Work

Graduate School of Westminster International University in Tashkent Professorship of Statistics and Econometrics

Year: 2024

Type: conference-paper

Abstract: Linear regression is one of the widely used statistical methods in social sciences. The core part of the regressions are coefficients which bring some inference. Yet, we rely on hypothesis testing or confidence intervals and certain assumptions underlying linear models such as sample size being large enough. In this study, we suggest alternative way of constructing confidence intervals using bootstrap which is expected to work well even when the sample size is smaller than required per OLS assumptions. We find that even in small samples, bootstrap confidence intervals can perform better than traditional interval estimations

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13822769

Authors: Rakhimov, Zarrukh, Rahimova, Nilufar

Institution: "Silk Road" International University of Tourism and Cultural Heritage

Language: English

Topic: Advanced Statistical Methods and Models

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6930610320

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