Bahadır Yüzbaşı

STATISTICS · ECONOMETRICS · DATA SCIENCE

Making structure visible in complex data.

Reliable inference, learning and prediction for high-dimensional, spatial and functional data.

I am Professor of Statistics in the Department of Econometrics at İnönü University. I develop shrinkage, pretest and penalized estimation methods for high-dimensional, spatial and functional data, connecting them with statistical learning and elastic shape analysis.

I develop theory alongside simulation studies, real-data analyses and open-source software in R, Python and C++. My work focuses on producing reliable, interpretable insight from complex data in economics, biometry, the social sciences and digital platforms.

Selected publications TÜBİTAK 1001 project Download CV

ACADEMIC IMPACT

Research at a glance

ISI

Elected Member since 2023

1001

TÜBİTAK research project · 2026

R · Python · C++

Open-source research software

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OpenAlex citations

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RESEARCH SOFTWARE

R · Python · C++

R packages and high-performance computing with C++/RcppArmadillo; Python workflows for statistical computing, machine learning and reproducible research.

Explore software and packages →

FEATURED WORK

Books

2023

Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data

Syed Ejaz Ahmed, Feryaal Ahmed, and Bahadir Yüzbaşı

2023

DOI

Publisher page

Reviews in international journals

Selected publications

2026

Collinear Groupwise Selection via Scaled Group Lasso

Bahadır Yüzbaşı and Jiguo Cao

The American Statistician, 2026

DOI
Shrinkage estimation in geographically weighted regression with applications to digital platform pricing

Bahadır Yüzbaşı, S. Ejaz Ahmed, and Shuangzhe Liu

Spatial Statistics, 2026

DOI

2021

Penalized regression via the restricted bridge estimator

Bahadır Yüzbaşı, Mohammad Arashi, and Fikri Akdeniz

Soft Computing, Jul 2021

DOI

2020

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime

Bahadır Yüzbaşı, Mohammad Arashi, and S. Ejaz Ahmed

International Statistical Review, Apr 2020

Abs DOI

Summary In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high‐dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real‐data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.
Ridge-type pretest and shrinkage estimations in partially linear models

Bahadır Yüzbaşı, S. Ejaz Ahmed, and Dursun Aydın

Statistical Papers, Apr 2020

DOI

2016

Big data analytics: integrating penalty strategies

S. Ejaz Ahmed and Bahadır Yüzbaşı

International Journal of Management Science and Engineering Management, 2016

DOI
Shrinkage and penalized estimation in semi-parametric models with multicollinear data

Bahadır Yüzbaşı and S. Ejaz Ahmed

Journal of Statistical Computation and Simulation, Nov 2016

DOI