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    Wyświetlanie 1-7 z 7
    Tytuł:
    The Best Model of LASSO With The LARS (Least Angle Regression and Shrinkage) Algorithm Using Mallow’s Cp
    Autorzy:
    Januaviani, Trisha Magdalena Adelheid
    Gusriani, Nurul
    Joebaedi, Khafsah
    Supian, Sudradjat
    Subiyanto, Subiyanto
    Powiązania:
    https://bibliotekanauki.pl/articles/1076395.pdf
    Data publikacji:
    2019
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Cp Mallows
    LARS
    LASSO
    Multicollinearity
    Opis:
    Multicollinearity often occurs in regression analysis. Multicollinearity is a condition of correlation between independent variables which is a problem. One method that can overcome multicollinearity is the LASSO (Least Absolute Shrinkage and Selection Operator) method. LASSO is able to help to shrink multicollinearity and improve the accuracy of linear regression models. Estimators of LASSO parameters can be solved by the LARS (Least Angle Regression and Shrinkage) algorithm by algorithm which calculates the correlation vector, the largest absolute correlation value, equiangular vector, inner product vector, and determines the LARS algorithm limiter for LASSO. Selecting the best model using the Mallow’s C_p statistics. The smallest Mallow’s C_p value will be selected as the best model. LASSO method with a more detailed procedure with LARS algorithm and selecting the best model using the Mallow’s C_p statistics is discussed in this paper.
    Źródło:
    World Scientific News; 2019, 116; 245-252
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Supsim: a Python package and a web-based JavaScript tool to address the theoretical complexities in two-predictor suppression situations
    Autorzy:
    Nazifi, Morteza
    Fadishei, Hamid
    Powiązania:
    https://bibliotekanauki.pl/articles/2156990.pdf
    Data publikacji:
    2022-12-15
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Supsim
    multicollinearity
    suppression effects
    statistical control function
    Opis:
    Two-predictor suppression situations continue to produce uninterpretable conditions in linear regression. In an attempt to address the theoretical complexities related to suppression situations, the current study introduces two different versions of a software called suppression simulator (Supsim): a) the command-line Python package, and b) the web-based JavaScript tool, both of which are able to simulate numerous random twopredictor models (RTMs). RTMs are randomly generated, normally distributed data vectors x1, x2, and y simulated in such a way that regressing y on both x1 and x2 results in the occurrence of numerous suppression and non-suppression situations. The web-based Supsim requires no coding skills and additionally, it provides users with 3D scatterplots of the simulated RTMs. This study shows that comparing 3D scatterplots of different suppression and non-suppression situations provides important new insights into the underlying mechanisms of two-predictor suppression situations. An important focus is on the comparison of 3D scatterplots of certain enhancement situations called Hamilton's extreme example with those of redundancy situations. Such a comparison suggests that the basic mathematical concepts of two-predictor suppression situations need to be reconsidered with regard to the important issue of the statistical control function.
    Źródło:
    Statistics in Transition new series; 2022, 23, 4; 177-202
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Relationship between ridge regression estimator and sample size when multicollinearity present among regressors
    Autorzy:
    Alibuhtto, M. C.
    Powiązania:
    https://bibliotekanauki.pl/articles/1192721.pdf
    Data publikacji:
    2016
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Exponential
    Multicollinearity
    Variance Influence Factor
    Ridge Regression
    Simulation
    Opis:
    The problem of multicollinearity is the most common problem in multiple regression models as in such cases, the ordinary least squares (OLS) estimator is inaccurately estimated. Of many methods suggested to solve the problem of multicollinearity, ridge regression method is a one of popular method. In this paper, simulation data with different level of correlation coefficient were generated using Monte Carlo techniques in SAS. The level of multicollinearity was detected by correlation matrix, variance influence factor (VIF) and condition number. The biased parameter (k) of ridge regression has been computed by using iterative method for ordinary ridge regression in different sample sizes. According to the results of this study, it was found that biased parameter (k) of ridge regression and sample sizes are significantly negatively correlated at level of 5% significance. This study would helpful to develop biased parameter table for different level of sample sizes in present of multicollinearity.
    Źródło:
    World Scientific News; 2016, 59; 12-23
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient two-parameter estimator in linear regression model
    Autorzy:
    Dorugade, Ashok V.
    Powiązania:
    https://bibliotekanauki.pl/articles/1194454.pdf
    Data publikacji:
    2019-07-02
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    multicollinearity
    ridge regression
    two-parameter estimator
    mean squared error
    Opis:
    In this article, two-parameter estimators in linear model with multicollinearity are considered. An alternative efficient two-parameter estimator is proposed and its properties are examined. Furthermore, this was compared with the ordinary least squares (OLS) estimator and ordinary ridge regression (ORR) estimators. Also, using the mean squares error criterion the proposed estimator performs more efficiently than OLS estimator, ORR estimator and other reviewed two-parameter estimators. A numerical example and simulation study are finally conducted to illustrate the superiority of the proposed estimator.
