Autor: Sitek, Grzegorz - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Modeling income on the basis of distribution mixture
Autorzy:: Sitek, Grzegorz
Powiązania:: https://bibliotekanauki.pl/articles/584955.pdf
Data publikacji:: 2014
Wydawca:: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:: finite mixture
income distribution
maximum likelihood estimate
EM algorithm
number of components
Opis:: Finite mixtures of probability distributions may be successfully used in the modeling of probability distributions of incomes. These distributions are typically heavy tailed and positively skewed. This article deals with the problem of determining the number of components in mixture modeling. This paper considers the likelihood of ratio-based testing of the null hypothesis of homogeneity in mixture models. The number of components is an important parameter in the applications of finite mixture models.
Źródło:: Mathematical Economics; 2014, 10(17); 79-90
1733-9707
Pojawia się w:: Mathematical Economics
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Loss modeling with mixtures distributions in R package
Autorzy:: Sitek, Grzegorz
Powiązania:: https://bibliotekanauki.pl/articles/434037.pdf
Data publikacji:: 2017
Wydawca:: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:: finite mixture of distributions
loss distribution
maximum likelihood estimate
EM algorithm
Opis:: Finite mixtures of probability distributions may be successfully used in the modeling of probability distributions of losses. These distributions are typically heavy tailed and positively skewed. Finding the distribution that fits loss data well is often difficult. The paper shows that the use of mixed models can significantly improve the goodness-of-fit of the loss data. The paper also presents an algorithm to find estimates of parameters of mixture distribution and gives an illustrative example. The analytical approach is probably the most often used in practice and certainly the most frequently adopted in the actuarial literature. It is reduced to finding a suitable analytical expression which fits the observed data well. For parameters estimation we use the maximum likelihood method applying the Newton-Raphson and EM algorithm. Computations of goodness-of-fit can be judged using the Akaike information criterion.
Źródło:: Śląski Przegląd Statystyczny; 2017, 15 (21); 183-200
1644-6739
Pojawia się w:: Śląski Przegląd Statystyczny
Dostawca treści:: Biblioteka Nauki

Artykuł

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