PL
 |
EN
Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl
Preferencje help
Polish version English version Język
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 6

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last
Wyniki wyszukiwania
Wyszukiwano:
w słowach kluczowych:  mixture model
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
1
Content available Poczucie śląskości wśród Ślązaków – analiza empiryczna z wykorzystaniem modeli klas ukrytych
100%
Genge E.
Econometrics. Ekonometria. Advances in Applied Data Analytics
|
2013
|
nr 4(42)
48-59
EN
The paper focuses on latent class models and their application for quantitative data. Latent class modeling is one of multivariate analysis techniques of the contingency table and can be viewed as a special case of model-based clustering, for multivariate discrete data. It is assumed that each observation comes from one of the numbers of subpopulations, with its own probability distribution. We used latent class analysis for grouping and detecting homogeneity of Silesian people using poLCA package of R. We analyzed data collected by the Department of Social Pedagogy, University of Silesia in Katowice.
2
Content available Small Area Estimation Under a Mixture Model
88%
Chandra H. , Bathla H. , Sud U. C.
Statistics in Transition new series
|
2010
|
tom 11
|
nr 3
76-89
EN
Small area estimation (SAE) under a linear mixed model may not be efficient if data contain substantial proportion of zeros than would be expected under standard model assumptions (hereafter zero-inflated data). We discuss the SAE for zero-inflated data under a mixture model (Fletcher et al., 2005 and Karlberg, 2000) that account for excess zeros in the data. Our results from simulation studies show that mixture model based approach for SAE works well and produces an efficient set of small area estimates. An application to real survey data from the National Sample Survey Organisation of India demonstrates the satisfactory performance of the approach.
3
Content available remote Mixed model for interoccurrence times of earthquakes based on the expectation-maximization algorithm
88%
Tahernia N. , Khodabin M. , Noorbakhsh M.
Acta Geophysica
|
2011
|
tom Vol. 59, no. 5
872-890
EN
In this paper, the goodness-of-fit test based on a convex combination of Akaike and Bayesian information criteria is used to explain the features of interoccurrence times of earthquakes. By analyzing the seismic catalog of Iran for different tectonic settings, we have found that the probability distributions of time intervals between successive earthquakes can be described by the generalized normal distribution. This indicates that the sequence of successive earthquakes is not a Poisson process. It is found that by decreasing the threshold magnitude, the interoccurrence time distribution changes from the generalized normal distribution to the gamma distribution in some seismotectonic regions. As a new insight, the probability distribution of time intervals between earthquakes is described as a mixture distribution via the expectation-maximization algorithm.
4
Content available Discrete phase model of blood flow in a roughness microchannel simulating the formation of pseudointima
63%
Kopernik M. , Dyrda K. , Kurtyka P. , Major R.
Acta of Bioengineering and Biomechanics
|
2022
|
tom Vol. 24, no. 1
132--144
EN
The goal of the present study was the development of discrete phase model to simulate the phenomenon of backfilling a morphologically complex surface by red blood cells (RBCs) in a flow microchannel and to anticipate the conditions of forming a pseudointima. The objective of the experimental studies that inspired the development of the simulation was to create a surface that stimulates the formation of the pseudointima layer. Methods: The finite volume method (FVM) and discrete particle method (DPM) were applied to develop the target model. In addition, a mixture model and a roughness model of bottom layer were tested in the present study to show their influence on simulation the phenomenon of backfilling a morphologically complex surface by RBCs in a flow microchannel. Results: Numerical models were developed including: a) FVM models to compare the effect of applying boundary conditions with/without roughness and cubes, as well as the analysis of their influence on blood velocity and shear stress; b) mixture models to compare the effect of applying different boundary conditions and cubes on computed results; c) DPM models to compare the effect of applying and not applying roughness as a boundary condition; d) DPM models with a morphologically complex surface and RBCs collisions to present RBCs concentration, velocity and time distributions during flow in a channel. Conclusions: The analysis carried out for the developed numerical models indicates that DPM model with cubes computes the best results. It also shows the backfilling of a morphologically complex surface of the bottom microchannel with RBCs.
5
Content available GEE Estimators in Mixture Model with Varying Concentrations
63%
Doronin O. , Maiboroda R.
Acta Universitatis Lodziensis. Folia Oeconomica
|
2015
|
tom 3
|
nr 314
PL
W pracy omówiono semiparametryczny model mieszany, w którym pewne współczynniki są parametryzowane za pomocą wspólnego parametru euklidesowego, natomiast inne są zupełnie nieznane. Wprowadzono metodę estymacji parametrów opartą na podejściu GEE (uogólnionych równań estymujących)  oraz adaptacyjnym podejściu GEE. Proponowane estymatory zostały przeanalizowane w badaniu symulacyjnym.
EN
We discuss semiparametric mixture model where some components are parametrized with common Euclidean parameter and others are fully unknown. We introduce GEE approach and adaptive GEE-based approach for parameter estimation. Proposed estimators are tested on simulated sample.
6
Content available remote Gaussian mixture model for time series-based structural damage detection
51%
Słoński M.
Computer Assisted Methods in Engineering and Science
|
2012
|
tom Vol. 19, no. 4
331-338
EN
In this paper, a time series-based damage detection algorit hm is proposed using Gaussian mixture model (GMM) and expectation maximization (EM) framework. The vib ration time series from the structure are modelled as the autoregressive (AR) processes. The first AR coefficients are used as a feature vector for novelty detection. To test the efficacy of the damage detec tion algorithm, it has been tested on the pseudo-experimental data obtained from the FEM model of the ASCE benchmark frame structure. Results suggest that the presented approach is able to detect mainly major and moderate damage patterns
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.