restricted only to a certain family of rotation designs. Here we propose a dynamic version of the K-composite estimator (DK-composite estimator) without any restrictions on the rotation pattern. Mathematically, the algorithm, we develop, is much simpler than the one for the classical K-composite estimator with optimal weights. Moreover, it is precise, in the sense that it does not use any approximate or asymptotic approach (opposed to the method used in Rao and Graham (1964) for computing optimal weights).
Źródło:
Statistics in Transition new series; 2012, 13, 1; 7-20
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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