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1

Efficient two-parameter estimator in linear regression model

100%

Dorugade A. V.

Statistics in Transition new series

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2019

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vol. 20

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issue 2

173-185

EN

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.

2

Supsim: a Python package and a web-based JavaScript tool to address the theoretical complexities in two-predictor suppression situations

100%

Nazifi M., Fadishei H.

Statistics in Transition new series

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2022

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vol. 23

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issue 4

177-202

EN

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.

3

Extracting relevant predictors of the severity of mental illnesses from clinical information using regularisation regression models

75%

Kaushik S., Sabharwal A., Grover G.

Statistics in Transition new series

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2022

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vol. 23

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issue 2

129-152

EN

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.

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3 Statistics in Transition new series

1 Dorugade A. V.

1 Fadishei H.

1 Grover G.

1 Kaushik S.

1 Nazifi M.

1 Sabharwal A.

2 2022

1 2019

Efficient two-parameter estimator in linear regression model

Supsim: a Python package and a web-based JavaScript tool to address the theoretical complexities in two-predictor suppression situations

Extracting relevant predictors of the severity of mental illnesses from clinical information using regularisation regression models