    Źródło:
    Statistics in Transition new series; 2019, 20, 2; 173-185
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Drzewa klasyfikacyjne w medycynie
    Classification trees in medicine
    Autorzy:
    Owczarek, Aleksander J.
    Powiązania:
    https://bibliotekanauki.pl/articles/1035042.pdf
    Data publikacji:
    2014
    Wydawca:
    Śląski Uniwersytet Medyczny w Katowicach
    Tematy:
    drzewa klasyfikacyjne
    proces decyzyjny
    współliniowość zmiennych
    dane
    niepełne
    classification trees
    decision process
    multicollinearity
    missing data
    Opis:
    The paper presents the use of computerized diagnostic decision support systems for medical diagnostics in medicine. The structure of a classical decision tree and the advantages and disadvantages of using classification trees have been discussed. Moreover, the paper deals with the effect of classification trees with respect to other classic statistical methods, such as discriminant analysis and logistic regression, taking into account the problem of variable multicollinearity and the problem of the occurrence of so-called missing data. Additionally, some examples of the application of classification trees in medicine have been shown.
    W pracy zaprezentowano wykorzystanie w medycynie komputerowych systemów diagnostyki medycznej. Przedstawiono budowę klasycznego drzewa decyzyjnego oraz zalety i wady stosowania drzew klasyfikacyjnych. Ponadto omówiono działanie drzew klasyfikacyjnych w świetle innych klasycznych metod statystycznych, takich jak analiza dyskryminacyjna czy regresja logistyczna, z uwzględnieniem problemu współliniowości zmiennych czy problemu występowania tzw. danych niepełnych. Podano wybrane przykłady zastosowania drzew klasyfikacyjnych w medycynie.
    Źródło:
    Annales Academiae Medicae Silesiensis; 2014, 68, 6; 449-456
    1734-025X
    Pojawia się w:
    Annales Academiae Medicae Silesiensis
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An improved ridge type estimator for logistic regression
    Autorzy:
    Varathan, Nagarajah
    Powiązania:
    https://bibliotekanauki.pl/articles/2108296.pdf
    Data publikacji:
    2022-09-14
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Logistic Regression
    Multicollinearity
    ridge estimator
    Modified almost unbiased ridge logistic estimator
    Mean square error
    Opis:
    In this paper, an improved ridge type estimator is introduced to overcome the effect of multicollinearity in logistic regression. The proposed estimator is called a modified almost unbiased ridge logistic estimator. It is obtained by combining the ridge estimator and the almost unbiased ridge estimator. In order to asses the superiority of the proposed estimator over the existing estimators, theoretical comparisons based on the mean square error and the scalar mean square error criterion are presented. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimator with the existing ones. Finally, a real data example is provided to support the findings.
    Źródło:
    Statistics in Transition new series; 2022, 23, 3; 113-126
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extracting relevant predictors of the severity of mental illnesses from clinical information using regularisation regression models
    Autorzy:
    Kaushik, Sakshi
    Sabharwal, Alka
    Grover, Gurprit
    Powiązania:
    https://bibliotekanauki.pl/articles/2107145.pdf
    Data publikacji:
    2022-06-14
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    adaptive LASSO
    group LASSO
    mental disorder
    multicollinearity
    random forest imputation
    ridge regression
    severity of an illness
    Opis:
    Mental disorders are common non-communicable diseases whose occurrence rises at epidemic rates globally. The determination of the severity of a mental illness has important clinical implications and it serves as a prognostic factor for effective intervention planning and management. This paper aims to identify the relevant predictors of the severity of mental illnesses (measured by psychiatric rating scales) from a wide range of clinical variables consisting of information on both laboratory test results and psychiatric factors . The laboratory test results collectively indicate the measurements of 23 components derived from vital signs and blood tests results for the evaluation of the complete blood count. The 8 psychiatric factors known to affect the severity of mental illnesses are considered, viz. the family history, course and onset of an illness, etc. Retrospective data of 78 patients diagnosed with mental and behavioural disorders were collected from the Lady Hardinge Medical College & Smt. S.K, Hospital in New Delhi, India. The observations missing in the data are imputed using the non-parametric random forest algorithm. The multicollinearity is detected based on the variance inflation factor. Owing to the presence of multicollinearity, regularisation techniques such as ridge regression and extensions of the least absolute shrinkage and selection operator (LASSO), viz. adaptive and group LASSO are used for fitting the regression model. Optimal tuning parameter λ is obtained through 13-fold cross-validation. It was observed that the coefficients of the quantitative predictors extracted by the adaptive LASSO and the group of predictors extracted by the group LASSO were comparable to the coefficients obtained through ridge regression.
    Źródło:
    Statistics in Transition new series; 2022, 23, 2; 129-152
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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