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An application of branching processes in stochastic modeling of economic development

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Dudziński M., Furmańczyk K., Kociński M., Twardowska K.
Metody Ilościowe w Badaniach Ekonomicznych
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2010
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vol. 11
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issue 1
70-80
EN
In our paper, a stochastic model of forecasting of the numer of firms of a given type, acting on the market in a given year, is proposed. The model uses the probabilistic tools of the theory of branching processes. Our approach is an alternative method to the forecasting methods proposed so far, including those based on time series. The theoretical results presented in the paper may be applied in the forecasting of the market position of the firms of a given sector.
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The distribution and parameters of the distribution of the quotient of random variables

100%
Czajkowski A., Parys D., University of Łódź C. o. S. M.
Acta Universitatis Lodziensis. Folia Oeconomica
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1997
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vol. 141
EN
This paper presents some important earlier results of the research concerning the properties of the distribution of the quotient of random variables. We present also our own ideas and results of the research concerning the mean and the variance of the distribution of the quotient of random quadratic forms.
3
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Agu-Eghwerido distribution, regression model and applications

75%
Agu F. I., Eghwerido J. T.
Statistics in Transition new series
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2021
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vol. 22
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issue 4
59-76
EN
Modelling lifetime data with simple mathematical representations and an ease in obtaining the parameter estimate of survival models are crucial quests pursued by survival researchers. In this paper, we derived and introduced a one-parameter distribution called the Agu-Eghwerido (AGUE) distribution with its simple mathematical representation. The regression model of the AGUE distribution was also presented. Several basic properties of the new distribution, such as reliability measures, mean residual function, median, moment generating function, skewness, kurtosis, coefficient of variation, and index of dispersion, were derived. The estimation of the proposed distribution parameter was based on the maximum likelihood estimation method. The real-life applications of the distribution were illustrated using two real lifetime negatively and positively skewed data sets. The new distribution provides a better fit than the Pranav, exponential, and Lindley distributions for the data sets. The simulation results showed that the increase in parameter values decreases the mean squared error value. Similarly, the mean estimate tends towards the true parameter value as the sample sizes increase.
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