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Wyszukujesz frazę "Cholesky decomposition" wg kryterium: Temat

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Multiple Linear Regression Using Cholesky Decomposition
    Autorzy:
    Sumiati, Ira
    Handoyo, Fiyan
    Purwani, Sri
    Powiązania:
    https://bibliotekanauki.pl/articles/1031897.pdf
    Data publikacji:
    2020
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Cholesky decomposition
    Multiple linear regression
    covariance matrix
    Opis:
    Various real-world problem areas, such as engineering, physics, chemistry, biology, economics, social, and other problems can be modeled with mathematics to be more easily studied and done calculations. One mathematical model that is very well known and is often used to solve various problem areas in the real world is multiple linear regression. One of the stages of working on multiple linear regression models is the preparation of normal equations which is a system of linear equations using the least-squares method. If more independent variables are used, the more linear equations are obtained. So that other mathematical tools that can be used to simplify and help to solve the system of linear equations are matrices. Based on the properties and operations of the matrix, the linear equation system produces a symmetric covariance matrix. If the covariance matrix is also positive definite, then the Cholesky decomposition method can be used to solve the system of linear equations obtained through the least-squares method in multiple linear regression. Based on the background of the problem outlined, such that this paper aims to construct a multiple linear regression model using Cholesky decomposition. Then, the application is used in the numerical simulation and real case.
    Źródło:
    World Scientific News; 2020, 140; 12-25
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modeling Macro-Financial Linkages: Combined Impulse Response Functions in SVAR Models
    Autorzy:
    Serwa, Dobromił
    Wdowiński, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2119956.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    vector autoregression
    Cholesky decomposition
    combined impulseresponse
    banking sector
    real economy
    Opis:
    We estimated a structural vector autoregressive (SVAR) model describing the links between a banking sector and a real economy. We proposed a new method to verify robustness of impulse-response functions to the ordering of variables in an SVAR model. This method applies permutations of orderings of variables and uses the Cholesky decomposition of the error covariance matrix to identify parameters. Impulse response functions are computed and combined for all permutations. We explored the method in practice by analyzing the macro- financial linkages in the Polish economy. Our results indicate that the combined impulse response functions are more uncertain than those from a single model specification with a given ordering of variables, but some findings remain robust. It is evident that macroeconomic aggregate shocks and interest rate shocks have a significant impact on banking variables.
    Źródło:
    Central European Journal of Economic Modelling and Econometrics; 2017, 4; 323-357
    2080-0886
    2080-119X
    Pojawia się w:
    Central European Journal of Economic Modelling and Econometrics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Linear Cholesky decomposition of covariance matrices in mixed models with correlated random effects
    Autorzy:
    Rabe, Anasu
    Shangodoyin, D. K.
    Thaga, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/1186928.pdf
    Data publikacji:
    2019-12-10
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    correlated random effects
    covariance matrix
    linear Cholesky decomposition
    linear mixed models
    Opis:
    Modelling the covariance matrix in linear mixed models provides an additional advantage in making inference about subject-specific effects, particularly in the analysis of repeated measurement data, where time-ordering of the responses induces significant correlation. Some difficulties encountered in these modelling procedures include high dimensionality and statistical interpretability of parameters, positive definiteness constraint and violation of model assumptions. One key assumption in linear mixed models is that random errors and random effects are independent, and its violation leads to biased and inefficient parameter estimates. To minimize these drawbacks, we developed a procedure that accounts for correlations induced by violation of this key assumption. In recent literature, variants of Cholesky decomposition were employed to circumvent the positive definiteness constraint, with parsimony achieved by joint modelling of mean and covariance parameters using covariates. In this article, we developed a linear Cholesky decomposition of the random effects covariance matrix, providing a framework for inference that accounts for correlations induced by covariate(s) shared by both fixed and random effects design matrices, a circumstance leading to lack of independence between random errors and random effects. The proposed decomposition is particularly useful in parameter estimation using the maximum likelihood and restricted/residual maximum likelihood procedures.
    Źródło:
    Statistics in Transition new series; 2019, 20, 4; 59-70
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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