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Wyszukujesz frazę "representative measure" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
On representativeness, informative sampling, nonignorable nonresponse, semiparametric prediction and calibration
Autorzy:
Eideh, Abdulhakeem
Powiązania:
https://bibliotekanauki.pl/articles/14781728.pdf
Data publikacji:
2023-03-15
Wydawca:
Główny Urząd Statystyczny
Tematy:
calibration
representative measure
response distribution
nonignorable nonresponse
informative sampling esign
Opis:
Informative sampling refers to a sampling design for which the sample selection probabilities depend on the values of the model outcome variable. In such cases the model holding for the sample data is different from the model holding for the population data. Similarly, nonignorable nonresponse refers to a nonresponse mechanism in which the response probability depends on the value of a missing outcome variable. For such a nonresponse mechanism the model holding for the response data is different from the model holding for the population data. In this paper, we study, within a modelling framework, the semi-parametric prediction of a finite population total by specifying the probability distribution of the response units under informative sampling and nonignorable nonresponse. This is the most general situation in surveys and other combinations of sampling informativeness and response mechanisms can be considered as special cases. Furthermore, based on the relationship between response distribution and population distribution, we introduce a new measure of the representativeness of a response set and a new test of nonignorable nonresponse and informative sampling, jointly. Finally, a calibration estimator is obtained when the sampling design is informative and the nonresponse mechanism is nonignorable.
Źródło:
Statistics in Transition new series; 2023, 24, 2; 93-111
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast reduction of large dataset for nearest neighbor classifier
Autorzy:
Raniszewski, M.
Powiązania:
https://bibliotekanauki.pl/articles/333106.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
metody podziału
metody redukcji
przetwarzanie obrazów
reguła najbliższego sąsiada
pomiar reprezentatywny
division methods
reduction methods
images processing
nearest neighbour rule
representative measure
Opis:
Accurate and fast classification of large data obtained from medical images is very important. Proper images (data) processing results to construct a classifier, which supports the work of doctors and can solve many medical problems. Unfortunately, Nearest Neighbor classifiers become inefficient and slow for large datasets. A dataset reduction is one of the most popular solution to this problem, but the large size of a dataset causes long time of a reduction phase for reduction algorithms. A simple method to overcome the large dataset reduction problem is a dataset division into smaller subsets. In this paper five different methods of large dataset division are considered. The received subsets are reduced by using an algorithm based on representative measure. The reduced subsets are combined to form the reduced dataset. The experiments were performed on a large (almost 82 000 samples) two–class dataset dating from ultrasound images of certain 3D objects found in a human body.
Źródło:
Journal of Medical Informatics & Technologies; 2010, 16; 111-116
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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