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Wyszukujesz frazę "hierarchical linear model" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Application of HLM to data with multilevel structure
Autorzy:
Valente, Vítor
Oliveira, Teresa
Powiązania:
https://bibliotekanauki.pl/articles/729938.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:
hierarchical linear model
multilevel model
cross-classification models
academic achievement
Opis:
Many data sets analyzed in human and social sciences have a multilevel or hierarchical structure. By hierarchy we mean that units of a certain level (also referred micro units) are grouped into, or nested within, higher level (or macro) units. In these cases, the units within a cluster tend to be more different than units from other clusters, i.e., they are correlated. Thus, unlike in the classical setting where there exists a single source of variation between observational units, the heterogeneity between clusters introduces an additional source of variation and complicates the analysis.
Collecting data on Educational Research often does not follow the principles of simple random sample, suspected by classical regression, but rather a sample by nested clusters. Selected to students and also the contextual units to which they belong such as classes, courses, schools, neighborhoods or regions, and so forth.
Using classical regression bias is produced in the typical error of measurement and an increased likelihood of committing errors of inference. The hierarchical linear or multilevel models are most suitable because they consider the hierarchical relationships and also provide estimates on the contextual variability of regression coefficients. In practice, often the data structures are not hierarchical, are more complex structures such as cross-classification (level 2 or macro). For example, students (level 1 or micro) to attend different courses at a school while in other schools there are students who attend the same courses. Two examples of application to academic achievement of students are presented. First, a model of cross-classification of level 2 is used. Second, a hierarchical model of two levels (students and schools) is presented, taking into account the different areas of science - scientific-humanistic courses and technology courses.
Źródło:
Discussiones Mathematicae Probability and Statistics; 2011, 31, 1-2; 87-101
1509-9423
Pojawia się w:
Discussiones Mathematicae Probability and Statistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Small Area Estimation of Income Under Spatial SAR Model
Autorzy:
Kubacki, Jan
Jędrzejczak, Alina
Powiązania:
https://bibliotekanauki.pl/articles/465667.pdf
Data publikacji:
2016
Wydawca:
Główny Urząd Statystyczny
Tematy:
small area estimation (SAE)
SAR model
hierarchical Bayes estimation
spatial empirical best linear unbiased predictor
Opis:
The paper presents the method of hierarchical Bayes (HB) estimation under small area models with spatially correlated random effects and a spatial structure implied by the Simultaneous Autoregressive (SAR) process. The idea was to improve the spatial EBLUP by incorporating the HB approach into the estimation algorithm. The computation procedure applied in the paper uses the concept of sampling from a posterior distribution under generalized linear mixed models implemented in WinBUGS software and adapts the idea of parameter estimation for small areas by means of the HB method in the case of known model hyperparameters. The illustration of the approach mentioned above was based on a real-world example concerning household income data. The precision of the direct estimators was determined using own three-stage procedure which employs Balanced Repeated Replication, bootstrap and Generalized Variance Function. Additional simulations were conducted to show the influence of the spatial autoregression coefficient on the estimation error reduction. The computations performed by ‘sae’ package for R project and a special procedure for WinBUGS reveal that the method provides reliable estimates of small area means. For high spatial correlation between domains, noticeable MSE reduction was observed, which seems more evident for HB-SAR method as compared with the traditional spatial EBLUP. In our opinion, the Gibbs sampler, revealing the simultaneous nature of processes, especially for random effects, can be a good starting point for the simulations based on stochastic SAR processes.
Źródło:
Statistics in Transition new series; 2016, 17, 3; 365-390
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Comparison of Small Area Estimation Methods for Poverty Mapping
Autorzy:
Guadarrama, María
Molina, Isabel
Rao, J. N. K.
Powiązania:
https://bibliotekanauki.pl/articles/465671.pdf
Data publikacji:
2016
Wydawca:
Główny Urząd Statystyczny
Tematy:
area level model
non-linear parameters
empirical best estimator
hierarchical Bayes
poverty mapping
unit level models
Opis:
We review main small area estimation methods for the estimation of general nonlinear parameters focusing on FGT family of poverty indicators introduced by Foster, Greer and Thorbecke (1984). In particular, we consider direct estimation, the Fay-Herriot area level model (Fay and Herriot, 1979), the method of Elbers, Lanjouw and Lanjouw (2003) used by the World Bank, the empirical Best/Bayes (EB) method of Molina and Rao (2010) and its extension, the Census EB, and finally the hierarchical Bayes proposal of Molina, Nandram and Rao (2014). We put ourselves in the point of view of a practitioner and discuss, as objectively as possible, the benefits and drawbacks of each method, illustrating some of them through simulation studies.
Źródło:
Statistics in Transition new series; 2016, 17, 1; 41-66
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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