Big data analytics: integrating penalty strategies

Ahmed, S. Ejaz; Yüzbaşı, Bahadır

doi:10.1080/17509653.2016.1153252

Cited by 15 publications

(5 citation statements)

References 21 publications

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“…In summary, the results of the data analyses strongly confirm the findings of the simulation study and suggest the use of the shrinkage ridge estimation strategy when no prior information about the parameter subspace is available. The results of our simulation study and real data application are consistent with available results in [27][28][29].…”

Section: Discussionsupporting

confidence: 88%

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Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

Opoku

Ahmed

Nathoo

2021

Entropy

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In a host of business applications, biomedical and epidemiological studies, the problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis for linear mixed models (LMM). We consider an efficient estimation strategy for high-dimensional data application, where the dimensions of the parameters are larger than the number of observations. In this paper, we are interested in estimating the fixed effects parameters of the LMM when it is assumed that some prior information is available in the form of linear restrictions on the parameters. We propose the pretest and shrinkage estimation strategies using the ridge full model as the base estimator. We establish the asymptotic distributional bias and risks of the suggested estimators and investigate their relative performance with respect to the ridge full model estimator. Furthermore, we compare the numerical performance of the LASSO-type estimators with the pretest and shrinkage ridge estimators. The methodology is investigated using simulation studies and then demonstrated on an application exploring how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease.

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Section: Discussionsupporting

confidence: 88%

“…The goal is to improve the estimation accuracy of the non-sparse set of the fixed effects parameters by combining an over-fitted model estimator with an under-fitted one [27,29]. This approach will include combining two sub-models produced by two different variable selection techniques from the LMM [28]. .…”

Section: Discussionmentioning

confidence: 99%

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

Opoku

Ahmed

Nathoo

2021

Entropy

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“…Moreover, to mitigate overfitting associated with LASSO regression, we have introduced an SVM-RFE model that employs intersection taking to identify key genes. To further prevent overfitting, methods such as Smoothed Truncated Absolute Deviation (SCAD) and Adaptive Lasso (aLasso) could also be considered (Ejaz Ahmed, 2016 ; Ahmed et al, 2023 ). In addition, the GSE83456 control group is a healthy control.…”

Section: Discussionmentioning

confidence: 99%

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

Wang,

Jin,

Mao

et al. 2024

Front. Microbiol.

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The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717–0.805) and 0.833 (95% CI: 0.793–0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

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“…In our study, we consider preliminarily test and shrinkage estimation, more information on which can be found in Ahmed (2014), in ridge-type SUR models when the explanatory variables are affected by multicollinearity. In a previous paper, we combined penalized estimations in an optimal way to define shrinkage estimation (Ahmed and Yüzbaşı 2016). Gao et al (2017) suggested the use of the weighted ridge regression model for post-selection shrinkage estimation.…”

Section: Introductionmentioning

confidence: 99%

Ridge Type Shrinkage Estimation of Seemingly Unrelated Regressions And Analytics of Economic and Financial Data from “Fragile Five” Countries

Yüzbaşı

Ahmed

2020

JRFM

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In this paper, we suggest improved estimation strategies based on preliminarily test and shrinkage principles in a seemingly unrelated regression model when explanatory variables are affected by multicollinearity. To that end, we split the vector regression coefficient of each equation into two parts: one includes the coefficient vector for the main effects, and the other is a vector for nuisance effects, which could be close to zero. Therefore, two competing models per equation of the system regression model are obtained: one includes all the regression of coefficients (full model); the other (sub model) includes only the coefficients of the main effects based on the auxiliary information. The preliminarily test estimation improves the estimation procedure if there is evidence that the vector of nuisance parameters does not provide a useful contribution to the model. The shrinkage estimation method shrinks the full model estimator in the direction of the sub-model estimator. We conduct a Monte Carlo simulation study in order to examine the relative performance of the suggested estimation strategies. More importantly, we apply our methodology based on the preliminarily test and the shrinkage estimations to analyse economic data by investigating the relationship between foreign direct investment and several economic variables in the “Fragile Five” countries between 1983 and 2018.

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Big data analytics: integrating penalty strategies

Cited by 15 publications

References 21 publications

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

Ridge Type Shrinkage Estimation of Seemingly Unrelated Regressions And Analytics of Economic and Financial Data from “Fragile Five” Countries

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