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Wyszukujesz frazę "linear mixed−effects models" wg kryterium: Temat

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    Tytuł:
    Modelowanie cech drzew i drzewostanów z wykorzystaniem modeli efektów mieszanych
    Modeling of the tree and stand parameters using mixed-effects models
    Autorzy:
    Bronisz, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/980258.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Leśne
    Tematy:
    lesnictwo
    drzewostany
    drzewa lesne
    grubosc kory
    cechy dendrometryczne
    modelowanie
    modele liniowe
    modele efektow mieszanych
    bark thickness
    scots pine
    regression
    linear mixed−effects models
    Opis:
    Regression analysis is one of the most popular statistical modeling tools, which can define linear or nonlinear relationships between individual trees and stands parameters. Mixed−effects models are one of the contemporary trends of those regression methods. These models can be applied to describe such features as: height, biomass, taper, site index or volume both at the level of a single tree and whole forest ecosystems. The aim of this work is to present the characteristics of the mixed−effects model, the applicability of linear and nonlinear mixed−effects models in forest studies, and the example of a linear mixed−effects model defining the relationship between bark thickness and diameter at breast height for Scots pine (Pinus sylvestris L.) in comparison to the linear fixed−effect model. Goodness−of−fit for the obtained linear mixed−effect model indicate its better fit to the pine bark thickness than in the case of the mixed−effects model. Moreover, most of the published research results indicate the predominance of both linear and nonlinear mixed−effects models according to fixed−effect ones. These studies indicate the wide possibilities of using mixed−effect models in forestry. However, there are also results pointing to the disadvantages of these models and put into question the legitimacy of their use in forest research. This fact to some extent confirms the results (residuals behavior) obtained in this study. Therefore, it seems necessary to conduct further research, which on the one hand will allow the potential of this solution to be used, and on the other hand will help to clarify emerging doubts.
    Źródło:
    Sylwan; 2019, 163, 07; 564-575
    0039-7660
    Pojawia się w:
    Sylwan
    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 1-2 z 2

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